Guavus is at the forefront of AI-based big data analytics and machine learning innovation, driving digital transformation at 6 of the 7 world's largest …

At IBM Research's recent "The Path to More Flexible AI" virtual roundtable, a panel of MIT and IBM experts discussed some of the biggest obstacles they face in developing artificial intelligence that can perform optimally in real-world situations. The solution, they agreed during the July 8 panel, is to embrace an integrated AI paradigm that amplifies the strengths and compensates for the weaknesses found in different approaches, including symbolic programming and deep learning. AI and automation are largely synonymous when you talk about industrial uses, said panelist David Cox, IBM Director of the MIT-IBM Watson AI Lab. "A lot of what people mean when they talk about AI today is automation," he added. "But automation is incredibly labor-intensive today, in a way that really just doesn't work for the problems we want to solve."

To improve how sporting events are covered in the news, a new AI 3D pose estimation model was recently developed by a group of researchers from The New York Times R&D group, and wrnch, an AI-computer vision company and member of the NVIDIA Inception program. The 3D pose estimation model can help extract data the human eye can easily miss and help journalists tell a story with more concrete data. "Traditional motion capture techniques require an athlete to wear physical markers. But this isn't possible during live sporting events. Instead, we built a solution that uses our photographers' cameras, machine learning and computer vision to capture this data as an event unfolds," the researchers stated in their article, Estimating 3D Poses of Athletes at Live Sporting Events.

Are voice assistants such as Amazon's Alexa, Apple's Siri, and Google Assistant stifling competition when they only let you stream music from a single service, or when then send you to a specific shopping site by default? It's a fair question, and one that European Union regulators are looking to answer as part of a "sweeping" antitrust probe, Bloomberg reports. The European Commission announced the massive probe on Thursday, with EU Competition Commissioner Margrethe Vestager warning of a "serious risk" that Alexa, Siri, Google Assistant, and similar voice assistants could be helping "big companies" such as Amazon, Apple, and Google "push markets beyond the tipping point where competition turns into monopoly." EU regulators, who are slated to survey more than 400 firms as part of the probe, will be focussing on how users of Alexa, Google Assistant, and other voice assistants may often "be presented with an option" rather than a full slate of competitive choices, according to Bloomberg. If a particular voice assistant tends to favor its own products and services over those of a competitor, that "might lead to the fast emergence of dominant digital ecosystems and gatekeepers and might present tipping risks," Bloomberg continues.

Banks around the world were already under pressure to fully deploy Artificial Intelligence prior to the pandemic.

Artificial Intelligence (AI) in Drug Discovery Market Research report is a professional and in-depth study on the market size, growth, share, trends, as well …

PALM BEACH, Florida, July 16, 2020 /PRNewswire/ — The global Artificial Intelligence (AI) -based Drone Software market size is expected to continue …

No other topic has dominated industry conversation in recent years like AI. But what exactly does it mean when we speak of AI? Artificial intelligence is the generic term for a machine simulation of human cognitive abilities. Machine Learning, in turn, describes a series of mathematical methods that can identify certain patterns in data from learned examples. Deep Learning is a subset of machine learning and uses artificial neural networks that enable the system to learn autonomously. Deep Learning enables the processing of amounts of data that is not practical to process manually.

Fake news is a major concern in our society right now. It has gone hand-in-hand with the rise of the data-driven era – not a coincidence when you consider the sheer volume of data we are generating every second! Fake news is such a widespread issue that even the world's leading dictionaries are trying to combat it in their own way. So what role has Machine Learning played in this? I'm sure you must have heard about a machine learning technique that generates fake videos mimicking famous personalities. Similarly, Natural Language Processing (NLP) techniques are being used to generate fake articles – a concept called "Neural Fake News". I've been working in the Natural Language Processing (NLP) space for the last few years and while I love the pace at which breakthroughs are happening, I'm also deeply concerned about the way these NLP frameworks are being used to create and spread false information.

Getting a good job in the field of Machine Learning is getting very competitive. The best way to showcase your Machine Learning skills is in the form of Portfolio of Data Science and Machine Learning Projects. A good Portfolio of Projects will show that you can apply those Machine Learning skills in your work. Here are 10 Machine Learning Projects which will boost your Portfolio and will help you to get a job as a Data Scientist. Human activity recognition is the problem of classifying sequences of data recorded by specialized harnesses or smartphones into known well-defined Human activities.