Artificial intelligence has been a topic of debate for decades. With the advent of deep learning and neural networks, AI is now able to perform tasks that were previously performed by humans. This has led to many experts believing that the rise of artificial intelligence will lead to the destruction of human civilization as we know it today. The intelligent and intellectuals, find it increasingly difficult to differentiate real intelligence from the mediocre and AI generated output. This is a challenge that we all face as our lives become more intertwined with artificial intelligence. How do you know who your friends are?

Regular readers may have noticed that DSC Weekly didn't come out last week. The reason was personal – a close relative of mine had a series of strokes over the last couple of weeks, and I needed to take some time away to deal with the consequences. In addition, we migrated over to a new newsletter platform during this same period, which came with its own set of problems that we were eventually able to worth through. From the standpoint of someone working in the field of artificial cognition, strokes are fascinating. As someone watching a beloved relative deal with the aftermath of one, they are terrifying.

With the rise of zero-dollar commissions at custodians and low cost robo-advisors, the financial industry--and Financial Advisors--are at a competitive disadvantage to artificial intelligence. By 2030, an estimated 80% of heritage financial services companies will go out of business, struggle for relevance, fail to use technology to change their business model, or become commoditized. Real Intelligence LLC is warning Financial Planners of this potential outcome plagued by technology enhancements, like robo-advisors. Real Intelligence, however, is executing a real-time solution that provides a patented dynamic mapping system in conjunction with human effort. "Thirty percent of advisors will exit the business in three to five years," predicts Jeff Mount, president of Real Intelligence.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of artificial intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and causality of the models. Pearl also went on record to say that deep learning was stuck with curve fitting and called it "sacrilege". From the point of view of the mathematical hierarchy, Pearl said that no matter how well the data is manipulated, it's still a curve-fitting exercise. This a very controversial accusation coming from Pearl, who was awarded the ACM Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of Artificial Intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and Causality of the models. Pearl also went on record to say that Deep learning was stuck with curve fitting and called it "sacrilege". From the point of view of the mathematical hierarchy, Pearl said that no matter how well the data is manipulated, it's still a curve-fitting exercise. This a very controversial accusation coming from Pearl, who was awarded the ACM Turing Award for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning.

Last year, the machine learning community was thrown into disarray when its top minds Yann LeCun, Ali Rahimi and Judea Pearl had a faceoff on the state of artificial intelligence and machine learning. While Rahimi and Pearl tried to tone down the hype around AI, LeCun was aghast over the scepticism around intelligence and causality of the models. I see dozens of "Data Science Institutes" erected across the country, I read their manifestos and I check their advisory boards. Causality does not seem to be on their agenda. Which makes one doubt whether the Ladder has been internalized and where this hype will end.

Siri, Alexa, Google Home--technology that parses language is increasingly finding its way into everyday life. Boris Katz, a principal research scientist at MIT, isn't that impressed. Over the past 40 years, Katz has made key contributions to the linguistic abilities of machines. In the 1980s, he developed START, a system capable of responding to naturally phrased queries. The ideas used in START helped IBM's Watson win on Jeopardy!

For anyone worrying about machines taking over the world, I have reassuring news: The idea of artificial intelligence has been overcome by hype. I don't mean to belittle AI's promise or even its existing capabilities. The technology allows organizations to put data to use in ways we could only imagine not that long ago. It's revolutionized the way executives approach strategic planning. But very often lately--when I'm in meetings, reading research papers or listening to an expert's presentation--I can't shake the feeling that to many people, terms like "AI," "machine learning" and "cognitive computing" have become answers unto themselves.

When Elon Musk co-founded OpenAI its goal was to determine how AI technologies could best serve humanity. According to a new company charter, its mission going forward will be developing "highly autonomous systems that outperform humans at most economically valuable work." It wants to make machines smarter than people. It's called artificial general intelligence (AGI) and, depending on who you ask, it's either the Holy Grail or Pandora's Box when it comes to machine learning. Despite the fact that Musk recently distanced himself from the company -- stating Tesla's development of AI presented a conflict of interests for him – it still has his sense of ambition.

The original vision of AI some 60 years ago was to build machines that can think, learn, and reason like humans. Initial optimism of achieving this in just a few years was grossly misplaced, and indeed continued to haunt AI for decades. As researchers failed to get anywhere near the flexibility and general cognitive ability of humans they turned their focus to solving very specific, narrow problems of'intelligence' -- And to this day'AI' is practiced almost entirely this way. A recent breakthrough in artificial intelligence called deep learning (DL) has been hailed as a major breakthrough-- it changes the development dynamic from having to'program' computers to'teaching' them. This characterizing has some merit, but glosses over the significant human expertise and'tweaking' required to make these systems work.