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) …
Tesla Powerwalls and Solar Roof, two of Elon Musk's innovative strategies to get consumers onto the solar grid, require waits of six months or longer. The company says customers are hungry, but it doesn't have the product yet. Tesla is cutting the price of the Model 3, as it aims to make its best-selling product more affordable, and is discontinuing versions of other vehicles. Tesla said on Monday that it's reducing the price of the Model 3 by $1,000 to $38,990. The company will no longer sell the standard range versions of the Model S and Model X, raising the minimum costs consumers will have to pay for those cars.
Elon Musk wants to link human brains with computers. The CEO of SpaceX and Tesla Motors is exploring just such a connection through another company he has launched, called Neuralink. Elon Musk has revealed that his stealth neurotechnology start-up is poised to begin human clinical trials soon on brain implants. Musk's Neuralink gave a presentation late Tuesday and released a white paper divulging details of its progress on implants that could eventually enable patients to overcome devastating injuries. The company, which has been pursuing the technology for years with Musk's financial backing and leadership, touted its initial results as promising for potentially treating conditions such as Alzheimer's, spinal injuries and blindness.
Some Uber drivers in New York City want to see a decrease in the commission taken by the company. SAN FRANCISCO -- Gig economy workers are increasingly ubiquitous, shuttling us to appointments and delivering our food while working for Uber, Lyft, DoorDash and others. Thanks in large part to the app-based tech boom emanating from this city, 36% of U.S. workers participate in the gig economy, according to Gallup. But not all gigs are created equal, Gallup adds, noting that so-called "contingent gig workers" experience their workplace "like regular employees do, just without the benefits of a traditional job -- benefits, pay and security." California lawmakers are weighing what is considered a pro-worker bill that, if passed into law, would set a national precedent that fundamentally redefines the relationship between worker and boss by forcing corporations to pay up.
We hear a lot about AI and its transformative potential. What that means for the future of humanity, however, is not altogether clear. Some futurists believe life will be improved, while others think it is under serious threat. Here's a range of takes from 11 experts. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.
Artificial intelligence (AI) is rapidly finding applications in nearly every walk of life. Self-driving cars, social media networks, cybersecurity companies, and everything in between uses it. But a new report published by the SHERPA consortium – an EU project studying the impact of AI on ethics and human rights – finds that while human attackers have access to machine learning techniques, they currently focus most of their efforts on manipulating existing AI systems for malicious purposes instead of creating new attacks that would use machine learning. The study's primary focus is on how malicious actors can abuse AI, machine learning, and smart information systems. The researchers identify a variety of potentially malicious uses for AI that are well within reach of today's attackers, including the creation of sophisticated disinformation and social engineering campaigns.
Categorical data are commonplace in many Data Science and Machine Learning problems but are usually more challenging to deal with than numerical data. In particular, many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms. One of the most common ways to make this transformation is to one-hot encode the categorical features, especially when there does not exist a natural ordering between the categories (e.g. a feature'City' with names of cities such as'London', 'Lisbon', 'Berlin', etc.). Even though this type of encoding is used very frequently, it can be frustrating to try to implement it using scikit-learn in Python, as there isn't currently a simple transformer to apply, especially if you want to use it as a step of your machine learning pipeline. In this post, I'm going to describe how you can still implement it using only scikit-learn and pandas (but with a bit of effort).
Since its invention by a Hungarian architect in 1974, the Rubik's Cube has furrowed the brows of many who have tried to solve it, but the 3-D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine. DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state--each of six sides displaying a solid color--which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.
CHICAGO -- Grant Thornton LLP is collaborating with Microsoft and Hitachi Solutions to turn information into foresight. The collaboration uses artificial intelligence (AI) and machine learning (ML) to help Grant Thornton identify its clients' nascent business needs. Grant Thornton can then design solutions to address its clients' challenges before they balloon. As one of the nation's largest accounting, tax and consulting firms, Grant Thornton works with clients to overcome all manner of hurdles, from financial and operational to technological and risk-related. "We focus on staying ahead of our clients' needs," explains Nichole Jordan, Grant Thornton's national managing partner of Markets, Clients and Industry.
TTEC Holdings, Inc. (NASDAQ: TTEC), a leading digital global customer experience (CX) technology and services company focused on the design, implementation and delivery of transformative customer experience, engagement and growth solutions, has recently been recognized by Chief Learning Officer magazine as a 2019 LearningElite Silver Award winner. This robust, peer-reviewed ranking and benchmarking program recognizes those organizations that employ exemplary workforce development strategies that deliver significant business results. Special emphasis was placed this year on how these learning teams are helping their organizations adapt to and prepare for change. Winners were recently announced during the ninth annual LearningElite Awards program at the CLO Symposium conference. "TTEC is honored to be recognized as an elite learning organization and appreciates this award from Chief Learning Officer," said Steve Pollema, Executive Vice President, TTEC Digital.
Artificial Intelligence (AI) represents a combination of various technologies including Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Speech Recognition, Context Aware Processing, Neural Network, and Predictive APIs. AI will be found in virtually everything, ranging from individual products and applications to wide spread systems and networks. Network infrastructure and computing equipment will rely upon AI algorithms for decision making while at the device level AI will be built into electronics at the chipset level. This report provides a multi-dimensional view into the AI market including analysis of embedded devices and components, embedded software, and AI platforms. This research also assesses the combined Artificial Intelligence (AI) marketplace including embedded IoT and non-IoT devices, embedded components (including AI chipsets), embedded software and AI platforms, and related services.