Researchers at DeepMind propose a new technique that automatically discovers a reinforcement learning algorithm from scratch.
Large-scale oceanic phenomena are complicated and often involve many natural processes. Tropical instability wave (TIW) is one of these phenomena.
Pacific TIW, a prominent prevailing oceanic event in the eastern equatorial Pacific Ocean, is featured with cusp-shaped waves propagating westward at both flanks of the tropical Pacific cold tongue.
The forecast of TIW has long been dependent on physical equation-based numerical models or statistical models. However, many natural processes need to be considered for understanding such complicated phenomena.
An AI algorithm is capable of automatically generating realistic-looking images from bits of pixels.
Why it matters: The achievement is the latest evidence that AI is increasingly able to learn from and copy the real world in ways that may eventually allow algorithms to create fictional images that are indistinguishable from reality.
What’s new: In a paper presented at this week’s International Conference on Machine Learning, researchers from OpenAI showed they could train the organization’s GPT-2 algorithm on images.
The latest AI algorithms are probing the evolution of galaxies, calculating quantum wave functions, discovering new chemical compounds and more. Is there anything that scientists do that can’t be automated?
The snake bites its tail
Google AI can independently discover AI methods.
Then optimizes them
The high energy consumption of artificial neural networks’ learning activities is one of the biggest hurdles for the broad use of Artificial Intelligence (AI), especially in mobile applications. One approach to solving this problem can be gleaned from knowledge about the human brain.
Although it has the computing power of a supercomputer, it only needs 20 watts, which is only a millionth of the energy of a supercomputer.
One of the reasons for this is the efficient transfer of information between neurons in the brain. Neurons send short electrical impulses (spikes) to other neurons—but, to save energy, only as often as absolutely necessary.
In February of last year, the San Francisco–based research lab OpenAI announced that its AI system could now write convincing passages of English. Feed the beginning of a sentence or paragraph into GPT-2, as it was called, and it could continue the thought for as long as an essay with almost human-like coherence.
Now, the lab is exploring what would happen if the same algorithm were instead fed part of an image. The results, which were given an honorable mention for best paper at this week’s International Conference on Machine Learning, open up a new avenue for image generation, ripe with opportunity and consequences.
How do you beat Tesla, Google, Uber and the entire multi-trillion dollar automotive industry with massive brands like Toyota, General Motors, and Volkswagen to a full self-driving car? Just maybe, by finding a way to train your AI systems that is 100,000 times cheaper.
It’s called Deep Teaching.
Perhaps not surprisingly, it works by taking human effort out of the equation.
No industry will be spared.
The pharmaceutical business is perhaps the only industry on the planet, where to get the product from idea to market the company needs to spend about a decade, several billion dollars, and there is about 90% chance of failure. It is very different from the IT business, where only the paranoid survive but a business where executives need to plan decades ahead and execute. So when the revolution in artificial intelligence fueled by credible advances in deep learning hit in 2013–2014, the pharmaceutical industry executives got interested but did not immediately jump on the bandwagon. Many pharmaceutical companies started investing heavily in internal data science R&D but without a coordinated strategy it looked more like re-branding exercise with the many heads of data science, digital, and AI in one organization and often in one department. And while some of the pharmaceutical companies invested in AI startups no sizable acquisitions were made to date. Most discussions with AI startups started with “show me a clinical asset in Phase III where you identified a target and generated a molecule using AI?” or “how are you different from a myriad of other AI startups?” often coming from the newly-minted heads of data science strategy who, in theory, need to know the market.
However, some of the pharmaceutical companies managed to demonstrate very impressive results in the individual segments of drug discovery and development. For example, around 2018 AstraZeneca started publishing in generative chemistry and by 2019 published several impressive papers that were noticed by the community. Several other pharmaceutical companies demonstrated impressive internal modules and Eli Lilly built an impressive AI-powered robotics lab in cooperation with a startup.
The 400 kilogram wheeled system moves about the lab guided by LIDAR laser scanners and has an industrial robotic arm made by German firm Kuka that it uses to carry out tasks like weighing out solids, dispensing liquids, removing air from the vessel, and interacting with other pieces of equipment.
In a paper in Nature, the team describes how they put the device to work trying to find catalysts that speed up reactions that use light to split water into hydrogen and oxygen. To do this, the robot used a search algorithm to decide how to combine a variety of different chemicals and updated its plans based on the results of previous experiments.
