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) …
In this Machine Learning tutorial, we'll build a video game with Unity, TensorFlow and Python. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. In this session, we'll build a little smartphone game, train a bot to play it using reinforcement learning, Python, and TensorFlow, and deploy it to a smartphone. We'll show you how easy it is to add ML-powered intelligence to video games or simulations, and how inference on smartphones is easier than it's ever been: modern, powerful tools like Unity's ML-Agents, Python, and TensorFlow make the complex easy. First, we'll spend 10 minutes of the session: Second, we'll spend 10 minutes of the session: Finally, we'll spend the last 10 minutes of the session: This is an engaging, fast-paced, and surprisingly in-depth exploration of how powerful modern game engines can be used for quick, relatively easy, but incredibly powerful state of the art machine learning and training, and how powerful inference on-device is, for mobile AI.
For all the progress researchers have made with machine learning in helping us doing things like crunch numbers, drive cars and detect cancer, we rarely think about how energy-intensive it is to maintain the massive data centers that make such work possible. Indeed, a 2017 study predicted that, by 2025, internet-connected devices would be using 20 percent of the world's electricity. The inefficiency of machine learning is partly a function of how such systems are created. Neural networks are typically developed by generating an initial model, tweaking a few parameters, trying it again, and then rinsing and repeating. But this approach means that significant time, energy and computing resources are spent on a project before anyone knows if it will actually work.
Although earlier AI-based models required human supervision and produced inconsistent results, reports STAT, this new deep learning approach was trained on a library of more than 2,000 chemical compounds with something known about their antibacterial potency, using those data to predict function based on structure. The platform identified molecules that looked quite different from existing antibiotics, overcoming the bias that human researchers exhibit when they search for potential anti-bacterial compounds that have structures similar to existing antibiotics, according to STAT.
Expansion of internet web services and recent advances in high-throughput technologies have made access to the significant biological datasets for the public easy, specifically for the scientific community. As a result, ways to process, analyze, and infer knowledge have drastically changed in recent years, whether it is clinical data, sequencing data, electronic health records, and medicine in general. Because of this, data science terminology like machine learning and artificial intelligence have become part of our daily use of vocabulary in one way or another. They have already revolutionized the way translational research is designed and executed, leading to discoveries across the globe for the betterment of human health.
Our smartwatches have gotten pretty good at being able to detect heart problems, or at least notify the wearer that something could be wrong. This is great because these are problems that might not have been picked up until it is too late, and we have heard multiple stories of how people have had their lives saved due to early detection. Now it looks like thanks to work done by University of Utah Health and VA Salt Lake City Health Care System scientists, they have developed an AI-powered wearable sensor that will also be able to detect heart failure in advance, which could also help prevent patients from having to visit a hospital. According to one of the study's authors, Josef Stehlik, "Being able to readily detect changes in the heart sufficiently early will allow physicians to initiate prompt interventions that could prevent rehospitalization and stave off worsening heart failure." How this works is that the sensor will transmit the data from it to a smartphone via Bluetooth.
Artificial intelligence (AI) is one of the hottest topics in today's headlines. It powers natural language recognition for voice-powered assistants like Siri and Alexa, beats world-class Google Go players, and enables hyper-targeted e-commerce and content recommendations across the web on industry-giant websites that include Target and Netflix. But recently, AI has begun actively expanding its footprint in the enterprise. Executives are trying to more fully comprehend what AI is and how they can use it to capitalize on business opportunities by gaining insight into the data they collect and engaging with customers more productively to hone their competitive edge. AI is the frontier of enterprise technology, but there remain many misperceptions about what it is and how it works.
More and more, it is becoming necessary to consider working collaboratively, not only for questions regarding skills or because of the very quick evolution of engineering skills, and devices in particular, but also because working alone in a studio or a laboratory will be less and less viable. Interlocution is also essential in artistic practice. After having developed most of my projects alone for a long time, I understand how sharing this experience and competences gives meaning to this activity. You have worked in Brazil for many years. Is it a good place for new media art?
The science of applied artificial intelligence doesn't get the same kinds of headlines as the pure research efforts of Google or Facebook or others. Mostly that's because what gets built by companies is obfuscated by those same companies, either for proprietary reasons or because the companies actually have nothing much to speak of. Last week, Babak Hodjat, who runs the machine learning operations of software giant Cognizant Technology Solutions, had something to show, so ZDNet traveled to the loft office near San Francisco's Embarcadero where Hodjat and a team of 18 staffers develop algorithms. The ostensible event was the publication, on the arXiv pre-print server, of a paper showing how Hodjat's style of machine learning could compete with the kind made famous by DeepMind's AlphaZero. Before digging into the paper, ZDNet accepted a challenge against the machine, a game of Flappy Bird.