In 2006, Fei-Fei Li started ruminating on an idea. Li, a newly-minted computer science professor at University of Illinois Urbana-Champaign, saw her colleagues across academia and the AI industry hammering away at the same concept: a better algorithm would make better decisions, regardless of the data. But she realized a limitation to this approach--the best algorithm wouldn't work well if the data it learned from didn't reflect the real world. Her solution: build a better dataset. "We decided we wanted to do something that was completely historically unprecedented," Li said, referring to a small team who would initially work with her.

The team used simulations in a complex world filled with obstacles, but the goal for the AI was simple: Make it as far as possible as fast as possible. The parkour course contained walls, cliffs, hurdles, and tilting floors. The "reward" for the AI drove the simulations to discover new ways to traverse the terrain, and none of the movements were provided programmatically -- this is all emergent behavior. For example, the AI tried many times to learn how to jump over a wall in search of a greater simulated reward. When it finally figured that out, the same movement was adapted by the AI to jump over all the walls.

Google's DeepMind is developing an AI capable of'imagination', enabling machines to see the consequences of their actions before they make them. In two new research papers, the British AI firm, which was acquired by Google in 2014, describes new developments for "imagination-based planning" to AI. Its attempt to create algorithms that simulate the distinctly human ability to construct a plan could eventually help to produce software and hardware capable of solving complex tasks more efficiently. DeepMind's previous research in this area has been incredibly successful, with its AlphaGo AI managing to beat a series of human champions at the notoriously tricky board game Go. However, AlphaGo relies on a clearly defined set of rules to provide likely outcomes, with relatively few factors to consider. "The real world is complex, rules are not so clearly defined and unpredictable problems often arise," explain the DeepMind researchers in a blog post.

Artificial intelligence is gaining momentum and is being increasingly used across industries, but more importantly, the lines between artificial and human intelligence are getting blurred. Google is blurring them even further by endowing artificial intelligence with imagination. Google owned AI lab and DeepMind is working on endowing AI with imagination which would open vast possibilities for the technology -- AI would be able to reason through decisions, make plans for the future, and even dream. AI isn't replacing humans just yet, and it might never be able to even, given the fact that it is based on algorithms. The only problem with Google AI is that it will use algorithms that can easily adapt to changing conditions, even those it hasn't been programmed for.

Gary Angel, Principal, Advisory Services, Advanced Analytics - Digital Analytics, EY, Digital Analytics Center of Excellence Considered one of the leading digital measurement experts in the world, Gary leads EY's Digital Analyt... With machine learning you just dump all your data into a fancy algorithm and everything gets sorted out. Analysts will tell you that they typically spend about 4/5s of a project munging data not analyzing it. And that percentage doesn't necessarily change when it comes to machine learning. To get big data and machine learning right it's critical to understand what type of analysis fits your data.

The rapid development of AI is like the birth of the universe right after the Big Bang: incredibly hot and expanding rapidly. AI is so dynamic that no one can predict what the field will be like in five years. Based on its current development, we believe that we will witness the standardization of models and frameworks and potentially the creation of a marketplace for AI models. Here's why: Machine learning (ML) models have become the core of most modern software because of their ability to adapt in numerous ways and their high efficiency, both in terms of product functions and implementation costs and the overall business itself. We can compare this stage of AI development with the early days of the internet, when everyone acknowledged its importance but didn't see its full potential.

To commemorate the silver jubilee of FICO's use of artificial intelligence and machine learning, we asked FICO employees a question: What does the future of AI look like? The post below is one of the thought-provoking responses, from Chahm An, a lead analytic scientist at FICO, working in San Diego. Artificial intelligence, like human intelligence, uses observations from the past to learn a general model that can be used to make predictions about future similar occurrences. The future I see for AI is based on current work being done in this field, and grounded in what I saw 20 years ago, when I first began to study AI. In 1997, and IBM's Deep Blue had just defeated reigning world champion Garry Kasparov at the game of chess in what was seen as a landmark event of artificial intelligence surpassing a human champion at an intellectual challenge.

Machine learning and deep learning are showing a sharp growth trajectory in many industries. Even the semiconductor industry, which generally has resisted this technology, is starting to changing its tune. Both machine learning (ML) and deep learning (DL) have been successfully used for image recognition in autonomous driving, speech recognition in natural language processing applications, and for multiple uses in the health care industry. The general consensus is that it can be similarly applied to semiconductor design. "We called it'metrics' in 1998-1999," said Andrew Kahng, a professor of computer science and engineering at UC San Diego. "The main principle was measure everything, data-mine the log files, predict tool sweet spots and failures, and figure out how to tune specific tool options for a specific design instance."

When you first heard of the term "convolutional neural networks," you may have thought of something related to neuroscience or biology. And you would be right.

Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. The essence of the innovation was a new algorithm for training deep networks to run efficiently on a neuromorphic architecture, such as TrueNorth, by using 1-bit neural spikes, low-precision synapses, and constrained block-wise connectivity--a task that was previously thought to be difficult, if not, impossible. "The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy," said IBM Fellow Dharmendra Modha, chief scientist, Brain-inspired Computing, IBM Research. Today, the TrueNorth development ecosystem includes not only the TrueNorth brain-inspired processor, the novel algorithm for training deep networks and the scaled-up NS16e System but also a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, a teaching curriculum, single-chip boards, and scaled-out systems.