Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

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Companies have been trying to drum up enthusiasm for them for years, with little success. Pepper, a humanoid machine carrying the hopes of SoftBank Group Corp.'s billionaire founder Masayoshi Son, was supposed to change that. Promoted as the first robot to be endowed with emotions, the company marketed Pepper aggressively after it was unveiled in 2014, promising the gadget was sophisticated enough for tasks usually handled by shop clerks, receptionists and translators. "It's not there to have a conversation," said Junichi Nishi, a municipal official in Fujieda, Shizuoka Prefecture, a city of about 140,000. "We use it primarily as a tablet," he said, referring to the touch screen attached to the robot's chest.

Stock market cheats, A.I. will soon be looking for you. The sheer volume of transactions conducted in financial markets renders market surveillance by regulatory groups difficult, but machine learning and artificial intelligence tools will soon be employed to ferret out cheaters, according to Reuters. The Financial Industry Regulatory Authority (FINRA) is developing A.I. that it will start testing in 2017 along with its existing surveillance and detection mechanisms. NASDAQ and the London Stock Exchange Group intend to start using artificial intelligence to spot trade irregularities and violation patterns this year. Related: Future AI assistants like Siri could be trained by Joey from'Friends' Reuters reports that financial firms already use artificial intelligence for picking stocks and for monitoring their own firms' compliance.

Artificial intelligence is learning to take on an increasing number of sophisticated tasks. Google Deepmind's AI is now able to imitate human speech, and just this past August IBM's Watson successfully diagnosed a rare case of leukemia. Rather than viewing these advances as threats to job security, we can look at them as opportunities for AI to fill in critical gaps in existing service providers, such as mental healthcare professionals. In the US alone, nearly eight percent of the population suffers from depression (that's about one in every 13 American adults), and yet about 45 percent of this population does not seek professional care due to the costs. There are many barriers to getting quality mental healthcare, from searching for a provider who's within your insurance network to screening multiple potential therapists in order to find someone you feel comfortable speaking with.

Scientists have found why amputees feel the sensation of phantom limb pain. It is due to this reorganization pain occurs in the amputated limbs of a vast majority of amputees. The researchers also found a proposed way of treating the affliction through the use of artificial intelligence techniques. The researchers used a brain-machine interface to come up with this conclusion. They trained a group of ten amputees so they could control a robotic arm from their brain. The research team found learning to control the prosthetic through the amputated arm resulted in pain.

"The holy grail of a chatbot or virtual assistant is to help you adjust your behavior, telling you when to save more or spend in order to reach certain goals," said Lex Sokolin, global director of fintech research for Autonomous Research. Could UBS clients soon be serviced by voice-controlled AI? Maybe not yet, but a new pilot program between the bank and Amazon's Alexa service is testing the frontiers of both science fiction and wealth management. UBS' partnership with Amazon will enable clients and non-clients of the bank to get answers to financial and economic questions, ranging from what is inflation to how the U.S. economy is faring. It's the latest example of how wealth management firms are experimenting with new technologies such as data analytics and artificial intelligence to expand or reinvent the business.

Facebook recently announced a rather scenically named system, Big Sur, designed around Nvidia's Tesla compute cards aimed at helping their neural networks, and obviously machine learning, become faster and more versatile. They are, of course, not alone. IBM Watson has similar visions as does Microsoft. Big Data and analytics have long staked claim to the holiday shopping season, but I sense that this 2015 holiday shopping season the real big winner will be Machine Learning. The big gun in the Machine Learning camp is the aforementioned IBM Watson.