If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
It seems that every day we read about newer and better applications of artificial intelligence (AI) and machine learning (ML). Need a good movie to watch? Estimates now suggest that up to 47 percent of U.S. jobs may disappear in the coming decades due the rise of automation. But at the same time, "spillover" effects will fuel the creation of entirely new industries and job categories. These statistics reflect one of the greatest challenges--and opportunities--for teachers in our seemingly paradoxical modern economy.
The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age.
By thinking of atoms as letters and molecules as words an Artificial Intelligence (AI) from IBM is now using the same neural network techniques that other AI's use to translate between different languages to predict the outcomes of organic chemical reactions, and the breakthrough could help speed up the development of new drugs. Scientists have been trying to teach computers about chemistry for decades in the hope that one day they'll be able to help them discover and predict the outcomes of chemical reactions but organic chemicals can be extraordinarily complex, and past simulations of their behaviours have been at best time consuming and inaccurate. Now though the team at IBM, and their new AI have tried a different technique to solve this thorny problem. "Instead of translating English into German or Chinese, we had the same artificial intelligence technology look at hundreds of thousands or millions of chemical reactions and got it learn the basic structure of the'language' of organic chemistry, and then we had it try to predict the outcomes of possible organic chemical reactions," said the study's co-author Teodoro Laino from IBM Research's lab in Zurich. "We want to help chemists design new synthesis routes for organic compounds," he added.
Artificial intelligence has gotten pretty darn smart--at least, at certain tasks. AI has defeated world champions in chess, Go, and now poker. But can artificial intelligence actually think? The answer is complicated, largely because intelligence is complicated. One can be book-smart, street-smart, emotionally gifted, wise, rational, or experienced; it's rare and difficult to be intelligent in all of these ways.
The next time you tweet while on a Vancouver TransLink bus or train, Saeid Allahdadian might be taking notes about that post. This postdoctoral researcher at the University of British Columbia is using AI technologies and social media data to map major travel routes. The goal is to identify areas in need of better transit service. During three weeks this summer, Allahdadian analyzed 30,000 public geotagged tweets posted by 3,440 different individuals around Metro Vancouver and Surrey. The tweets were filtered based on if they were geotagged with a location, if they were publicly available, or if they mentioned @TransLink.
Vendor management can be a tricky task for companies with even a few dozen suppliers. As Mary Shacklett at TechRepublic writes, even a standard contract with an IT vendor explicitly lays out things such as pricing, service level agreements and terms of services -- then, someone has to oversee the performance of those contractual agreements. Now, scale that process up to thousands of vendors, which is the case for many language service providers that have large networks of freelance translators. As MateCat Product Manager Alessandro Cattelan told the audience at the 2017 Language Industry Summit recently, finding the right translator for a specific project (especially when it's an esoteric subject, especially when the client needs a quick turnaround) can feel like trying to find a needle in a haystack. Of course, there are clear ways to filter out translators who wouldn't fit a given project.
As a kid, I saw the 1968 version of Planet of the Apes. As a future primatologist, I was mesmerized. Years later I discovered an anecdote about its filming: At lunchtime, the people playing chimps and those playing gorillas ate in separate groups. It's been said, "There are two kinds of people in the world: those who divide the world into two kinds of people and those who don't." In reality, there's lots more of the former.
So you want to learn how to program a quantum computer. Now, there's a toolkit for that. Microsoft is releasing a free preview version of its Quantum Development Kit, which includes the Q# programming language, a quantum computing simulator and other resources for people who want to start writing applications for a quantum computer. The Q# programming language was built from the ground up specifically for quantum computing. The Quantum Development Kit, which Microsoft first announced at its Ignite conference in September, is designed for developers who are eager to learn how to program on quantum computers whether or not they are experts in the field of quantum physics.
Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.
Unconscious imprinting from having grown up a Gen-Xer during the original Star Wars years tells us that robots are futuristic. Except that is no longer so, and hasn't been for a while, now. One of the features that made robots from Star Wars so awe-inspiring when we were children was their ability to understand and express human sentiments and behavior. Think: Little R2D2 rolling away offended, or C-3P0's exaggerated politeness contributing to building bonds and empathy with viewers. Now that AI and robots and are "invading" our daily lives, especially for shopping and e-commerce purposes, we have selected three robots and related technologies that are starting to integrate meta-communication aspects, aimed at going beyond purely utilitarian language to make the communication more human in our daily lives as consumers.