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) …
Thomson Reuters has a series, AI experts, where they interview thought leaders from different areas - including technology executives, researchers, robotics experts and policymakers - on what we might expect as we move towards AI. As part of that series I recently spoke to Paul Thies of Thomson Reuters, and here are the excerpts from the interview: Anticipating the next move in data science Thomson Reuters: For timely information concerning developments in data science, data mining and business analytics, KDnuggets is widely regarded as a leading outlet in the field. Created in 1993 by founder, editor and president Gregory Piatetsky-Shapiro, it is frequently cited as one of the top sources of data science news and influence by various industry watchers. Thomson Reuters: What are some use cases of data science that you find to be particularly valuable to organizations in this age of Big Data? GREGORY: Where people typically apply data science, probably not surprisingly, are in the areas of customer relationship management (CRM) and consumer analytics.
OpenAI today announced the launch of Spinning Up, a program designed to teach anyone deep reinforcement learning. OpenAI is well known for making funky-looking agents in virtual environments that learn how to walk on their own such as Humanoid v2 or POLO, a collaboration with University of Washington. Reinforcement learning involves providing reward signals to an agent in an environment incentivized to maximize its reward to meet a goal. RL has played a role in major AI breakthroughs such as Google DeepMind's AlphaGo and agents trained in environments like Dota 2. Spinning Up includes a collection of important reinforcement learning research papers, a glossary of terminology necessary to understand RL, and a collection of algorithms for running exercises. The program is being launched not just to help people learn how reinforcement learning works, but to make progress towards OpenAI's general goal of safely creating artificial general intelligence (AGI) by involving more people from fields beyond computer science.
Bin Liu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing, 210023 China Email: email@example.com Abstract This contribution presents a very brief and critical discussion on automated machine learning (AutoML), which is categorized here into two classes, referred to as narrow AutoML and generalized AutoML, respectively. The conclusions yielded from this discussion can be summarized as follows: (1) most existent research on AutoML belongs to the class of narrow AutoML; (2) advances in narrow AutoML are mainly motivated by commercial needs, while any possible benefit obtained is definitely at a cost of increase in computing burdens; (3)the concept of generalized AutoML has a strong tie in spirit with artificial general intelligence (AGI), also called "strong AI", for which obstacles abound for obtaining pivotal progresses. AutoML has recently emerged as a hot research topic in the field of machine learning (ML) and artificial intelligence (AI). As we know, a typical ML pipeline requires a lot of human's participation for e.g., data pre-processing, feature engineering, algorithm selection, model selection and hyperparameter optimization.
It was seven minutes to ten o'clock in the morning, and it was the only good thing that had happened." If you get the feeling that these sentences could have been better structured, it's simply because these seemingly disparate, literary threads have been stitched into a novel by an algorithm. That's also why the human author of this novel, Ross Godwin, calls himself'writer of writers'. He is an artist and creative technologist at Google, and also a former Obama administration ghostwriter. In March 2017, Godwin fitted a Cadillac car with a surveillance camera, global positioning system (GPS) unit, microphone and clock, and connected these devices to a portable artificial intelligence (AI) writing machine that fed on these input data in real-time.
WHEN SOPHIA THE ROBOT first switched on, the world couldn't get enough. It had a cheery personality, it joked with late-night hosts, it had facial expressions that echoed our own. Here it was, finally -- a robot plucked straight out of science fiction, the closest thing to true artificial intelligence that we had ever seen. There's no doubt that Sophia is an impressive piece of engineering. It didn't take much to convince people of Sophia's apparent humanity -- many of Futurism's own articles refer to the robot as "her."
Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.
"But there are also unknown unknowns – the ones we don't know we don't know." An artificial intelligence strategy is the corporate equivalent of your spleen: everyone has one, but not everyone understands quite what it will accomplish. There are bold plans afoot everywhere in the world of AI to be sure, but its reality is still distant from the vision of artificial general intelligence (AGI) – i.e., machines displaying intelligence equivalent to the natural intelligence of humans – of popular imagination. Investors in particular need a sober and realistic view of what's achievable in the field of machine learning-driven AI today, versus what promises nothing more than a waste of time and money. There are many business problems that map to the attributes above.
Court is now in session, and author Robert J. Sawyer makes the case for leveraging AI to improve ethics and fairness in civil society. With 23 novels under his belt, as well as scores of short stories, scripts, treatments and more, Hugo and Nebula Award-winning author Robert J. Sawyer is not shy about exploring the technological and cultural landscape of our future. Among the many works in his remarkable and widely regarded career, he authored the trilogy WWW (as in Wake, Watch and Wonder) in which a blind teenage girl uses advanced medical technology to augment her vision, only to discover a super-AI consciousness called Webmind that uses the Internet to grow. During the series, Sawyer investigates the possible consequences that such a super-AI could unleash upon society, and how humans might respond. For his perspective on how humanity might relate to future artificial intelligences and what shape those interactions may take, we asked Sawyer about the dynamics of judgment and control; he also shared his overall sentiment on AI development.
Artificial intelligence is one of the most compelling areas of computer science research. AI technologies have gone through periods of innovation and growth but never has AI research and development seemed as promising as it does now. This is due in part to amazing developments in machine learning, deep learning, and neural networks. Machine learning, a cutting-edge branch of artificial intelligence, is propelling the AI field further than ever before. While AI assistants like Siri, Cortana, and Bixby are useful, if not amusing, applications of AI, they lack the ability to learn, self-correct, and self-improve.
Artificial intelligence and machine learning are being adopted into the enterprise at a rapid clip and adoption is likely to surge in 2019. What comes next is the real business challenge: How will we manage technology that we likely don't understand? The issue is likely to bubble up in the year ahead. For now, most of us are lulled into thinking more algorithms are better and even assuming we can outsource critical thought to models. Why hurt our brains when we can trust Einstein, Watson, Alexa, Google Assistant, and other software tools to think for us?