The Current State of Machine Intelligence 3.0

By Hugo Angel,

ORIGINAL: O’Reilly 

(originally published by O’Reilly here, this year in collaboration with my amazing partner James Cham! If you’re interested in enterprise implications of this chart please refer to Harvard Business Review’s The Competitive Landscape for Machine Intelligence)
Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year’s landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there.
As has been the case for the last couple of years, our fund still obsesses over “problem first” machine intelligence—we’ve invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. (Our fund focuses on the future of work, so there are some machine intelligence domains where we invest more than others.)
At the same time, the hype around machine intelligence methods continues to grow: the words “deep learning” now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like “big data” (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one. We favor those who apply technology thoughtfully.
What’s the biggest change in the last year? We are getting inbound inquiries from a different mix of people. For v1.0, we heard almost exclusively from founders and academics. Then came a healthy mix of investors, both private and public. Now overwhelmingly we have heard from existing companies trying to figure out how to transform their businesses using machine intelligence.
For the first time, a “one stop shop” of the machine intelligence stack is coming into view—even if it’s a year or two off from being neatly formalized. The maturing of that stack might explain why more established companies are more focused on building legitimate machine intelligence capabilities. Anyone who has their wits about them is still going to be making initial build-and-buy decisions, so we figured an early attempt at laying out these technologies is better than no attempt.
Ready player world
Many of the most impressive looking feats we’ve seen have been in the gaming world, from DeepMind beating Atari classics and the world’s best at Go, to the OpenAI gym, which allows anyone to train intelligent agents across an array of gaming environments.
The gaming world offers a perfect place to start machine intelligence work (e.g., constrained environments, explicit rewards, easy-to-compare results, looks impressive)—especially for reinforcement learning. And it is much easier to have a self-driving car agent go a trillion miles in a simulated environment than on actual roads. Now we’re seeing the techniques used to conquer the gaming world moving to the real world. A newsworthy example of game-tested technology entering the real world was when DeepMind used neural networks to make Google’s data centers more efficient. This begs questions: What else in the world looks like a game? Or what else in the world can we reconfigure to make it look more like a game?
Early attempts are intriguing. Developers are dodging meter maids (brilliant—a modern day Paper Boy), categorizing cucumbers, sorting trash, and recreating the memories of loved ones as conversational bots. Otto’s self-driving trucks delivering beer on their first commercial ride even seems like a bonus level from Grand Theft Auto. We’re excited to see what new creative applications come in the next year.
Why even bot-her?
Ah, the great chatbot explosion of 2016, for better or worse—we liken it to the mobile app explosion we saw with the launch of iOS and Android. The dominant platforms (in the machine intelligence case, Facebook, Slack, Kik) race to get developers to build on their platforms. That means we’ll get some excellent bots but also many terrible ones—the joys of public experimentation.
The danger here, unlike the mobile app explosion (where we lacked expectations for what these widgets could actually do), is that we assume anything with a conversation interface will converse with us at near-human level. Most do not. This is going to lead to disillusionment over the course of the next year but it will clean itself up fairly quickly thereafter.
When our fund looks at this emerging field, we divide each technology into two components: the conversational interface itself and the “agent” behind the scenes that’s learning from data and transacting on a user’s behalf. While you certainly can’t drop the ball on the interface, we spend almost all our time thinking about that behind-the-scenes agent and whether it is actually solving a meaningful problem.
We get a lot of questions about whether there will be “one bot to rule them all.” To be honest, as with many areas at our fund, we disagree on this. We certainly believe there will not be one agent to rule them all, even if there is one interface to rule them all. For the time being, bots will be idiot savants: stellar for very specific applications.
We’ve written a bit about this, and the framework we use to think about how agents will evolve is a CEO and her support staff. Many Fortune 500 CEOs employ a scheduler, handler, a research team, a copy editor, a speechwriter, a personal shopper, a driver, and a professional coach. Each of these people performs a dramatically different function and has access to very different data to do their job. The bot / agent ecosystem will have a similar separation of responsibilities with very clear winners, and they will divide fairly cleanly along these lines. (Note that some CEO’s have a chief of staff who coordinates among all these functions, so perhaps we will see examples of “one interface to rule them all.”)
You can also see, in our landscape, some of the corporate functions machine intelligence will re-invent (most often in interfaces other than conversational bots).
On to 11111000001
Successful use of machine intelligence at a large organization is surprisingly binary, like flipping a stubborn light switch. It’s hard to do, but once machine intelligence is enabled, an organization sees everything through the lens of its potential. Organizations like Google, Facebook, Apple, Microsoft, Amazon, Uber, and Bloomberg (our sole investor) bet heavily on machine intelligence and have its capabilities pervasive throughout all of their products.
Other companies are struggling to figure out what to do, as many boardrooms did on “what to do about the Internet” in 1997. Why is this so difficult for companies to wrap their heads around? Machine intelligence is different from traditional software. Unlike with big data, where you could buy a new capability, machine intelligence depends on deeper organizational and process changes. Companies need to decide whether they will trust machine intelligence analysis for one-off decisions or if they will embed often-inscrutable machine intelligence models in core processes. Teams need to figure out how to test newfound capabilities, and applications need to change so they offer more than a system of record; they also need to coach employees and learn from the data they enter.
Unlike traditional hard-coded software, machine intelligence gives only probabilistic outputs. We want to ask machine intelligence to make subjective decisions based on imperfect information (eerily like what we trust our colleagues to do?). As a result, this new machine intelligence software will make mistakes, just like we do, and we’ll need to be thoughtful about when to trust it and when not to.
The idea of this new machine trust is daunting and makes machine intelligence harder to adopt than traditional software. We’ve had a few people tell us that the biggest predictor of whether a company will successfully adopt machine intelligence is whether they have a C-Suite executive with an advanced math degree. These executives understand it isn’t magic—it is just (hard) math.
Machine intelligence business models are going to be different from licensed and subscription software, but we don’t know how. Unlike traditional software, we still lack frameworks for management to decide where to deploy machine intelligence. Economists like Ajay Agrawal, Joshua Gans, and Avi Goldfarb have taken the first steps toward helping managers understand the economics of machine intelligence and predict where it will be most effective. But there is still a lot of work to be done.
In the next few years, the danger here isn’t what we see in dystopian sci-fi movies. The real danger of machine intelligence is that executives will make bad decisions about what machine intelligence capabilities to build.
Peter Pan’s never-never land
We’ve been wondering about the path to grow into a large machine intelligence company. Unsurprisingly, there have been many machine intelligence acquisitions (Nervana by Intel, Magic Pony by Twitter, Turi by Apple, Metamind by Salesforce, Otto by Uber, Cruise by GM, SalesPredict by Ebay, Viv by Samsung). Many of these happened fairly early in a company’s life and at quite a high price. Why is that?
Established companies struggle to understand machine intelligence technology, so it’s painful to sell to them, and the market for buyers who can use this technology in a self-service way is small. Then, if you do understand how this technology can supercharge your organization, you realize it’s so valuable that you want to hoard it. Businesses are saying to machine intelligence companies, “forget you selling this technology to others, I’m going to buy the whole thing.”
This absence of a market today makes it difficult for a machine intelligence startup, especially horizontal technology providers, to “grow up”—hence the Peter Pans. Companies we see successfully entering a long-term trajectory can package their technology as a new problem-specific application for enterprise or simply transform an industry themselves as a new entrant (love this). We flagged a few of the industry categories where we believe startups might “go the distance” in this year’s landscape.
Inspirational machine intelligence
Once we do figure it out, machine intelligence can solve much more interesting problems than traditional software. We’re thrilled to see so many smart people applying machine intelligence for good.
Established players like Conservation Metrics and Vulcan Conservation have been using deep learning to protect endangered animal species; the ever-inspiring team at Thorn is constantly coming up with creative algorithmic techniques to protect our children from online exploitation. The philanthropic arms of the tech titans joined in, enabling nonprofits with free storage, compute, and even developer time. Google partnered with nonprofits to found Global Fishing Watch to detect illegal fishing activity using satellite data in near real time, satellite intelligence startup Orbital Insight (in which we are investors) partnered with Global Forest Watch to detect illegal logging and other causes of global forest degradation. Startups are getting into the action, too. The Creative Destruction Lab machine intelligence accelerator (with whom we work closely) has companies working on problems like earlier diseasedetection and injury prevention. One area where we have seen some activity but would love to see more is machine intelligence to assist the elderly.
In talking to many people using machine intelligence for good, they all cite the critical role of open source technologies. In the last year, we’ve seen the launch of OpenAI, which offers everyone access to world class research and environments, and better and better releases of TensorFlow and Keras. Non-profits are always trying to do more with less, and machine intelligence has allowed them to extend the scope of their missions without extending budget. Algorithms allow non-profits to inexpensively scale what would not be affordable to do with people.
We also saw growth in universities and corporate think tanks, where new centers like USC’s Center for AI in Society, Berkeley’s Center for Human Compatible AI, and the multiple-corporation Partnership on AI study the ways in which machine intelligence can help humanity. The White House even got into the act: after a series of workshops around the U.S., they published a 48-page report outlining their recommendations for applying machine intelligence to safely and fairly address broad social problems.
On a lighter note, we’ve also heard whispers of more artisanal versions of machine intelligence. Folks are doing things like using computer vision algorithms to help them choose the best cocoa beans for high-grade chocolate, write poetry, cook steaks, and generate musicals.
Curious minds want to know. If you’re working on a unique or important application of machine intelligence we’d love to hear from you.
Looking forward
We see all this activity only continuing to accelerate. The world will give us more open sourced and commercially available machine intelligence building blocks, there will be more data, there will be more people interested in learning these methods, and there will always be problems worth solving. We still need ways of explaining the difference between machine intelligence and traditional software, and we’re working on that. The value of code is different from data, but what about the value of the model that code improves based on that data?
Once we understand machine intelligence deeply, we might look back on the era of traditional software and think it was just a prologue to what’s happening now. We look forward to seeing what the next year brings.
A massive thank you to the Bloomberg Beta team, David Klein, Adam Gibson, Ajay Agrawal, Alexandra Suich, Angela Tranyens, Anthony Goldblum, Avi Goldfarb, Beau Cronin, Ben Lorica, Chris Nicholson, Doug Fulop, Dror Berman, Dylan Tweney, Gary Kazantsev, Gideon Mann, Gordon Ritter, Jack Clark, John Lilly, Jon Lehr, Joshua Gans, Lauren Barless, Matt Turck, Matthew Granade, Mickey Graham, Nick Adams, Roger Magoulas, Sean Gourley, Shruti Gandhi, Steve Jurvetson, Vijay Sundaram, Zavain Dar, and for the help and fascinating conversations that led to this year’s report!
Landscape designed by Heidi Skinner.
Disclosure: Bloomberg Beta is an investor in Alation, Arimo, Aviso, Brightfunnel, Context Relevant, Deep Genomics, Diffbot, Digital Genius, Domino Data Labs, Drawbridge, Gigster, Gradescope, Graphistry, Gridspace, Howdy, Kaggle, Kindred.ai, Mavrx, Motiva, PopUpArchive, Primer, Sapho, Shield.AI, Textio, and Tule.
 
The Current State of Machine Intelligence 2.0
A year ago, I published my original attempt at mapping the machine intelligence ecosystem. So much has happened since. I spent the last 12 months geeking out on every company and nibble of information I can find, chatting with hundreds of academics, entrepreneurs, and investors about machine intelligence. This year, given the explosion of activity, my focus is on highlighting areas of innovation, rather than on trying to be comprehensive. Figure 1 showcases the new landscape of machine intelligence as we enter 2016:
Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.
The two biggest changes I’ve noted since I did this analysis last year are (1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.
Reflections on the landscape
With the focus moving from “machine intelligence as magic box” to delivering real value immediately, there are more ways to bring a machine intelligence startup to market. (There are as many ways to go to market as there are business problems to solve. I lay out many of the optionshere.)Most of these machine intelligence startups take well-worn machine intelligence techniques, some more than a decade old, and apply them to new data sets and workflows. It’s still true that big companies, with their massive data sets and contact with their customers, have inherent advantages — though startups are finding a way to enter.
Achieving autonomy
In last year’s roundup, the focus was almost exclusively on machine intelligence in the virtual world. This time we’re seeing it in the physical world, in the many flavors of autonomous systems: self-driving cars, autopilot drones, robots that can perform dynamic tasks without every action being hard coded. It’s still very early days — most of these systems are just barely useful, though we expect that to change quickly.
These physical systems are emerging because they meld many now-maturing research avenues in machine intelligence. Computer vision, the combination of deep learning and reinforcement learning, natural language interfaces, and question-answering systems are all building blocks to make a physical system autonomous and interactive. Building these autonomous systems today is as much about integrating these methods as inventing new ones.
The new (in)human touch
The virtual world is becoming more autonomous, too. Virtual agents, sometimes called bots, use conversational interfaces (think of Her, without the charm). Some of these virtual agents are entirely automated, others are a “human-in-the-loop” system, where algorithms take “machine-like” subtasks and a human adds creativity or execution. (In some, the human is training the bot while she or he works.) The user interacts with the system by either typing in natural language or speaking, and the agent responds in kind.
These services sometimes give customers confusing experiences, like mine the other day when I needed to contact customer service about my cell phone. I didn’t want to talk to anyone, so I opted for online chat. It was the most “human” customer service experience of my life, so weirdly perfect I found myself wondering whether I was chatting with a person, a bot, or some hybrid. Then I wondered if it even mattered. I had a fantastic experience and my issue was resolved. I felt gratitude to whatever it was on the other end, even if it was a bot.
On one hand, these agents can act utterly professional, helping us with customer support, research, project management, scheduling, and e-commerce transactions. On the other hand, they can be quite personal and maybe we are getting closer to Her — with Microsoft’s romantic chatbotXiaoice, automated emotional support is already here.
As these technologies warm up, they could transform new areas like education, psychiatry, and elder care, working alongside human beings to close the gap in care for students, patients, and the elderly.
50 shades of grey markets
At least I make myself laugh. 😉
Many machine intelligence technologies will transform the business world by starting in regulatory grey areas. On the short list: health care (automated diagnostics, early disease detection based on genomics, algorithmic drug discovery); agriculture (sensor- and vision-based intelligence systems, autonomous farming vehicles); transportation and logistics (self-driving cars, drone systems, sensor-based fleet management); and financial services (advanced credit decisioning).
To overcome the difficulties of entering grey markets, we’re seeing some unusual strategies:
Startups are making a global arbitrage (e.g., health care companies going to market in emerging markets, drone companies experimenting in the least regulated countries).
The “fly under the radar” strategy. Some startups are being very careful to stay on the safest side of the grey area, keep a low profile, and avoid the regulatory discussion as long as possible.
Big companies like Google, Apple, and IBM are seeking out these opportunities because they have the resources to be patient and are the most likely to be able to effect regulatory change — their ability to affect regulation is one of their advantages.
Startups are considering beefing up funding earlier than they would have, to fight inevitable legal battles and face regulatory hurdles sooner.
What’s your (business) problem?
A year ago, enterprises were struggling to make heads or tails of machine intelligence services (some of the most confusing were in the “platform” section of this landscape). When I spoke to potential enterprise customers, I often heard things like, “these companies are trying to sell me snake oil” or, “they can’t even explain to me what they do.”
The corporates wanted to know what current business problems these technologies could solve. They didn’t care about the technology itself. The machine intelligence companies, on the other hand, just wanted to talk about their algorithms and how their platform could solve hundreds of problems (this was often true, but that’s not the point!).
Two things have happened that are helping to create a more productive middle ground:
Enterprises have invested heavily in becoming “machine intelligence literate.” I’ve had roughly 100 companies reach out to get perspective on how they should think about machine intelligence. Their questions have been thoughtful, they’ve been changing their organizations to make use of these new technologies, and many different roles across the organization care about this topic (from CEOs to technical leads to product managers).
Many machine intelligence companies have figured out that they need to speak the language of solving a business problem. They are packaging solutions to specific business problems as separate products and branding them that way. They often work alongside a company to create a unique solution instead of just selling the technology itself, being one part educator and one part executor. Once businesses learn what new questions can be answered with machine intelligence, these startups may make a more traditional technology sale.
The great verticalization
I remember reading Who Says Elephants Can’t Dance and being blown away by the ability of a technology icon like IBM to risk it all. (This was one of the reasons I went to work for them out of college.) Now IBM seems poised to try another risk-it-all transformation — moving from a horizontal technology provider to directly transforming a vertical. And why shouldn’t Watson try to be a doctor or a concierge? It’s a brave attempt.
It’s not just IBM: you could probably make an entire machine intelligence landscape just of Google projects. (If anyone takes a stab, I’d love to see it!)
Your money is nice, but tell me more about your data
In the machine intelligence world, founders are selling their companies, as I suggested last year — but it’s about more than just money. I’ve heard from founders that they are only interested in an acquisition if the acquirer has the right data set to make their product work. We’re hearing things like, “I’m not taking conversations but, given our product, if X came calling it’d be hard to turn down.” “X” is most often Slack (!), Google, Facebook, Twitter in these conversations — the companies that have the data.
(Eh)-I
Until recently, there’s been one secret in machine intelligence talent:Canada!During the “AI winter,” when this technology fell out of favor in the 80s and 90s, the Canadian government was one of a few entities funding AI research. This support sustained the formidable trio of Geoffrey Hinton,Yoshua Bengio, and Yann LeCun, the godfathers of deep learning.
Canada continues to be central to the machine intelligence frontier. As an unapologetically proud Canadian, it’s been a pleasure to work with groups like AICML to commercialize advanced research, the Machine Learning Creative Destruction Lab to support startups, and to bring the machine intelligence world together at events like this one.
So what now?
Machine intelligence is even more of a story than last year, in large companies as well as startups. In the next year, the practical side of these technologies will flourish. Most new entrants will avoid generic technology solutions, and instead have a specific business purpose to which to put machine intelligence.
I can’t wait to see more combinations of the practical and eccentric. A few years ago, a company like Orbital Insight would have seemed farfetched — wait, you’re going to use satellites and computer vision algorithms to tell me what the construction growth rate is in China!? — and now it feels familiar.
Similarly, researchers are doing things that make us stop and say, “Wait, really?” They are tackling important problems we may not have imagined were possible, like creating fairy godmother drones to help the elderly, computer vision that detects the subtle signs of PTSD, autonomous surgical robots that remove cancerous lesions, and fixing airplane WiFi (just kidding, not even machine intelligence can do that).
Overall, agents will become more eloquent, autonomous systems more pervasive, machine intelligence more…intelligent. I expect more magic in the years to come.
Many thanks to those who helped me with this! Special thanks to Adam Spector, Ajay Agrawal, Angela Tran Kingyens, Beau Cronin, Chris Michel, Chris Nicholson, Dan Strickland, David Beyer, David Klein, Doug Fulop, Dror Berman, Jack Clark, James Cham, James Rattner, Jeffrey Chung, Jon Lehr, Karin Klein, Lauren Barless, Lynda Ting, Matt Turck, Mike Dauber, Morgan Polotan, Nick Adams, Pete Skomoroch, Roy Bahat, Sean Gourley, Shruti Gandhi, Zavain Dar, and Heidi Skinner (who designed this graphic).
 
Disclosure: Bloomberg Beta is an investor in Alation, Adatao, Aviso, BrightFunnel, Context Relevant, Deep Genomics, Diffbot, Domino Data Lab, Gigster, Graphistry, Howdy, Kaggle, Mavrx, Orbital Insight, Primer, Sapho, Textio, and Tule.
Machine Intelligence in the Real World
(this pieces was originally posted on Tech Crunch) .
I’ve been laser-focused on machine intelligence in the past few years. I’ve talked to hundreds of entrepreneurs, researchers and investors about helping machines make us smarter.
In the months since I shared my landscape of machine intelligence companies, folks keep asking me what I think of them — as if they’re all doing more or less the same thing. (I’m guessing this is how people talked about “dot coms” in 1997.)
On average, people seem most concerned about how to interact with these technologies once they are out in the wild. This post will focus on how these companies go to market, not on the methods they use.
In an attempt to explain the differences between how these companies go to market, I found myself using (admittedly colorful) nicknames. It ended up being useful, so I took a moment to spell them out in more detail so, in case you run into one or need a handy way to describe yours, you have the vernacular.
The categories aren’t airtight — this is a complex space — but this framework helps our fund (which invests in companies that make work better) be more thoughtful about how we think about and interact with machine intelligence companies.
“Panopticons” Collect A Broad Dataset
Machine intelligence starts with the data computers analyze, so the companies I call “panopticons” are assembling enormous, important new datasets. Defensible businesses tend to be global in nature. “Global” is very literal in the case of a company like Planet Labs, which has satellites physically orbiting the earth. Or it’s more metaphorical, in the case of a company like Premise, which is crowdsourcing data from many countries.
With many of these new datasets we can automatically get answers to questions we have struggled to answer before. There are massive barriers to entry because it’s difficult to amass a global dataset of significance.
However, it’s important to ask whether there is a “good enough” dataset that might provide a cheaper alternative, since data license businesses are at risk of being commoditized. Companies approaching this space should feel confident that either (1) no one else can or will collect a “good enough” alternative, or (2) they can successfully capture the intelligence layer on top of their own dataset and own the end user.
Examples include Planet Labs, Premise and Diffbot.
“Lasers” Collect A Focused Dataset
The companies I like to call “lasers” are also building new datasets, but in niches, to solve industry-specific problems with laser-like focus. Successful companies in this space provide more than just the dataset — they also must own the algorithms and user interface. They focus on narrower initial uses and must provide more value than just data to win customers.
The products immediately help users answer specific questions like, “how much should I water my crops?” or “which applicants are eligible for loans?” This category may spawn many, many companies — a hundred or more — because companies in it can produce business value right away.
With these technologies, many industries will be able to make decisions in a data-driven way for the first time. The power for good here is enormous: We’ve seen these technologies help us feed the world more efficiently, improve medical diagnostics, aid in conservation projects and provide credit to those in the world that didn’t have access to it before.
But to succeed, these companies need to find a single “killer” (meant in the benevolent way) use case to solve, and solve that problem in a way that makes the user’s life simpler, not more complex.
Examples include Tule Technologies, Enlitic, InVenture, Conservation Metrics, Red Bird, Mavrx and Watson Health.
“Alchemists” Promise To Turn Your Data Into Gold
These companies have a simple pitch: Let me work with your data, and I will return gold. Rather than creating their own datasets, they use novel algorithms to enrich and draw insights from their customers’ data. They come in three forms:
Self-service API-based solutions.
Service providers who work on top of their customers’ existing stacks.
Full-stack solutions that deliver their own hardware-optimized stacks.
Because the alchemists see across an array of data types, they’re likely to get early insight into powerful applications of machine intelligence. If they go directly to customers to solve problems in a hands-on way (i.e., with consulting services), they often become trusted partners.
But be careful. This industry is nascent, and those using an API-based approach may struggle to scale as revenue sources can only go as far as the still-small user base. Many of the self-service companies have moved toward a more hands-on model to address this problem (and those people-heavy consulting services can sometimes be harder to scale).
Examples include Nervana Systems, Context Relevant, IBM Watson, Metamind, AlchemyAPI (acquired by IBM Watson), Skymind, Lucid.ai and Citrine.
“Gateways” Create New Use Cases From Specific Data Types
These companies allow enterprises to unlock insights from a type of data they had trouble dealing with before (e.g., image, audio, video, genomic data). They don’t collect their own data, but rather work with client data and/or a third-party data provider. Unlike the Alchemists, who tend to do analysis across an array of data types and use cases, these are specialists.
What’s most exciting here is that this is genuinely new intelligence. Enterprises have generally had this data, but they either weren’t storing it or didn’t have the ability to interpret it economically. All of that “lost” data can now be used.
Still, beware the “so what” problem. Just because we have the methods to extract new insights doesn’t make them valuable. We’ve seen companies that begin with the problem they want to solve, and others blinded by the magic of the method. The latter category struggles to get funding.
Examples include Clarifai, Gridspace, Orbital Insight, Descartes Labs, Deep Genomics and Atomwise.
“Magic Wands” Seamlessly Fix A Workflow
These are SaaS tools that make work more effective, not just by extracting insights from the data you provide but by seamlessly integrating those insights into your daily workflow, creating a level of machine intelligence assistance that feels like “magic.” They are similar to the Lasers in that they have an interface that helps the user solve a specific problem — but they tend to rely on a user’s or enterprise’s data rather than creating their own new dataset from scratch.
For example, Textio is a text editor that recommends improvements to job descriptions as you type. With it, I can go from a 40th percentile job description to a 90th percentile one in just a few minutes, all thanks to a beautifully presented machine learning algorithm.
I believe that in five years we all will be using these tools across different use cases. They make the user look like an instant expert by codifying lessons found in domain-specific data. They can aggregate intelligence and silently bake it into products. We expect this space to heat up, and can’t wait to see more Magic Wands.
The risk is that by relying on such tools, humans will lose expertise (in the same way that the autopilot created the risk that pilots’ core skills may decay). To offset this, makers of these products should create UI in a way that will actually fortify the user’s knowledge rather than replace it (e.g., educating the user during the process of making a recommendation or using a double-blind interface).
Examples include Textio, RelateIQ (acquired by Salesforce), InboxVudu, Sigopt and The Grid
“Navigators” Create Autonomous Systems For The Physical World
Machine intelligence plays a huge role in enabling autonomous systems like self-driving cars, drones and robots to augment processes in warehouses, agriculture and elderly care. This category is a mix of early stage companies and large established companies like Google, Apple, Uber and Amazon.
Such technologies give us the ability to rethink transportation and logistics entirely, especially in emerging market countries that lack robust physical infrastructure. We also can use them to complete tasks that were historically very dangerous for humans.
Before committing to this kind of technology, companies should feel confident that they can raise large amounts of capital and recruit the best minds in some of the most sought-after fields. Many of these problems require experts across varied specialties, like hardware, robotics, vision and audio. They also will have to deal with steep regulatory hurdles (e.g., self-driving car regulations).
Examples include Blue River Technologies, Airware, Clearpath Robotics, Kiva Systems (acquired by Amazon), 3DR, Skycatch, Cruise Automation and the self-driving car groups at Google, Uber, Apple and Tesla.
“Agents” Create Cyborgs And Bots To Help With Virtual Tasks
Sometimes the best way to use machine intelligence is to pair it with human intelligence. Cyborgs and bots are similar in that they help you complete tasks, but the difference is a cyborg appears as if it’s a human (it blends human and machine intelligence behind the scenes, has a proper name and attempts to interact like a person would), whereas a bot is explicitly non-human and relies on you to provide the human-level guidance to instruct it what to do.
Cyborgs most often complete complex tasks, like customer service via real-time chat or meeting scheduling via email (e.g., Clara from Clara Labs or Amy from x.ai). Bots tend to help you perform basic research, complete online transactions and help your team stay on top of tasks (e.g., Howdy, the project management bot).
In both cases, this is the perfect blending of humans and machines: The computers take the transactional grunt work pieces of the task and interact with us for the higher-level decision-making and creativity.
Cyborg-based companies start as mostly manual services and, over time, become more machine-driven as technology matures. The risk is whether they can make that transition quickly enough. For both cyborgs and bots, privacy and security will be an ongoing concern, as we trust more and more of our data (e.g., calendars, email, documents, credit cards) to them.
Examples include Clara, x.ai, Facebook M, Digital Genius, Kasisto and Howdy.
“Pioneers” Are Very Smart
Some machine intelligence companies begin life as academic projects. When the teams — professors and graduate students with years of experience in the field — discover they have something marketable, they (or their universities) spin them out into companies.
Aggregating a team like that is, in itself, a viable market strategy, because there are so few people with 8-10 years of experience in this field. Their brains are so valuable that investors are willing to take the risk on the basis of the team alone — even if the business models still need some work.
In fact, there are many extremely important problems to solve that don’t line up with short-term use cases. These teams are the ones solving the problems that seem impossible, and they are among the few who can potentially make them possible!
This approach can work brilliantly if the team has a problem they are truly devoted to working on, but it is tough to keep the team together if they are banding together for the sake of solidarity and the prospect of an acqui-hire. They also need funders who are aligned with their longer-term vision.
Examples include DeepMind (acquired by Google), DNN Research (acquired by Google), Numenta, Vicarious, NNaiSense and Curious AI.
As you can see, it’s clear that machine intelligence is a very active space. There are many companies out there that may not fit into one of these categories, but these are the ones we see most often.
The obvious question for all of these categories is which are most attractive for investment? Individual startups are outliers by definition, so it’s hard to make it black and white, and we’re so excited about this space that it’s really just different degrees of optimism. That said, I’m particularly excited about the Lasers and Magic Wands, because they can turn new types of data into actionable intelligence right now, and because they can take advantage of well-worn SaaS techniques.
More on these to come. Stay tuned.
Disclosure: Bloomberg Beta is an investor in Diffbot, Tule Technologies, Mavrx, Gridspace, Orbital Insight, Textio, Howdy and several other machine intelligence companies that are not mentioned in this article.
The Current State of Machine Intelligence
I spent the last three months learning about every artificial intelligence, machine learning, or data related startup I could find — my current list has 2,529 of them to be exact. Yes, I should find better things to do with my evenings and weekends but until then…
Why do this?
A few years ago, investors and startups were chasing “big data” (I helped put together a landscape on that industry). Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or somesuch — collectively I call these “machine intelligence” (I’ll get into the definitions in a second). Our fund, Bloomberg Beta, which is focused on the future of work, has been investing in these approaches. I created this landscape to start to put startups into context. I’m a thesis-oriented investor and it’s much easier to identify crowded areas and see white space once the landscape has some sort of taxonomy.
What is “machine intelligence,” anyway?
I mean “machine intelligence” as a unifying term for what others call machine learning and artificial intelligence. (Some others have used the term before, without quite describing it or understanding how laden this field has been with debates over descriptions.)
I would have preferred to avoid a different label but when I tried either “artificial intelligence” or “machine learning” both proved to too narrow: when I called it “artificial intelligence” too many people were distracted by whether certain companies were “true AI,” and when I called it “machine learning,” many thought I wasn’t doing justice to the more “AI-esque” like the various flavors of deep learning. People have immediately grasped “machine intelligence” so here we are. ☺
Computers are learning to think, read, and write. They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus). Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.
What this landscape doesn’t include, however important, is “big data” technologies. Some have used this term interchangeably with machine learning and artificial intelligence, but I want to focus on the intelligence methods rather than data, storage, and computation pieces of the puzzle for this landscape (though of course data technologies enable machine intelligence).
Which companies are on the landscape?
I considered thousands of companies, so while the chart is crowded it’s still a small subset of the overall ecosystem. “Admissions rates” to the chart were fairly in line with those of Yale or Harvard, and perhaps equally arbitrary. ☺
I tried to pick companies that used machine intelligence methods as a defining part of their technology. Many of these companies clearly belong in multiple areas but for the sake of simplicity I tried to keep companies in their primary area and categorized them by the language they use to describe themselves (instead of quibbling over whether a company used “NLP” accurately in its self-description).
If you want to get a sense for innovations at the heart of machine intelligence, focus on the core technologies layer. Some of these companies have APIs that power other applications, some sell their platforms directly into enterprise, some are at the stage of cryptic demos, and some are so stealthy that all we have is a few sentences to describe them.
The most exciting part for me was seeing how much is happening the the application space. These companies separated nicely into those that reinvent the enterprise, industries, and ourselves.
If I were looking to build a company right now, I’d use this landscape to help figure out what core and supporting technologies I could package into a novel industry application. Everyone likes solving the sexy problems but there are an incredible amount of ‘unsexy’ industry use cases that have massive market opportunities and powerful enabling technologies that are begging to be used for creative applications (e.g., Watson Developer Cloud, AlchemyAPI).
Reflections on the landscape:
We’ve seen a few great articles recently outlining why machine intelligence is experiencing a resurgence, documenting the enabling factors of this resurgence. (Kevin Kelly, for example chalks it up to cheap parallel computing, large datasets, and better algorithms.) I focused on understanding the ecosystem on a company-by-company level and drawing implications from that.
Yes, it’s true, machine intelligence is transforming the enterprise, industries and humans alike.
On a high level it’s easy to understand why machine intelligence is important, but it wasn’t until I laid out what many of these companies are actually doing that I started to grok how much it is already transforming everything around us. As Kevin Kelly more provocatively put it, “the business plans of the next 10,000 startups are easy to forecast: Take X and add AI”. In many cases you don’t even need the X — machine intelligence will certainly transform existing industries, but will also likely create entirely new ones.
Machine intelligence is enabling applications we already expect like automated assistants (Siri), adorable robots (Jibo), and identifying people in images (like the highly effective but unfortunately named DeepFace). However, it’s also doing the unexpected: protecting children from sex trafficking, reducing the chemical content in the lettuce we eat, helping us buy shoes online that fit our feet precisely, and destroying 80’s classic video games.
Many companies will be acquired.
I was surprised to find that over 10% of the eligible (non-public) companies on the slide have been acquired. It was in stark contrast to big data landscape we created, which had very few acquisitions at the time.No jaw will drop when I reveal that Google is the number one acquirer, though there were more than 15 different acquirers just for the companies on this chart. My guess is that by the end of 2015 almost another 10% will be acquired. For thoughts on which specific ones will get snapped up in the next year you’ll have to twist my arm…
Big companies have a disproportionate advantage, especially those that build consumer products.
The giants in search (Google, Baidu), social networks (Facebook, LinkedIn, Pinterest), content (Netflix, Yahoo!), mobile (Apple) and e-commerce (Amazon) are in an incredible position. They have massive datasets and constant consumer interactions that enable tight feedback loops for their algorithms (and these factors combine to create powerful network effects) — and they have the most to gain from the low hanging fruit that machine intelligence bears.
Best-in-class personalization and recommendation algorithms have enabled these companies’ success (it’s both impressive and disconcerting that Facebook recommends you add the person you had a crush on in college and Netflix tees up that perfect guilty pleasure sitcom). Now they are all competing in a new battlefield: the move to mobile. Winning mobile will require lots of machine intelligence: state of the art natural language interfaces (like Apple’s Siri), visual search (like Amazon’s “FireFly”), and dynamic question answering technology that tells you the answer instead of providing a menu of links (all of the search companies are wrestling with this).Large enterprise companies (IBM and Microsoft) have also made incredible strides in the field, though they don’t have the same human-facing requirements so are focusing their attention more on knowledge representation tasks on large industry datasets, like IBM Watson’s application to assist doctors with diagnoses.
The talent’s in the New (AI)vy League.
In the last 20 years, most of the best minds in machine intelligence (especially the ‘hardcore AI’ types) worked in academia. They developed new machine intelligence methods, but there were few real world applications that could drive business value.
Now that real world applications of more complex machine intelligence methods like deep belief nets and hierarchical neural networks are starting to solve real world problems, we’re seeing academic talent move to corporate settings. Facebook recruited NYU professors Yann LeCun and Rob Fergus to their AI Lab, Google hired University of Toronto’s Geoffrey Hinton, Baidu wooed Andrew Ng. It’s important to note that they all still give back significantly to the academic community (one of LeCun’s lab mandates is to work on core research to give back to the community, Hinton spends half of his time teaching, Ng has made machine intelligence more accessible through Coursera) but it is clear that a lot of the intellectual horsepower is moving away from academia.
For aspiring minds in the space, these corporate labs not only offer lucrative salaries and access to the “godfathers” of the industry, but, the most important ingredient: data. These labs offer talent access to datasets they could never get otherwise (the ImageNet dataset is fantastic, but can’t compare to what Facebook, Google, and Baidu have in house).
As a result, we’ll likely see corporations become the home of many of the most important innovations in machine intelligence and recruit many of the graduate students and postdocs that would have otherwise stayed in academia.
There will be a peace dividend.
Big companies have an inherent advantage and it’s likely that the ones who will win the machine intelligence race will be even more powerful than they are today. However, the good news for the rest of the world is that the core technology they develop will rapidly spill into other areas, both via departing talent and published research.
Similar to the big data revolution, which was sparked by the release of Google’s BigTable and BigQuery papers, we will see corporations release equally groundbreaking new technologies into the community. Those innovations will be adapted to new industries and use cases that the Googles of the world don’t have the DNA or desire to tackle.
Opportunities for entrepreneurs:
“My company does deep learning for X”
Few words will make you more popular in 2015. That is, if you can credibly say them.Deep learning is a particularly popular method in the machine intelligence field that has been getting a lot of attention. Google, Facebook, and Baidu have achieved excellent results with the method for vision and language based tasks and startups like Enlitic have shown promising results as well.
Yes, it will be an overused buzzword with excitement ahead of results and business models, but unlike the hundreds of companies that say they do “big data”, it’s much easier to cut to the chase in terms of verifying credibility here if you’re paying attention.The most exciting part about the deep learning method is that when applied with the appropriate levels of care and feeding, it can replace some of the intuition that comes from domain expertise with automatically-learned features. The hope is that, in many cases, it will allow us to fundamentally rethink what a best-in-class solution is.
As an investor who is curious about the quirkier applications of data and machine intelligence, I can’t wait to see what creative problems deep learning practitioners try to solve. I completely agree with Jeff Hawkins when he says a lot of the killer applications of these types of technologies will sneak up on us. I fully intend to keep an open mind.
“Acquihire as a business model”
People say that data scientists are unicorns in short supply. The talent crunch in machine intelligence will make it look like we had a glut of data scientists. In the data field, many people had industry experience over the past decade. Most hardcore machine intelligence work has only been in academia. We won’t be able to grow this talent overnight.
This shortage of talent is a boon for founders who actually understand machine intelligence. A lot of companies in the space will get seed funding because there are early signs that the acquihire price for a machine intelligence expert is north of 5x that of a normal technical acquihire (take, for example Deep Mind, where price per technical head was somewhere between $5–10M, if we choose to consider it in the acquihire category). I’ve had multiple friends ask me, only semi-jokingly, “Shivon, should I just round up all of my smartest friends in the AI world and call it a company?” To be honest, I’m not sure what to tell them. (At Bloomberg Beta, we’d rather back companies building for the long term, but that doesn’t mean this won’t be a lucrative strategy for many enterprising founders.)
A good demo is disproportionately valuable in machine intelligence
I remember watching Watson play Jeopardy. When it struggled at the beginning I felt really sad for it. When it started trouncing its competitors I remember cheering it on as if it were the Toronto Maple Leafs in the Stanley Cup finals (disclaimers: (1) I was an IBMer at the time so was biased towards my team (2) the Maple Leafs have not made the finals during my lifetime — yet — so that was purely a hypothetical).
Why do these awe-inspiring demos matter? The last wave of technology companies to IPO didn’t have demos that most of us would watch, so why should machine intelligence companies? The last wave of companies were very computer-like: database companies, enterprise applications, and the like. Sure, I’d like to see a 10x more performant database, but most people wouldn’t care. Machine intelligence wins and loses on demos because 1) the technology is very human, enough to inspire shock and awe, 2) business models tend to take a while to form, so they need more funding for longer period of time to get them there, 3) they are fantastic acquisition bait.Watson beat the world’s best humans at trivia, even if it thought Toronto was a US city. DeepMind blew people away by beating video games. Vicarious took on CAPTCHA. There are a few companies still in stealth that promise to impress beyond that, and I can’t wait to see if they get there.
Demo or not, I’d love to talk to anyone using machine intelligence to change the world. There’s no industry too unsexy, no problem too geeky. I’d love to be there to help so don’t be shy.I hope this landscape chart sparks a conversation. The goal to is make this a living document and I want to know if there are companies or categories missing. I welcome feedback and would like to put together a dynamic visualization where I can add more companies and dimensions to the data (methods used, data types, end users, investment to date, location, etc.) so that folks can interact with it to better explore the space.
Questions and comments: Please email me. Thank you to Andrew Paprocki, Aria Haghighi, Beau Cronin, Ben Lorica, Doug Fulop, David Andrzejewski, Eric Berlow, Eric Jonas, Gary Kazantsev, Gideon Mann, Greg Smithies, Heidi Skinner, Jack Clark, Jon Lehr, Kurt Keutzer, Lauren Barless, Pete Skomoroch, Pete Warden, Roger Magoulas, Sean Gourley, Stephen Purpura, Wes McKinney, Zach Bogue, the Quid team, and the Bloomberg Beta team for your ever-helpful perspectives!
Disclaimer: Bloomberg Beta is an investor in Adatao, Alation, Aviso, Context Relevant, Mavrx, Newsle, Orbital Insights, Pop Up Archive, and two others on the chart that are still undisclosed. We’re also investors in a few other machine intelligence companies that aren’t focusing on areas that were a fit for this landscape, so we left them off.
For the full resolution version of the landscape please click here.

The Competitive Landscape for Machine Intelligence

By Hugo Angel,

hbr_machineintelligence_nov16-01-machine-850x478
da6nci_mi-landscape-3_7
Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we’ve tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term “machine intelligence” over “AI.”)
In past years, we heard mostly from startup founders and academics — people who pay attention to early, far-reaching trends in technology. But this year was different. This year we’ve heard more from Fortune 500 executives with questions about machine intelligence than from startup founders.
These executives are asking themselves what to do. Over the past year, machine intelligence has exploded, with $5 billion in venture investment, a few big acquisitions, and hundreds of thousands of people reading our earlier research. As with the internet in the 1990s, executives are realizing that this new technology could change everything, but nobody knows exactly how or when.
If this year’s landscape shows anything, it’s that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses.
And unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage.
So what should the Fortune 500 and other companies be doing to get started?
Make Talent More Productive
One way to immediately begin getting the value of machine intelligence is to support your talent with readily available machine intelligence productivity tools. Some of the earliest wins have been productivity tools tuned to specific areas of knowledge work — what we call “Enterprise Functions” in our landscape. With these tools, every employee can get some of the powers previously available only to CEOs.
These tools can aid with monitoring and predicting (e.g., companies like Clari forecasting client-by-client sales to help prioritize deals) and with coaching and training (Textio’s* predictive text-editing platform to help employees write more-effective documents).
Find Entirely New Sources of Data
The next step is to use machine intelligence to realize value from new sources of data, which we highlight in the “Enterprise Intelligence” section of the landscape. These new sources are now accessible because machine intelligence software can rapidly review enormous amounts of data in a way that would have been too difficult and expensive for people to do.
Imagine if you could afford to have someone listen to every audio recording of your salespeople and predict their performance, or have a team look at every satellite image taken from space and determine what macroeconomic indicators could be gleaned from them. These data sources might already be owned by your company (e.g., transcripts of customer service conversations or sensor data predicting outages and required maintenance), or they might be newly available in the outside world (data on the open web providing competitive information).
Rethink How You Build Software
Let’s say you’ve tried some new productivity tools and started to mine new sources of data for insight. The next frontier in capturing machine intelligence’s value is building a lasting competitive advantage based on this new kind of software.
But machine intelligence is not just about better software; it requires entirely new processes and a different mindset. Machine intelligence is a new discipline for managers to learn, one that demands a new class of software talent and a new organizational structure.
Most IT groups think in terms of applications and data. New machine intelligence IT groups will think about applications, data, and models. Think of software as the combination of code, data, and a model. “Model” here means business rules, like rules for approving loans or adjusting power consumption in data centers. In traditional software, programmers created these rules by hand. Today machine intelligence can use data and new algorithms to generate a model too complex for any human programmer to write.
With traditional software, the model changes only when programmers explicitly rewrite it. With machine intelligence, companies can create models that evolve much more regularly, allowing you to build a lasting advantage that strengthens over time as the model “learns.”
Think of these models as narrowly focused employees with great memories and not-so-great social skills — idiot savants. They can predict how best to grow the business, make customers happier, or cut costs. But they’ll often fail miserably if you try to apply them to something new, or, worse, they may degrade invisibly as your business and data change.
All of this means that the discipline of creating machine intelligence software differs from traditional software, and companies need to staff accordingly. Luckily, though finding the right talent may be hard, the tools that developers need to build this software is readily available.
How robotics and machine learning are changing business.

For the first time, there is a maturing “Stack” (see our landscape) of building blocks that companies can use to practice the new discipline of machine intelligence. Many of these tools are available as free, open-source libraries from technology companies such as

  • Google (TensorFlow),
  • Microsoft (CNTK), or
  • Amazon (DSSTNE).

Others make it easier for data scientists to collaborate(see “Data Science”) and manage machine intelligence models (“Machine Learning”).

If your CEO is struggling to answer the question of how machine intelligence will change your industry, take a look at the range of markets in our landscape. The startups in these sections give a sense of how different industries may be altered. Machine intelligence’s first useful applications in an industry tend to use data that previously had lain dormant. Health care is a prime example: We’re seeing predictive models that run on patient data and computer vision that diagnoses disease from medical images and gleans lifesaving insights from genomic data. Next up will be finance, transportation, and agriculture because of the volume of data available and their sheer economic value.
Your company will still need to decide how much to trust these models and how much power to grant them in making business decisions. In some cases the risk of an error will be too great to justify the speed and new capabilities. Your company will also need to decide how often and with how much oversight to revise your models. But the companies that decide to invest in the right models and successfully embed machine intelligence in their organization will improve by default as their models learn from experience.
Economists have long wondered why the so-called computing revolution has failed to deliver productivity gains. Machine intelligence will finally realize computing’s promise. The C-suites and boardrooms that recognize that fact first — and transform their ways of working accordingly — will outrun and outlast their competitors.
*The authors’ fund has invested in this company.
Shivon Zilis is a partner and founding member of Bloomberg Beta, which invests heavily in the future of work. She focuses on early-stage data and machine intelligence investments.
James Cham is a Partner at Bloomberg Beta where he invests in data-centric and machine learning-related companies.

Google’s AI can now learn from its own memory independently

By Hugo Angel,

An artist’s impression of the DNC. Credit: DeepMind
The DeepMind artificial intelligence (AI) being developed by Google‘s parent company, Alphabet, can now intelligently build on what’s already inside its memory, the system’s programmers have announced.
Their new hybrid system – called a Differential Neural Computer (DNC)pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. 
What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain.
These models… can learn from examples like neural networks, but they can also store complex data like computers,” write DeepMind researchers Alexander Graves and Greg Wayne in a blog post.
At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones. Over time, it’s able to get more and more accurate, figuring out how to use its memory data banks at the same time.
Take a family tree: after being told about certain relationships, the DNC was able to figure out other family connections on its own – writing, rewriting, and optimising its memory along the way to pull out the correct information at the right time.
Another example the researchers give is a public transit system, like the London Underground. Once it’s learned the basics, the DNC can figure out more complex relationships and routes without any extra help, relying on what it’s already got in its memory banks.
In other words, it’s functioning like a human brain, taking data from memory (like tube station positions) and figuring out new information (like how many stops to stay on for).
Of course, any smartphone mapping app can tell you the quickest way from one tube station to another, but the difference is that the DNC isn’t pulling this information out of a pre-programmed timetable – it’s working out the information on its own, and juggling a lot of data in its memory all at once.
The approach means a DNC system could take what it learned about the London Underground and apply parts of its knowledge to another transport network, like the New York subway.
The system points to a future where artificial intelligence could answer questions on new topics, by deducing responses from prior experiences, without needing to have learned every possible answer beforehand.
Credit: DeepMind

Of course, that’s how DeepMind was able to beat human champions at Go – by studying millions of Go moves. But by adding external memory, DNCs are able to take on much more complex tasks and work out better overall strategies, its creators say.

Like a conventional computer, [a DNC] can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data,” the researchers explain in Nature.
In another test, the DNC was given two bits of information: “John is in the playground,” and “John picked up the football.” With those known facts, when asked “Where is the football?“, it was able to answer correctly by combining memory with deep learning. (The football is in the playground, if you’re stuck.)
Making those connections might seem like a simple task for our powerful human brains, but until now, it’s been a lot harder for virtual assistants, such as Siri, to figure out.
With the advances DeepMind is making, the researchers say we’re another step forward to producing a computer that can reason independently.
And then we can all start enjoying our robot-driven utopia – or technological dystopia – depending on your point of view.
ORIGINAL: ScienceAlert
By DAVID NIELD

14 OCT 2016

Google’s Deep Mind Gives AI a Memory Boost That Lets It Navigate London’s Underground

By Hugo Angel,

Photo: iStockphoto

Google’s DeepMind artificial intelligence lab does more than just develop computer programs capable of beating the world’s best human players in the ancient game of Go. The DeepMind unit has also been working on the next generation of deep learning software that combines the ability to recognize data patterns with the memory required to decipher more complex relationships within the data.

Deep learning is the latest buzz word for artificial intelligence algorithms called neural networks that can learn over time by filtering huge amounts of relevant data through many “deep” layers. The brain-inspired neural network layers consist of nodes (also known as neurons). Tech giants such as Google, Facebook, Amazon, and Microsoft have been training neural networks to learn how to better handle tasks such as recognizing images of dogs or making better Chinese-to-English translations. These AI capabilities have already benefited millions of people using Google Translate and other online services.
But neural networks face huge challenges when they try to rely solely on pattern recognition without having the external memory to store and retrieve information. To improve deep learning’s capabilities, Google DeepMind created a “differentiable neural computer” (DNC) that gives neural networks an external memory for storing information for later use.
Neural networks are like the human brain; we humans cannot assimilate massive amounts of data and we must rely on external read-write memory all the time,” says Jay McClelland, director of the Center for Mind, Brain and Computation at Stanford University. “We once relied on our physical address books and Rolodexes; now of course we rely on the read-write storage capabilities of regular computers.
McClelland is a cognitive scientist who served as one of several independent peer reviewers for the Google DeepMind paper that describes development of this improved deep learning system. The full paper is presented in the 12 Oct 2016 issue of the journal Nature.
The DeepMind team found that the DNC system’s combination of the neural network and external memory did much better than a neural network alone in tackling the complex relationships between data points in so-called “graph tasks.” For example, they asked their system to either simply take any path between points A and B or to find the shortest travel routes based on a symbolic map of the London Underground subway.
An unaided neural network could not even finish the first level of training, based on traveling between two subway stations without trying to find the shortest route. It achieved an average accuracy of just 37 percent after going through almost two million training examples. By comparison, the neural network with access to external memory in the DNC system successfully completed the entire training curriculum and reached an average of 98.8 percent accuracy on the final lesson.
The external memory of the DNC system also proved critical to success in performing logical planning tasks such as solving simple block puzzle challenges. Again, a neural network by itself could not even finish the first lesson of the training curriculum for the block puzzle challenge. The DNC system was able to use its memory to store information about the challenge’s goals and to effectively plan ahead by writing its decisions to memory before acting upon them.
In 2014, DeepMind’s researchers developed another system, called the neural Turing machine, that also combined neural networks with external memory. But the neural Turing machine was limited in the way it could access “memories” (information) because such memories were effectively stored and retrieved in fixed blocks or arrays. The latest DNC system can access memories in any arbitrary location, McClelland explains.
The DNC system’s memory architecture even bears a certain resemblance to how the hippocampus region of the brain supports new brain cell growth and new connections in order to store new memories. Just as the DNC system uses the equivalent of time stamps to organize the storage and retrieval of memories, human “free recall” experiments have shown that people are more likely to recall certain items in the same order as first presented.
Despite these similarities, the DNC’s design was driven by computational considerations rather than taking direct inspiration from biological brains, DeepMind’s researchers write in their paper. But McClelland says that he prefers not to think of the similarities as being purely coincidental.
The design decisions that motivated the architects of the DNC were the same as those that structured the human memory system, although the latter (in my opinion) was designed by a gradual evolutionary process, rather than by a group of brilliant AI researchers,” McClelland says.
Human brains still have significant advantages over any brain-inspired deep learning software. For example, human memory seems much better at storing information so that it is accessible by both context or content, McClelland says. He expressed hope that future deep learning and AI research could better capture the memory advantages of biological brains.
 
DeepMind’s DNC system and similar neural learning systems may represent crucial steps for the ongoing development of AI. But the DNC system still falls well short of what McClelland considers the most important parts of human intelligence.
The DNC is a sophisticated form of external memory, but ultimately it is like the papyrus on which Euclid wrote the elements. The insights of mathematicians that Euclid codified relied (in my view) on a gradual learning process that structured the neural circuits in their brains so that they came to be able to see relationships that others had not seen, and that structured the neural circuits in Euclid’s brain so that he could formulate what to write. We have a long way to go before we understand fully the algorithms the human brain uses to support these processes.
It’s unclear when or how Google might take advantage of the capabilities offered by the DNC system to boost its commercial products and services. The DeepMind team was “heads down in research” or too busy with travel to entertain media questions at this time, according to a Google spokesperson.
But Herbert Jaeger, professor for computational science at Jacobs University Bremen in Germany, sees the DeepMind team’s work as a “passing snapshot in a fast evolution sequence of novel neural learning architectures.” In fact, he’s confident that the DeepMind team already has something better than the DNC system described in the Nature paper. (Keep in mind that the paper was submitted back in January 2016.)
DeepMind’s work is also part of a bigger trend in deep learning, Jaeger says. The leading deep learning teams at Google and other companies are racing to build new AI architectures with many different functional modules—among them, attentional control or working memory; they then train the systems through deep learning.
The DNC is just one among dozens of novel, highly potent, and cleverly-thought-out neural learning systems that are popping up all over the place,” Jaeger says.
ORIGINAL: IEEE Spectrum
12 Oct 2016

Partnership on Artificial Intelligence to Benefit People and Society

By Hugo Angel,

homebg-2

Established to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

THE LATEST
INDUSTRY LEADERS ESTABLISH PARTNERSHIP ON AI BEST PRACTICES
Press ReleasesSeptember 28, 2016 NEW YORK —  IBM, DeepMind,/Google,  Microsoft, Amazon, and Facebook today announced that they will create a non-profit organization that will work to advance public understanding of artificial intelligence technologies (AI) and formulate best practices on the challenges and opportunities within the field. Academics, non-profits, and specialists in policy and ethics will be invited to join the Board of the organization, named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI).

The objective of the Partnership on AI is to address opportunities and challenges with AI technologies to benefit people and society. Together, the organization’s members will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology. It does not intend to lobby government or other policymaking bodies.

The organization’s founding members will each contribute financial and research resources to the partnership and will share leadership with independent third-parties, including academics, user group advocates, and industry domain experts. There will be equal representation of corporate and non-corporate members on the board of this new organization. The Partnership is in discussions with professional and scientific organizations, such as the Association for the Advancement of Artificial Intelligence (AAAI), as well as non-profit research groups including the Allen Institute for Artificial Intelligence (AI2), and anticipates announcements regarding additional participants in the near future.

AI technologies hold tremendous potential to improve many aspects of life, ranging from healthcare, education, and manufacturing to home automation and transportation. Through rigorous research, the development of best practices, and an open and transparent dialogue, the founding members of the Partnership on AI hope to maximize this potential and ensure it benefits as many people as possible.

… Continue reading

Show and Tell: image captioning open sourced in TensorFlow

By Hugo Angel,

 In 2014, research scientists on the Google Brain team trained a machine learning system to automatically produce captions that accurately describe images. Further development of that system led to its success in the Microsoft COCO 2015 image captioning challenge, a competition to compare the best algorithms for computing accurate image captions, where it tied for first place.
Today, we’re making the latest version of our image captioning system available as an open source model in TensorFlow.
This release contains significant improvements to the computer vision component of the captioning system, is much faster to train, and produces more detailed and accurate descriptions compared to the original system. These improvements are outlined and analyzed in the paper Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge, published in IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatically captioned by our system.
So what’s new? 
Our 2014 system used the Inception V1 image classification model to initialize the image encoder, which
produces the encodings that are useful for recognizing different objects in the images. This was the best image model available at the time, achieving 89.6% top-5 accuracy on the benchmark ImageNet 2012 image classification task. We replaced this in 2015 with the newer Inception V2 image classification model, which achieves 91.8% accuracy on the same task.The improved vision component gave our captioning system an accuracy boost of 2 points in the BLEU-4 metric (which is commonly used in machine translation to evaluate the quality of generated sentences) and was an important factor of its success in the captioning challenge.Today’s code release initializes the image encoder using the Inception V3 model, which achieves 93.9% accuracy on the ImageNet classification task. Initializing the image encoder with a better vision model gives the image captioning system a better ability to recognize different objects in the images, allowing it to generate more detailed and accurate descriptions. This gives an additional 2 points of improvement in the BLEU-4 metric over the system used in the captioning challenge.Another key improvement to the vision component comes from fine-tuning the image model. This step addresses the problem that the image encoder is initialized by a model trained to classify objects in images, whereas the goal of the captioning system is to describe the objects in images using the encodings produced by the image model.  For example, an image classification model will tell you that a dog, grass and a frisbee are in the image, but a natural description should also tell you the color of the grass and how the dog relates to the frisbee.  In the fine-tuning phase, the captioning system is improved by jointly training its vision and language components on human generated captions. This allows the captioning system to transfer information from the image that is specifically useful for generating descriptive captions, but which was not necessary for classifying objects. In particular,  after fine-tuning it becomes better at correctly describing the colors of objects. Importantly, the fine-tuning phase must occur after the language component has already learned to generate captions – otherwise, the noisiness of the randomly initialized language component causes irreversible corruption to the vision component. For more details, read the full paper here.
Left: the better image model allows the captioning model to generate more detailed and accurate descriptions. Right: after fine-tuning the image model, the image captioning system is more likely to describe the colors of objects correctly.
Until recently our image captioning system was implemented in the DistBelief software framework. The TensorFlow implementation released today achieves the same level of accuracy with significantly faster performance: time per training step
is just 0.7 seconds in TensorFlow compared to 3 seconds in DistBelief on an Nvidia K20 GPU, meaning that total training time is just 25% of the time previously required.
A natural question is whether our captioning system can generate novel descriptions of previously unseen contexts and interactions. The system is trained by showing it hundreds of thousands of images that were captioned manually by humans, and it often re-uses human captions when presented with scenes similar to what it’s seen before.
When the model is presented with scenes similar to what it’s seen before, it will often re-use human generated captions.
So does it really understand the objects and their interactions in each image? Or does it always regurgitate descriptions from the training data? Excitingly, our model does indeed develop the ability to generate accurate new captions when presented with completely new scenes, indicating a deeper understanding of the objects and context in the images. Moreover, it learns how to express that knowledge in natural-sounding English phrases despite receiving no additional language training other than reading the human captions.
 

Our model generates a completely new caption using concepts learned from similar scenes in the training set
We hope that sharing this model in TensorFlow will help push forward image captioning research and applications, and will also
allow interested people to learn and have fun. To get started training your own image captioning system, and for more details on the neural network architecture, navigate to the model’s home-page here. While our system uses the Inception V3 image classification model, you could even try training our system with the recently released Inception-ResNet-v2 model to see if it can do even better!

ORIGINAL: Google Blog

by Chris Shallue, Software Engineer, Google Brain Team
September 22, 2016

What Are The Differences Between AI, Machine Learning, NLP, And Deep Learning?

By Hugo Angel,

(Image: Creative Commons)
What is the difference between AI, Machine Learning, NLP, and Deep Learning? originally appeared on Quora: the knowledge sharing network where compelling questions are answered by people with unique insights.Answer by Dmitriy Genzel, PhD in Computer Science, on Quora:

  • AI (Artificial intelligence) is a subfield of computer science that was created in the 1960s, and it was/is concerned with solving tasks that are easy for humans but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic and includes all kinds of tasks such as  
    • planning, 
    • moving around in the world, 
    • recognizing objects and sounds, 
    • speaking, 
    • translating, 
    • performing social or business transactions, 
    • creative work (making art or poetry), 
    • etc.
  • NLP (Natural language processing) is simply the part of AI that has to do with language (usually written).
  • Machine learning is concerned with one aspect of this:
    • given some AI problem that can be described in discrete terms (e.g. out of a particular set of actions, which one is the right one), and
    • given a lot of information about the world,
    • figure out what is the “correct” action, without having the programmer program it in.
    • Typically some outside process is needed to judge whether the action was correct or not.
    • In mathematical terms, it’s a function: you feed in some input, and you want it to to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behavior, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning.
  • Deep learning is one kind of machine learning that’s very popular now. It involves a particular kind of mathematical model that can be thought of as a composition of simple blocks (function composition) of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
Add caption

The word “deep” means that the composition has many of these blocks stacked on top of each other, and the tricky bit is how to adjust the blocks that are far from the output, since a small change there can have very indirect effects on the output. This is done via something called Backpropagation inside of a larger process called Gradient descent which lets you change the parameters in a way that improves your model.

Artificial Intelligence: What is artificial intelligence and why do we need it?
Machine Learning: What is machine learning?
Natural Language Processing: What makes natural language processing difficult?

ORIGINAL: Quora
June 8, 2016

WaveNet: A Generative Model for Raw Audio by Google DeepMind

By Hugo Angel,

WaveNet: A Generative Model for Raw Audio
This post presents WaveNet, a deep generative model of raw audio waveforms. We show that WaveNets are able to generate speech which mimics any human voice and which sounds more natural than the best existing Text-to-Speech systems, reducing the gap with human performance by over 50%.
We also demonstrate that the same network can be used to synthesize other audio signals such as music, and present some striking samples of automatically generated piano pieces.
Talking Machines
Allowing people to converse with machines is a long-standing dream of human-computer interaction. The ability of computers to understand natural speech has been revolutionised in the last few years by the application of deep neural networks (e.g.,Google Voice Search). However, generating speech with computers — a process usually referred to as speech synthesis or text-to-speech (TTS) — is still largely based on so-called concatenative TTS, where a very large database of short speech fragments are recorded from a single speaker and then recombined to form complete utterances. This makes it difficult to modify the voice (for example switching to a different speaker, or altering the emphasis or emotion of their speech) without recording a whole new database.
This has led to a great demand for parametric TTS, where all the information required to generate the data is stored in the parameters of the model, and the contents and characteristics of the speech can be controlled via the inputs to the model. So far, however, parametric TTS has tended to sound less natural than concatenative, at least for syllabic languages such as English. Existing parametric models typically generate audio signals by passing their outputs through signal processing algorithms known asvocoders.
WaveNet changes this paradigm by directly modelling the raw waveform of the audio signal, one sample at a time. As well as yielding more natural-sounding speech, using raw waveforms means that WaveNet can model any kind of audio, including music.
WaveNets

 

Wave animation

 

Researchers usually avoid modelling raw audio because it ticks so quickly: typically 16,000 samples per second or more, with important structure at many time-scales. Building a completely autoregressive model, in which the prediction for every one of those samples is influenced by all previous ones (in statistics-speak, each predictive distribution is conditioned on all previous observations), is clearly a challenging task.
However, our PixelRNN and PixelCNN models, published earlier this year, showed that it was possible to generate complex natural images not only one pixel at a time, but one colour-channel at a time, requiring thousands of predictions per image. This inspired us to adapt our two-dimensional PixelNets to a one-dimensional WaveNet.
Architecture animation

 

 The above animation shows how a WaveNet is structured. It is a fully convolutional neural network, where the convolutional layers have various dilation factors that allow its receptive field to grow exponentially with depth and cover thousands of timesteps.At training time, the input sequences are real waveforms recorded from human speakers. After training, we can sample the network to generate synthetic utterances. At each step during sampling a value is drawn from the probability distribution computed by the network. This value is then fed back into the input and a new prediction for the next step is made. Building up samples one step at a time like this is computationally expensive, but we have found it essential for generating complex, realistic-sounding audio.
Improving the State of the Art
We trained WaveNet using some of Google’s TTS datasets so we could evaluate its performance. The following figure shows the quality of WaveNets on a scale from 1 to 5, compared with Google’s current best TTS systems (parametric and concatenative), and with human speech using Mean Opinion Scores (MOS). MOS are a standard measure for subjective sound quality tests, and were obtained in blind tests with human subjects (from over 500 ratings on 100 test sentences). As we can see, WaveNets reduce the gap between the state of the art and human-level performance by over 50% for both US English and Mandarin Chinese.
For both Chinese and English, Google’s current TTS systems are considered among the best worldwide, so improving on both with a single model is a major achievement.

 

Here are some samples from all three systems so you can listen and compare yourself:

US English:

Mandarin Chinese:

Knowing What to Say

In order to use WaveNet to turn text into speech, we have to tell it what the text is. We do this by transforming the text into a sequence of linguistic and phonetic features (which contain information about the current phoneme, syllable, word, etc.) and by feeding it into WaveNet. This means the network’s predictions are conditioned not only on the previous audio samples, but also on the text we want it to say.
If we train the network without the text sequence, it still generates speech, but now it has to make up what to say. As you can hear from the samples below, this results in a kind of babbling, where real words are interspersed with made-up word-like sounds:

 

Notice that non-speech sounds, such as breathing and mouth movements, are also sometimes generated by WaveNet; this reflects the greater flexibility of a raw-audio model.
As you can hear from these samples, a single WaveNet is able to learn the characteristics of many different voices, male and female. To make sure it knew which voice to use for any given utterance, we conditioned the network on the identity of the speaker. Interestingly, we found that training on many speakers made it better at modelling a single speaker than training on that speaker alone, suggesting a form of transfer learning.
By changing the speaker identity, we can use WaveNet to say the same thing in different voices:

 

Similarly, we could provide additional inputs to the model, such as emotions or accents, to make the speech even more diverse and interesting.
Making Music
Since WaveNets can be used to model any audio signal, we thought it would also be fun to try to generate music. Unlike the TTS experiments, we didn’t condition the networks on an input sequence telling it what to play (such as a musical score); instead, we simply let it generate whatever it wanted to. When we trained it on a dataset of classical piano music, it produced fascinating samples like the ones below:

 

WaveNets open up a lot of possibilities for TTS, music generation and audio modelling in general. The fact that directly generating timestep per timestep with deep neural networks works at all for 16kHz audio is really surprising, let alone that it outperforms state-of-the-art TTS systems. We are excited to see what we can do with them next.
For more details, take a look at our paper.


ORIGINAL: Google DeepMind
Aäron van den Oord. Research Scientist, DeepMind
Heiga Zen. Research Scientist, Google
Sander Dieleman. Research Scientist, DeepMind
8 September 2016

© 2016 DeepMind Technologies Limited

 

Carbon Nanotube Transistors Finally Outperform Silicon

By Hugo Angel,

Photo: Stephanie Precourt/UW-Madison College of Engineering

Back in the 1990s, observers predicted that the single-walled carbon nanotube (SWCNT) would be the nanomaterial that pushed silicon aside and created a post-CMOS world where Moore’s Law could continue its march towards ever=smaller chip dimensions. All of that hope was swallowed up by inconsistencies between semiconducting and metallic SWCNTs and the vexing issue of trying to get them all to align on a wafer.

The introduction of graphene seemed to take the final bit of luster off of carbon nanotubes’ shine, but the material, which researchers have been using to make transistors for over 20 years, has experienced a renaissance of late.
Now, researchers at the University of Wisconsin-Madison (UW-Madison) have given SWCNTs a new boost in their resurgence by using them to make a transistor that outperforms state-of-the-art silicon transistors.
This achievement has been a dream of nanotechnology for the last 20 years,” said Michael Arnold, a professor at UW-Madison, in a press release. “Making carbon nanotube transistors that are better than silicon transistors is a big milestone,” Arnold added. “[It’s] a critical advance toward exploiting carbon nanotubes in logic, high-speed communications, and other semiconductor electronics technologies.
In research described in the journal Science Advances, the UW-Madison researchers were able to achieve a current that is 1.9 times as fast as that seen in silicon transistors. The measure of how rapidly the current that can travel through the channel between a transistor’s source and drain determines how fast the circuit is. The more current there is, the more quickly the gate of the next device in the circuit can be charged .

The key to getting the nanotubes to create such a fast transistor was a new process that employs polymers to sort between the metallic and semiconducting SWCNTs to create an ultra-high purity of solution.
We’ve identified specific conditions in which you can get rid of nearly all metallic nanotubes, [leaving] less than 0.01 percent metallic nanotubes [in a sample],” said Arnold.
The researchers had already tackled the problem of aligning and placing the nanotubes on a wafer two years ago when they developed a process they dubbed “floating evaporative self-assembly.” That technique uses a hydrophobic substrate and partially submerges it in water. Then the SWCNTs are deposited on its surface and the substrate removed vertically from the water.
In our research, we’ve shown that we can simultaneously overcome all of these challenges of working with nanotubes, and that has allowed us to create these groundbreaking carbon nanotube transistors that surpass silicon and gallium arsenide transistors,” said Arnold.
In the video below, Arnold provides a little primer on SWCNTs and what his group’s research with them could mean to the future of electronics.

In continuing research, the UW-Madison team will be aiming to replicate the manufacturability of silicon transistors. To date, they have managed to scale their alignment and deposition process to 1-inch-by-1-inch wafers; the longer-term goal is to bring this up to commercial scales.

Arnold added: “There has been a lot of hype about carbon nanotubes that hasn’t been realized, and that has kind of soured many people’s outlook. But we think the hype is deserved. It has just taken decades of work for the materials science to catch up and allow us to effectively harness these materials.

ORIGINAL: IEEE

By Dexter Johnson
6 Sep 2016

Quantum Computers Explained – Limits of Human Technology

By Hugo Angel,

Where are the limits of human technology? And can we somehow avoid them? This is where quantum computers become very interesting. 
Check out THE NOVA PROJECT to learn more about dark energy: www.nova.org.au 


ORIGINAL: YouTube



Robots Can Now Learn Just By Observing, Without Being Told What To Look For

By Hugo Angel,

Machines are getting smarter every day—and that is both good and terrifying.
[Illustrations: v_alex/iStock]
Scientists at the University of Sheffield have come up with a way for machines to learn just by looking. They don’t need to be told what to look for—they can just learn how a system works by observing it. The method is called Turing Learning and is inspired by Alan Turing’s famous test.
For a computer to learn, usually it has to be told what to look for. For instance, if you wanted to teach a robot to paint like Picasso, you’d train software to mimic real Picasso paintings. “Someone would have to tell the algorithms what is considered similar to a Picasso to begin with,” says Roderick Gross, in a news release.
Turing Learning would not require such prior knowledge, he says. It would use two computer systems, plus the original “system” you’re investigating: a shoal of fish, a Picasso painting, anything. One of the computer systems tries to copy the real-world system as closely as possible. The other computer is an observer. Its task is to watch the goings-on and try to discern which of the systems is real, and which is the copy. If it guesses right, it gets a reward. At the same time, the counterfeit system is rewarded if it fools the observer.
Proceeding like this, the counterfeit models get better and better, and the observer works out how to distinguish real from fake to a more and more accurate degree. In the end, it can not only tell real from fake, but it has also—almost as a by-product of the process—created a precise model of how the genuine system works.
The experiment is named after Alan Turing‘s famous test for artificial intelligence, which says that if a computer program can fool a human observer into believing it is a real person, then it can be considered intelligent. In reality this never really works, as a) convincing a person that you’re another person isn’t a guarantee of intelligence, and b) many computer programs have simply been designed to game the human observers.
Turing Learning, though, is actually practical. It can be used to teach robots certain behaviors, but perhaps more useful is the categorization it performs. Set a Turing Learning machine loose on a swarm of insects, for instance, and it could tease out details in the behavior of a bee colony that remain invisible to humans.
The systems can also be used to recognize abnormal behavior, without first teaching the system what constitutes abnormal behavior. The possibilities here are huge, because noticing oddities in otherwise uniform behavior is something we humans can be terrible at. Look at airport security, for example and how often TSA agents miss guns, explosives, and other weapons.
The technique could also be used in video games to make the virtual players act more like real human players to monitor livestock for odd behaviors that might signal health problems, and for security purposes like lie detection.
In some ways, the technology is terrifying, as computers are able to get to the very basics of how things behave. On the other hand, they still need to be told what to do with that knowledge, so at least there’s something for us puny humans to do in the world of the future.
ORIGINAL: FastCoExist
09.07.16

Deep Learning With Python & Tensorflow – PyConSG 2016

By Hugo Angel,

ORIGINAL: Pycon.SG
Jul 5, 2016
Speaker: Ian Lewis
Description
Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How can you use a model that has been trained in your production app? In this talk I will discuss how you can use TensorFlow to create Deep Learning applications and how to deploy them into production.
Abstract
Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production application?
TensorFlow is a new Open-Source framework created at Google for building Deep Learning applications. Tensorflow allows you to construct easy to understand data flow graphs in Python which form a mathematical and logical pipeline. Creating data flow graphs allow easier visualization of complicated algorithms as well as running the training operations over multiple hardware GPUs in parallel.
In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.
Event Page: https://pycon.sg
Produced by Engineers.SG