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) …
Using machine learning to analyze blockchain datasets is a fascinating challenge. Beyond the incredible potential of uncovering unknown insights that help us understand the behavior of crypto-assets, blockchain datasets presents very unique challenges to a machine learning practitioner. Many of these challenges translate into major roadblocks for most traditional machine learning techniques. However, the rapid evolution of machine intelligence technologies has enabled the creation of novel machine learning methods that result very applicable to the analysis of blockchain datasets. At IntoTheBlock, we regularly experiment with these new methods to improve the efficiency of our market intelligence signals.
Sometimes, starting from scratch is the best way forward. Nearly a year into his PhD studies, Jeff found himself stuck. He had been using machine-learning algorithms to study how cancers evolve, but he found that existing algorithms were failing as the experimental data became more complex. He had set out to map how individual cancers change over time, such as between diagnosis and relapse, or between the patient's primary tumour and when it spreads. If successful, his algorithms could help us learn which cellular changes are driving cancers, which changes allow cancer cells to survive through therapy, and which cancer cells we should focus on treating.
"Our research team at Qwant works at the cutting edge of AI to quickly deliver the best possible results on our users search queries while ensuring the results are neutral, impartial and accurate. We see millions of searches each day for images alone. One of the latest AI innovations that we are implementing is a new class of image recognition model called ResNext, to improve our accuracy and speed when delivering image search results. We have been working closely with Microsoft and Graphcore to use IPU processor technology in Azure and are seeing a significant improvement – with 3.5x higher performance - in our image search capability using ResNext on IPUs, out of the box. There is huge potential for innovation with Graphcore IPUs on new machine intelligence models and we are working on these approaches to refine our search results so that we can deliver exactly what our customers are looking for."
MIT researchers have developed a bot equipped with artificial intelligence that can beat human players in tricky online multiplayer games where player roles and motives are kept secret. Many gaming bots have been built to keep up with human players. Earlier this year, a team from Carnegie Mellon University developed the world's first bot that can beat professionals in multiplayer poker. DeepMind's AlphaGo made headlines in 2016 for besting a professional Go player. Several bots have also been built to beat professional chess players or join forces in cooperative games such as online capture the flag.
AI technology has become increasingly sophisticated in recent years. So many products and services now rely on the technology to provide automation and intelligence that it is deeply and irrevocably intertwined with our everyday world. Whether through devices we use to enable convenience at home or in the way products we use all the time are manufactured, its impact is everywhere, driving innovation in just about every aspect of our lives. But there are missing pieces to this puzzle that still cause frustration for end-users and present significant challenges for researchers trying to improve how AI technology performs. A common sense approach Before his passing in 2018, Microsoft co-founder Paul Allen dedicated an admirable amount of time and resources to solving an essential challenge that seems to come up again and again: The fundamental lack of common sense in AI technologies.
Researchers from Alphabet-owned company DeepMind say a new AI can ingest a patient's medical history and predict, with 90 percent accuracy, whether they're going to need dialysis for acute kidney injury 48 hours before it occurs. "Currently we pick these things up too late and harm is caused to patients, and we think there's a real opportunity for these AI systems to be able to predict and prevent rather than just what currently happens, which is clinicians almost firefighting and running around problems that have already developed," DeepMind clinical lead Dominic King told Wired. The team fed health data from more than 700,000 Veterans Affairs hospital patients across the U.S. to their neural network. Their results were promising, according to a paper about the research published Wednesday in the journal Nature: the system can even tell doctors what piece of medical data tipped it off that a kidney crisis was imminent. But while the system is speedy, it's way too trigger-happy: it reported two false positives for every correctly identified kidney injury.
That would improve health alerts for people at heightened risk of developing problems because of high ozone levels. Yunsoo Choi, associate professor in the Department of Earth and Atmospheric Sciences and corresponding author for a paper explaining the work, said they built an artificially intelligent model using a convolutional neural network, which is able to take information from current conditions and accurately predict ozone levels for the next day. The work was published in the journal Neural Networks. "If we know the conditions of today, we can predict the conditions of tomorrow," Choi said. Ozone is an unstable gas, formed by a chemical reaction when sunlight combines with nitrogen oxides (NOx) and volatile organic compounds, both of which are found in automobile and industrial emissions.
Cirta is a new machine-learning challenge for high-energy physics on Zindi, the Africa-based data-science challenge platform. Launched this autumn at the International Conference on High Energy and Astroparticle Physics (TIC-HEAP), Constantine, Algeria, Cirta challenges participants to provide machine-learning solutions for identifying particles in LHC experiment data. Cirta* is the first particle-physics challenge to specifically target computer scientists in Africa, and puts the public TrackML challenge dataset to new use. Created by ATLAS computer scientists Sabrina Amrouche and Dalila Salamani, the Cirta challenge aims to bring new blood into the growing field of machine learning for particle physics. "Zindi has a strong community of computer scientists based on the continent, and we're looking forward to reviewing their creative solutions to the challenge," says Salamani.
Linear transformations are often used in machine learning applications. They are useful in the modeling of 2D and 3D animation, where an objects size and shape needs to be transformed from one viewing angle to the next. An object can be rotated and scaled within a space using a type of linear transformations known as geometric transformations, as well as applying transformation matrices.
There is a pernicious myth floating around. It's that rule-based Security Information and Event Management (SIEM) is old technology, and is no longer worth using today. It's that modern attacks can bypass rule-based SIEMs. This is partly true, but mostly false. There are large, older companies out there that don't use SIEMs to defend their data, and they do so at their own risk.