ORLANDO – From Watson to Siri, Alexa to Cortana, consumers and patients have become much more familiar with artificial intelligence and natural language processing in recent years. Pick your terminology: machine learning, cognitive computing, neural networks/deep learning. All are becoming more commonplace – in our smartphones, in our kitchens – and as they continue to evolve at a rapid pace, expectations are high for how they'll impact healthcare. As it sparks equal part doubt and hope (and not a little hype) from patients, physicians and technologists, a panel of IT experts at HIMSS17 discussed the future of AI in healthcare on Sunday afternoon. Kenneth Kleinberg, managing director at The Advisory Board Company, spoke with execs from two medical AI startups: Cory Kidd, CEO of Catalia Health, and Jay Parkinson, MD, founder and CMO of Sherpaa.

Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene (e.g., an image from a camera) and build a 3D model of the objects in it and their relationships… Today's paper choice is a big step in that direction, learning the 3D structure of objects from 2D observations. The 2D projection of a scene is a complex function of the attributes and positions of the camera, lights and objects that make up the scene. If endowed with 3D understanding agents can abstract away from this complexity to form stable disentangled representations, e.g., recognizing that a chair is a chair whether seen from above or from the side, under different lighting conditions, or under partial occlusion. Moreover, such representations would allow agents to determine downstream properties of these elements more easily and with less training, e.g., enabling intuitive physical reasoning… The approach described is this paper uses an unsupervised deep learning end-to-end model and "demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner."

While we mostly hear about Artificial Intelligence systems like IBM's Watson, which won Jeopardy! Over the last five years or so, Machine Learning, a type of AI, has been a quickly rising tide that's now starting to permeate nearly every corner of technology. From self-driving cars to online advertising, cybersecurity, and video surveillance, companies are training computers to do many of the things human workers have been doing but better, or at least cheaper. Neural networks, computer systems that aim to simulate the way neurons are interconnected in the human brain, are trained to do these tasks the same way babies learn about the world – by observation, repetition, trial, and error, assisted instead of parents by computer scientists – although babies are still much, much better at it. A neural net learns to understand spoken language, for example, by listening to a lot of recorded speech, such as movie dialogue; it learns to identify objects by looking at tons of images.

Artificial intelligence, machine learning, and neural networks-based deep learning are concepts that have recently come to dominate venture capital funding, startup formation, promotion and exits, and policy discussions. The highly-publicized triumphs over humans in Go and Poker, rapid progress in speech recognition, image identification, and language translation, and the proliferation of talking and texting virtual assistants and chatbots, have helped inflate the market cap of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these companies dominate the headlines--and the war for the relevant talent--other companies that have been analyzing data or providing tools for analysis for years are also capitalizing on recent AI advances. A case in point are Equifax and SAS: The former developing deep learning tools to improve credit scoring and the latter adding new deep learning functionality to its data mining tools and offering a deep learning API. Neural network created in SAS Visual Data Mining and Machine Learning 8.1 Both companies have a lot of experience in what they do.

Scientists know that the first signs of autism can appear in early childhood, but reliably predicting that at very young ages is difficult. A behavior questionnaire is a crapshoot at 12 months. However, artificial intelligence might just be the key to making an accurate call. University of North Carolina researchers have developed a deep learning algorithm that can predict autism in babies with a relatively high 81 percent accuracy and 88 percent sensitivity. The team trained the algorithm to recognize early hints of autism by feeding it brain scans and asking it to watch for three common factors: the brain's surface area, its volume and the child's gender (as boys are more likely to have autism).

Artificial Intelligence and Machine Learning will be eating the world. Don't take my word for it -- in the roundup of venture capital predictions for 2017, I found it to be the top recurring theme. Since AI and ML startups cut across verticals (analytics, fintech, health, adtech, security, etc), it's easier to group them under the "machine intelligence" umbrella, coined by Shivon Zillis, a partner at Bloomberg Beta. In 2016 alone, 300 "machine intelligence" (AI ML) startups in Europe raised over €1.4 Billion in VC funding. While the terms AI and ML get thrown around readily, the companies that truly apply Artificial Intelligence, Machine Learning, Computer Vision and Deep Learning have the potential to address problems that were unsolvable before.

Potentially describing how general artificial intelligence will look like. Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck. Transfer Learning is the ability of an AI to learn from different tasks and apply its pre-learned knowledge to a completely new task. It is implicit that with this precedent knowledge, the AI will perform better and train faster than de novo neural networks on the new task. DeepMind is on the path of solving this with PathNet.

In a bid to boost its prospects in the world of artificial intelligence (AI), Apple has acquired Israel-based startup RealFace that develops deep learning-based face authentication technology, media reported on Monday. Reported by Calcalist, the acquisition is to be worth roughly $2 million (roughly Rs. 13.39 crores). A Times of Israel report cites Startup Nation Central to note RealFace had raised $1 million in funding thus far, employed about 10 people, and had sales operations China, Europe, Israel, and the US. Set up in 2014 by Adi Eckhouse Barzilai and Aviv Mader, RealFace has developed a facial recognition software that offers users a smart biometric login, aiming to make passwords redundant when accessing mobile devices or PCs. The firm's first app - Pickeez - selects the best photos from the user's album.

Will artificial intelligence get more aggressive and selfish the more intelligent it becomes? A new report out of Google's DeepMind AI division suggests this is possible based on the outcome of millions of video game sessions it monitored. The results of the two games indicate that as artificial intelligence becomes more complex, it is more likely to take extreme measures to ensure victory, including sabotage and greed. The first game, Gathering, is a simple one that involves gathering digital fruit. Two DeepMind AI agents were pitted against each other after being trained in the ways of deep reinforcement learning.

An Artificial Intelligence (AI) program developed by Google has demonstrated human-like aggression during simulations. Google's Deep Mind AI was tested playing several different games to see what kind of behavior would emerge. In one particular game, the AI was tasked with attempting to collect more'apples' within a 2D environment than another'player'. The AI also had the ability to hit a player with a beam which would remove them from the game. After running the game through millions of simulations, Deep Mind's'deep reinforcing learning' algorithm showed human-like emergent behavior.