You might have also heard about narrow, general and super artificial intelligence, or about machine learning, deep learning, reinforced learning, supervised and unsupervised learning, neural networks, Bayesian networks and a whole lot of other confusing terms. But then it gives a more understandable definition of machines that mimics cognitive functions such as problem solving and learning. General AI, also known as human-level AI or strong AI, is the type of Artificial Intelligence that can understand and reason its environment as a human would. According to University of Oxford scholar and AI expert Nick Bostrom, when AI becomes much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills, we've achieved Artificial Super Intelligence.
An excellent example of this is the company Frank, an AI based advertising firm for startups. Overall, while Wall Street recognizes Artificial Intelligence's potential impact in the creative world, it's safe to say when it comes to telling a story, that human touch will never go away. Perhaps one of the most underrated things about AI is its potential to eliminate practices altogether. While before B2B sales could rely on either targeted ads or sales teams to bring clients in, software like Leadcrunch's is eliminating those processes altogether.
If AI is being used to make decisions about who to hire or whether to extend a bank loan, people want to make sure the algorithm hasn't absorbed race or gender biases from the society that trained it. Singh is a coauthor of a frequently cited paper published last year that proposes a system for making machine-learning decisions more comprehensible to humans. In one example from the paper, an algorithm trained to distinguish forum posts about Christianity from those about atheism appears accurate at first blush, but LIME reveals that it's relying heavily on forum-specific features, like the names of "prolific posters." Developing explainable AI, as such systems are frequently called, is more than an academic exercise.
If memory works the way most neuroscientists think it does--by altering the strength of connections between neurons--storing all that information would be way too energy-intensive, especially if memories are encoded in Shannon information, high fidelity signals encoded in binary. That assumption leads some scientists--mind-body dualists--to argue that we won't learn much by studying the physical brain. Over time, our memories are physically encoded in our brains in spidery networks of neurons--software building new hardware, in a way. That's because the street lamp infrastructure in the two halves of the city remain different, to this day--West Berlin street lamps use bright white mercury bulbs and East Berlin uses tea-stained sodium vapor bulbs.
Machine learning and data analytics are on the rise, leaving some employees to fear that computers will take over their jobs, but that is not the case. In the past, the job of professionals was to gather data and information as if they were solving a puzzle, but that changes with today's data analytics and artificial intelligence in the workplace. Employees today aren't puzzle solvers who go out and gather information, but mystery solvers who must make sense of complex information that machines gather, Gladwell said. "In the future, we are not getting rid of human judgment," Gladwell said.
Tesla has completely shaken up its Autopilot team, and its newest addition is Andrej Karpathy, the new director of artificial intelligence and Autopilot vision. He received a pHd in machine learning and computer vision from Stanford University. Karpathy has mostly worked in academia, but he joined Tesla's artificial intelligence group OpenAI last September as a research scientist. As a Tesla exec, Karpathy said he will look to apply his work with convolutional nets to Autopilot.
Morgan Stanley's recent decision to partner 16,000 financial advisers with algorithms that can identify trades and prod brokers to reach out to clients is evidence of yet another in-road being made by machines into human roles. For these digital disruptors, their mastery of machine learning would make it relatively easy for them to enter finance -- arguably far more easily than financial advisers could enter the field of machine learning. Thus, for Wall Street's biggest brokerages such as Morgan Stanley, AI becomes a tool for wealth management. But as Morgan Stanley and other Wall Street firms embrace more AI, trust in wealth advisement is likely to become a triangulated relationship.
Deep Blue challenged world chess champion Garry Kasparov to a series of chess matches and Deep Blue won. Today's best reps use predictive analytics, a form of AI that optimizes decision making around sales efforts. AI-driven software can eliminate a great deal of manual work, helping sales reps make decisions about how to approach prospects, personalize conversations, and most importantly, focus on the leads that deserve follow-up. By sourcing and analyzing the data coming from different sales channels (emails, calls, social media), the AI algorithms can provide optimal personalized propositions for customers.
Nobel prize laureate Sir Christopher Pissarides's comments at a conference in Norway attracted fierce criticism. The gender and accent of Apple's voice assistant across iPhone, iPad, Mac and other Apple devices has historically been dependent on regional settings. "The comments made do reflect consistent results that people make social judgements about computer speech outputs, and those seem to relate to gender stereotypes that exist in the wider world," Dr Kate Hone, a computer science academic at Brunel University, told the BBC. Out of the 15 male and 17 female participants interviewed, the participants mainly preferred male voices because they found the voices to be more reassuring.
And it's becoming clear that delivery drones themselves will play an increasingly important role in collecting weather conditions on their journeys through the sky, relaying that information to computer weather models and perhaps back to fleets of drones following behind. Weather reports for drones will rely on multilayered systems of ground-based weather gauges, sensors on the drones themselves, and data from national weather services, all feeding computer models, said Marcus Johnson, a research aerospace engineer at the NASA Ames Research Center at Moffett Field, California. It just comes down to cost," said Tarleton, whose company makes the weather balloons the National Weather Service sets loose each day to compile the national forecast. SEE ALSO: The only company this activist investor isn't taking on is his dad's BNSF Railway Co.--the only company in the U.S. flying drones long distances, a project it's undertaken as part of an FAA study--has called back flights or kept them grounded because of the elements, said Todd Graetz, director of BNSF's drone program.