Wednesday 20th August 2014
The Quest to Build an Artificial Brain
Deep learning has suddenly spread across the commercial tech world, from Google to Microsoft to Baidu to Twitter, just a few years after most AI researchers openly scoffed at it.
All of these tech companies are now exploring a particular type of deep learning called convolutional neural networks, aiming to build web services that can do things like automatically understand natural language and recognize images. At Google, “convnets” power the voice recognition system available on Android phones. At China’s Baidu, they drive a new visual search engine.
But this is just a start. The deep learning community are working to improve the technology. Today’s most widely used convolutional neural nets rely almost exclusively on supervised learning. Basically, that means that if you want it to learn how to identify a particular object, you have to label more than a few examples. Yet unsupervised learning—or learning from unlabeled data—is closer to how real brains learn, and some deep learning research is exploring this area.
“How this is done in the brain is pretty much completely unknown. Synapses adjust themselves, but we don’t have a clear picture for what the algorithm of the cortex is,” says LeCun. “We know the ultimate answer is unsupervised learning, but we don’t have the answer yet.” – Daniela Hernandez
Monday 29th September 2014
AI Expert Predicts 50% of Web Searches Will Soon be Speech and Images
Baidu — the second-largest web search provider in the world, with its biggest user base in its home country of China — has been preparing its systems for a time when text will be just another option for searching, and not necessarily the default.
“In five years, we think 50 percent of queries will be on speech or images,” Andrew Ng, Baidu’s chief scientist and the head of Baidu Research, said Wednesday during a Gigaom meetup on his area of expertise, deep learning.
A type of artificial intelligence, deep learning involves training systems called artificial neural networks on lots of information derived from audio, images, and other inputs, and then presenting the systems with new information and receiving inferences about it in response.
“Speech and images are, in my view, a much more natural way to communicate [than text],” Ng said.
Indeed, already one out of all 10 queries Baidu receives comes through speech, he said. And pointing a smartphone camera at a handbag might identify a particular model more quickly than endlessly rephrasing a typed query. As Ng put it, “It’s easier to show us a picture.”
“I think that whoever wins AI will win the Internet,” Ng said – Jordan Novet
Sunday 18th January 2015
In the past five years, advances in artificial intelligence—in particular, within a branch of AI algorithms called deep neural networks—are putting AI-driven products front-and-center in our lives. Google, Facebook, Microsoft and Baidu, to name a few, are hiring artificial intelligence researchers at an unprecedented rate, and putting hundreds of millions of dollars into the race for better algorithms and smarter computers.
AI problems that seemed nearly unassailable just a few years ago are now being solved. Deep learning has boosted Android’s speech recognition, and given Skype Star Trek-like instant translation capabilities. Google is building self-driving cars, and computer systems that can teach themselves to identify cat videos. Robot dogs can now walk very much like their living counterparts.
“Things like computer vision are starting to work; speech recognition is starting to work. There’s quite a bit of acceleration in the development of AI systems,” says Bart Selman, a Cornell professor and AI ethicist – Robert Mcmillan
Monday 18th May
Baidu’s AI Supercomputer Beats Google at Image Recognition
Chinese search giant Baidu says it has invented a powerful supercomputer that brings new muscle to an artificial-intelligence technique giving software more power to understand speech, images, and written language.
The new computer, called Minwa and located in Beijing, has 72 powerful processors and 144 graphics processors, known as GPUs. Late Monday, Baidu released a paper claiming that the computer had been used to train machine-learning software that set a new record for recognizing images, beating a previous mark set by Google.
“Our company is now leading the race in computer intelligence,” said Ren Wu, a Baidu scientist working on the project, speaking at the Embedded Vision Summit on Tuesday.
Minwa’s computational power would probably put it among the 300 most powerful computers in the world if it weren’t specialized for deep learning, said Wu. “I think this is the fastest supercomputer dedicated to deep learning,” he said. “We have great power in our hands—much greater than our competitors.” – Tom Simonite
Sunday 13th March 2016
Investing in Robotics and AI Companies
Here are some AI (and robotics) related companies to think about.
I’m not saying you should buy them (now) or sell for that matter, but they are definitely worth considering at the right valuations.
Think about becoming an owner of AI and robotics companies while there is still time. I plan to buy some of the most obvious ones (including Google) in the ongoing market downturn (2016-2017).
Top 5 most obvious AI companies
- Alphabet (Google)
- Facebook (M, Deep Learning)
- IBM (Watson, neuromorphic chips)
- Apple (Siri)
- MSFT (skype RT lang, emo)
- Amazon (customer prediction; link to old article)
Yes, I’m US centric. So sue me 🙂
- SAP (BI)
- Oracle (BI)
- Nuance (HHMM, speech)
- Nippon Ceramic
- Pacific Industrial
Private companies (*I think):
- *Scaled Inference
- *Expect Labs
- *Nara Logics
- *Context Relevant
- *Rethink Robotics
- *Sentient Technologies
General AI areas to consider when searching for AI companies
- Self-driving cars
- Language processing
- Search agents
- Image processing
- Machine learning
- Oil and mineral exploration
- Pharmaceutical research
- Materials research
- Computer chips (neuromorphic, memristors)
- Energy, power utilities
Sunday 24th April 2016
AI Hits the Mainstream
Insurance, finance, manufacturing, oil and gas, auto manufacturing, health care: these may not be the industries that first spring to mind when you think of artificial intelligence. But as technology companies like Google and Baidu build labs and pioneer advances in the field, a broader group of industries are beginning to investigate how AI can work for them, too.
Today the industry selling AI software and services remains a small one. Dave Schubmehl, research director at IDC, calculates that sales for all companies selling cognitive software platforms —excluding companies like Google and Facebook, which do research for their own use—added up to $1 billion last year.
He predicts that by 2020 that number will exceed $10 billion. Other than a few large players like IBM and Palantir Technologies, AI remains a market of startups: 2,600 companies, by Bloomberg’s count.
General Electric is using AI to improve service on its highly engineered jet engines. By combining a form of AI called computer vision (originally developed to categorize movies and TV footage when GE owned NBC Universal) with CAD drawings and data from cameras and infrared detectors, GE has improved its detection of cracks and other problems in airplane engine blades.
The system eliminates errors common to traditional human reviews, such as a dip in detections on Fridays and Mondays, but also relies on human experts to confirm its alerts. The program then learns from that feedback, says Colin Parris, GE’s vice president of software research. – Nanette Byrnes
Sunday 24th April 2016
A $2 Billion Chip to Accelerate Artificial Intelligence
Two years ago we were talking to 100 companies interested in using deep learning. This year we’re supporting 3,500. In two years there has been 35X growth. – Jen-Hsun Huang, CEO of Nvidia
The field of artificial intelligence has experienced a striking spurt of progress in recent years, with software becoming much better at understanding images, speech, and new tasks such as how to play games. Now the company whose hardware has underpinned much of that progress has created a chip to keep it going.
Nvidia announced a new chip called the Tesla P100 that’s designed to put more power behind a technique called deep learning. This technique has produced recent major advances such as the Google software AlphaGo that defeated the world’s top Go player last month.
Deep learning involves passing data through large collections of crudely simulated neurons. The P100 could help deliver more breakthroughs by making it possible for computer scientists to feed more data to their artificial neural networks or to create larger collections of virtual neurons.
Artificial neural networks have been around for decades, but deep learning only became relevant in the last five years, after researchers figured out that chips originally designed to handle video-game graphics made the technique much more powerful. Graphics processors remain crucial for deep learning, but Nvidia CEO Jen-Hsun Huang says that it is now time to make chips customized for this use case.
At a company event in San Jose, he said, “For the first time we designed a [graphics-processing] architecture dedicated to accelerating AI and to accelerating deep learning.” Nvidia spent more than $2 billion on R&D to produce the new chip, said Huang.
It has a total of 15 billion transistors, roughly three times as many as Nvidia’s previous chips. Huang said an artificial neural network powered by the new chip could learn from incoming data 12 times as fast as was possible using Nvidia’s previous best chip.
Deep-learning researchers from Facebook, Microsoft, and other companies that Nvidia granted early access to the new chip said they expect it to accelerate their progress by allowing them to work with larger collections of neurons.
“I think we’re going to be able to go quite a bit larger than we have been able to in the past, like 30 times bigger,” said Bryan Catanzero, who works on deep learning at the Chinese search company Baidu. Increasing the size of neural networks has previously enabled major jumps in the smartness of software. For example, last year Microsoft managed to make software that beats humans at recognizing objects in photos by creating a much larger neural network.
Huang of Nvidia said that the new chip is already in production and that he expects cloud-computing companies to start using it this year. IBM, Dell, and HP are expected to sell it inside servers starting next year. – Tom Simonite