Microsoft believes that lessons from a liberal arts education are necessary for the proper development of AI. In 2011, Microsoft cofounder Bill Gates told a panel of American governors that a liberal arts education would hold back college graduates in the modern economy. A few days later, late Apple cofounder Steve Jobs declared that "it's technology married with liberal arts, married with the humanities, that yields us the result that makes our heart sing." Seven years later, Gates' company is siding with Jobs. Microsoft president Brad Smith and EVP of AI and research Harry Shum wrote in their new book "The Future Computed" that "one of the most important conclusions" of Microsoft's recent research into artificial intelligence is that lessons from liberal arts will be critical to unleashing the full potential of AI.
Electropherograms are produced in great numbers in forensic DNA laboratories as part of everyday criminal casework. Before the results of these electropherograms can be used they must be scrutinised by analysts to determine what the identified data tells us about the underlying DNA sequences and what is purely an artefact of the DNA profiling process. A technique that lends itself well to such a task of classification in the face of vast amounts of data is the use of artificial neural networks. These networks, inspired by the workings of the human brain, have been increasingly successful in analysing large datasets, performing medical diagnoses, identifying handwriting, playing games, or recognising images. In this work we demonstrate the use of an artificial neural network which we train to'read' electropherograms and show that it can generalise to unseen profiles.
About 4,000 people listened to Cuban as he kicked off his shoes--literally--and explained how AI will change the game for companies, educators, and future developments. He's also keeping his eyes peeled for smaller companies in machine learning and AI, and already has at least three companies in his investment portfolio. "[Software writing] skill sets won't be nearly as valuable as being able to take a liberal arts education … and applying those [skills] in assisting and developing networks." But in order for the country to advance to that future, AI and robotics need to become core competencies in the U.S., and not just in the business world, Cuban said.
An estimated 4,000 people listened to Cuban as he kicked off his shoes and explained how AI will change the game for companies, educators, and future developments. He's also keeping his eyes peeled for smaller companies in machine learning and AI, and already has at least three companies in his investment portfolio. "[Software writing] skill sets won't be nearly as valuable as being able to take a liberal arts education … and applying those [skills] in assisting and developing networks." But in order for the country to advance to that future, AI and robotics need to become core competencies in the U.S., and not just in the business world, Cuban said.
Hundreds of Argentine kids like Kaori who were born without limbs are now able to write, play sports and make music thanks to low-cost prosthetic hands devised by Gino Tubaro, a 21-year-old inventor whose work was praised by President Barack Obama during a visit to Argentina last year. In this June 12, 2017 photo, Kaori Misue attends art class in Buenos Aires, Argentina. In this May 30, 2017 photo, Gino Tubaro, right, fits a prosthetic arm on Juan Pablo Pelaez in Buenos Aires, Argentina. In this May 30, 2017 photo, Juan Pablo Pelaez stands in Gino Tubaro's workshop, as he waits for a 3D printer to finish a piece for his prosthetic arm, in Buenos Aires, Argentina.
We live in a world that's increasingly being shaped by complex algorithms and interactive artificial intelligence assistants who help us plot out our days and get from point A to point B. According to a new Princeton study, though, the engineers responsible for teaching these AI programs things about humans are also teaching them how to be racist, sexist assholes. The study, published in today's edition of Science magazine by Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, focuses on machine learning, the process by which AI programs begin to think by making associations based on patterns observed in mass quantities of data. In a completely neutral vacuum, this would mean that AI would learn to provide responses based solely on objective, data-driven facts. But because the data sets fed to the AI are selected and influenced by humans, there's a degree to which certain biases become a part of the AI's diet. To demonstrate this, Caliskan and her team created a modified version of an Implicit Association Test, an exercise that tasks participants to quickly associate concrete ideas like people of color and women with abstract concepts like goodness and evil.
Andrew Ng, one of the world's leading artificial intelligence researchers, said in a Medium post that he is resigning as the head of AI initiatives at Baidu Corp., one of China's largest Internet companies. Ng said he said he will "continue to shepherd" the growth of AI in society, but provided few clues about what might come next. He portrayed his departure from Baidu as amicable, saying: "the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish." Ng has held a multitude of high-profile positions in Silicon Valley in the past decade, serving as a computer science professor at Stanford University, as head of the Google Brain project, and as chairman of Coursera, an online-education company that he co-founded with Stanford faculty colleague Daphne Koller. I've completed a new book called "You Can Do Anything: The Surprising Power of a Useless Liberal Arts Education."
Computers have been defeating their human counterparts for some time. In 1997, Garry Kasparov was defeated by IBM supercomputer Deep Blue in a chess match. This was the first time that artificial intelligence had defeated a reigning world champion. Likewise in 2007, scientists created a computer program called Chinook that cannot be beat at checkers. It evaluates every possible move and even if its human opponent plays flawlessly, the best they can hope to achieve is a draw.
We describe a variety of projects developed as part of a course in Artificial Intelligence at the University of Minnesota. The projects cover navigation of small mobile robots and learning to accomplish simple tasks, and require a variety of approaches from neural networks to genetic programming to reactive behaviors. The projects have all been implemented on real robots. We discuss how the combination of robotics with Artificial Intelligence adds value to the learning of AI concepts and how the fun of building and programming a robot is a highly motivating force for the learning process. 1 Introduction The major goal of this paper is to describe examples of integration of real robotics projects in a course in Artificial Intelligence. The examples presented here are some of the class projects done by students taking a course in Artificial Intelligence at the University of Minnesota. The course is intended for senior undergraduate and first year graduate students. The textbook we use i...