Machine Intelligence with Michael Schmidt: OpenAI and doomsday artificial intelligence

Posted by Michael Schmidt

01.06.2016 09:30 AM

Speaking at the Open Data Science Conference (ODSC) last week, I discussed where artificial intelligence is going, what it will automate, and what its impact will be on science, business, and jobs. While the impact from Eureqa has been overwhelmingly positive, many are warning about a darker future:

Robot.jpg“With artificial intelligence we are summoning the demon. In all those stories where there’s the guy with the pentagram and the holy water, he’s sure he can control the demon [but it] doesn’t work out.” –Elon Musk

Elon Musk, in the above quote, is worried about a very specific area of AI research – the sentient autonomous AI and robotics research as popularized in movies.

In fact, the press has characterized Eureqa as a “Robot Scientist” as well, speculating advanced tasks like scientific inquiry may become automated by machines one day. However, Eureqa was born out of the challenge to accelerate and scale the complexity of problems that we can tackle and solve – not simply mimic human behavior.

The areas of AI focused on simply learning tasks and replicating human behavior (e.g. IBM Watson or Google AlphaGo) are much hazier. It’s not clear what type of impact this trajectory will have.

The research group OpenAI, founded to support “beneficial” AI research, signaled they are focused entirely on this type of AI last month. Their platform OpenAI Gym enables researchers to develop reinforcement learning algorithms. Reinforcement learning is a class of machine learning algorithms used for tasks like chat bots, video games, and robots. Interestingly, it doesn’t typically start with data or try to learn from an existing data set; it attempts to learn to control an agent (like a robot) based purely on a set of actions it can take and its current state.

The downside of reinforcement learning is that it is not immediately applicable or natural for most business problems that I observe today. That is, businesses are not clamouring for chat bots or interactive agents; they tend to have more data than they can analyze and are invested in putting it to work instead.

Of all areas of machine learning and AI, reinforcement learning may be the furthest out. But early research is producing some exciting results, for example learning to play videogames like Mario from trial and error.

It’s important to keep in mind, however, how far there is to go before sentient AI systems Musk and OpenAI are alluding to may arise. Last week the White House Science and Technology Office concluded that despite improvement in areas like machine vision and speech understanding, AI research is still far from matching the flexibility and learning capability of the human mind. That said, I’ll be rooting for OpenAI to keep this area of AI beneficial for us as it matures.

Topics: Artificial intelligence, OpenAI, Reinforcement learning

Machine Intelligence with Michael Schmidt: IBM’s Watson, Eureqa, and the race for smart machines

Posted by Michael Schmidt

16.05.2016 11:12 AM

Three months ago I spoke at a conference affectionately titled “Datapalooza” sponsored by IBM. My talk covered how modern AI can infer the features and transformations that make raw data predictive. I’m not sure exactly how many IBM people were in the crowd, but two IBM database and analytics leads grabbed me after the talk:

“We love what you’re doing. The Watson team is attempting to do things like this internally but is nowhere near this yet.” – [names withheld]

What’s interesting is that Watson has been coming up more and more recently when I speak to customers. The billions of dollars IBM has invested to market Watson has created an air of mystery and hype around what artificial intelligence can do. In fact, IBM is expecting Watson to grow to over $1B per year in revenue in the next 18 months. Yet we haven’t seen any prospect choose Watson over Eureqa to date. So what’s going on?


Speaking at IBM’s Datapalooza (2016) in Seattle, WA.

I remember the excitement in the AI lab (CCSL) at Cornell University when IBM’s Watson computer competed in the game show Jeopardy in 2011. A group of us watched live as the computer beat the show’s top player, Ken Jennings.

IBM had pioneered one of the most interactive AI systems in history. Instead of simulating moves in chess more than before (as it’s predecessor Deep Blue had done), Watson appeared to actually “think.” It interpreted speech and searched data sources for a relevant response. It inspired similar technology, like Apple’s Siri and Microsoft’s Cortana, which came out over the next few years.

Unlike Apple, Google, Facebook, and others, however, IBM recognized an enormous opportunity in the market. Every business in the world today stockpiles data faster than can be analyzed. Literally hundreds of billions of dollars in value lies in the applications of this data. Perhaps the technology that could win quiz competitions like Jeopardy could unlock some of this value as well. IBM decided to step out of the safe confines of a specific application, and attempted to work with business data and real-world problems with commercial deployments of Watson.


The interest in IBM Watson tied to Jeopardy over 10 years.

Coincidentally, I began working on the preliminary technology behind Eureqa around the same time. Eureqa was focused on a broader challenge; instead of trying to interpret sentences and look up responses, Eureqa was tasked with deducing how any arbitrary system behaved – just provide the data/observations. It became the first AI that could think like a scientist and produce new explanations for how any system worked.

The similarity, and the power, of both Eureqa and Watson is that they are examples of Machine Intelligence – meaning the answers they output can be meaningfully interpreted and understood, as opposed to some statistical prediction or data visualization. But this is where the similarities end.

Watson’s great challenge has been adapting its technology for answering trivia questions to real business problems. Despite the prevalence of unstructured text data, very few new business problems appear to be blocked by the ability to look up relevant information in text. From the WSJ: “According to a review of internal IBM documents and interviews with Watson’s first customers, Watson is having more trouble solving real-life problems.”

The data that most businesses have today consists of log data, event data, sensor data, sales data, or other numeric data (data where Watson’s core technology doesn’t apply). The major questions they need answered relate to what causes other things to happen, what triggers or blocks certain outcomes, or simply what’s possible with the data I have and how do I even begin? To me, the key interest and success behind Eureqa has come from its applicability to real business data and problems. It finds physical relationships, and interpretable models, which answer these types of questions directly.

Earlier this year, IBM announced they are splitting the different components inside Watson into individual services instead of trying to map a complete solution for customers. They may no longer be a pioneer in the space, but perhaps they’re starting to acknowledge what businesses need most today.


Topics: Artificial intelligence, IBM Watson, Machine Intelligence

NSF Uses Artificial Intelligence to Tackle Illegal Tiger Poaching

Posted by Jon Millis

26.04.2016 09:23 AM

AI to the rescue. Forget doomsday scenarios of robots transforming humans into paperclips. This time it’s more positive. The National Science Foundation (NSF) announced it’s turned to artificial intelligence as a critical weapon in the fight against poaching.

Whether killed for skins, “medicine”, or trophy hunting, tigers have been devastated by illegal shooters. Poachers have driven the population of wild tigers down from 60,000 in the early 1900s to just 3,200 today. And with protection relying heavily on human capital and resources that just aren’t there, governments and nonprofits have to get smarter about how they enforce the rule of law before tigers (as well as other species, forests, and coral reefs) disappear.


Currently, ranger patrol routes are mostly “reactive”, keeping tabs on the areas that have been hit hard before and preventing what they can. An NSF-funded team at the University of Southern California, however, has built an AI-driven application called Protection Assistant for Wildlife Security (PAWS) that makes patrolling more predictive, and hence, more effective. PAWS incorporates data on past patrols, evidence of poaching, and complex terrain information like topography, to determine the highest-probability patrol routes while minimizing elevation changes, saving time and energy. As it receives more data, the system “learns” and improves its patrol planning. The application also randomizes patrol routes to avoid falling into predictable patterns that can be anticipated by poachers.

The NSF said that since 2015, non-governmental organizations Panthera and Rimbat have used PAWS to protect forests in Malaysia. The research won the Innovative Applications of Artificial Intelligence award for deployed application, as one of the best AI applications with measurable benefits.

This is not the first instance of leveraging AI for good. Unfortunately, the public is bombarded with negative depictions of AI, with stories like targeted online ads and Facebook’s almost eerie knowledge of its user base dominating the headlines. That’s because negativity sometimes sells more headlines. The truth is, like any technological advancement, the power of AI is in the hands of its users. AI can vastly improve human productivity and thus raise living standards, solve problems, discover new breakthroughs. As more applications like PAWS come to light, we hope that more people will see the incredible good that comes from the power of data, supplementing human expertise to drive towards solutions for the most pressing social, economic and environmental issues of our day.

Topics: Artificial intelligence

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