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) …
It's hard to believe, but a year in which the unprecedented seemed to happen every day is just weeks from being over. In AI circles, the end of the calendar year means the rollout of annual reports aimed at defining progress, impact, and areas for improvement. The AI Index is due out in the coming weeks, as is CB Insights' assessment of global AI startup activity, but two reports -- both called The State of AI -- have already been released. Last week, McKinsey released its global survey on the state of AI, a report now in its third year. Interviews with executives and a survey of business respondents found a potential widening of the gap between businesses that apply AI and those that do not.
The need for Energy Savings has become increasily foundamental to fight Climate Change. We have been working on a cloud-based RL algorithm that can retrofit existing HVAC controls to obtain substantial results. In the last decade, a new class of controls which relies on Artificial Intelligence have been proposed. In particular, we are going to highlight data-driven controls based on Reinforcement Learning (RL), since they showed from the very beginning promising results as HVAC controls . There are two main ways to upgrade with RL the air conditioning systems: to implement RL on new systems or to retrofit the existing ones.
Christopher Savoie, PhD is the CEO & founder of Zapata Computing. He is a published scholar in medicine, biochemistry and computer science. In the coming weeks, the Chinese fintech giant Ant Group is set to raise $34 billion in the world's largest-ever IPO. Although it only spun out of Alibaba in 2014, Ant's valuation, at $310 billion, will be comparable to that of JPMorgan Chase, whose origins date back to 1799. In their 2018 book, Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Harvard Business School professors Marco Iansiti and Karim R. Lakhani make the case that Ant's stunning growth can be directly attributed to its use of artificial intelligence (AI).
The applications of Artificial Intelligence (AI) for X-ray and CT-Scan image analysis using Convolutional neural network architectures, Generative adversarial networks, transfer learning, and data augmentation techniques are discussed. Currently, AI algorithms embedded on a mobile x-ray and CT-Scan devices for automated diagnosis, measurements, case prioritization, and quality control are most popular research area. More than 60,000 research articles have been published related to the use of deep learning in healthcare and related applications. Established architectures, such as ResNet-50 or DenseNet-161 (with 50 and 161 representing the number of layers within the respective neural network) are easy to use. Integration of the AI modules with the drug systems and the experts are the key issues of implementing AI systems in healthcare.
As DeepMind, the British artificial intelligence (AI) firm owned by Google, claims to have solved one of science's toughest and most enduring mysteries, the'protein folding problem', you can't help but think what sort of genius must be the driving forced behind such a triumph. 'Thrilled to announce our first major breakthrough in applying AI to a grand challenge in science,' writes Demis Hassabis, the company's 44-year-old founder says in reaction to the news. But was it really a surprise that Hassabis' firm had achieved such a feat? Thirty years ago, Hassabis was the world's second best 12-year-old chess player, his career as a future grandmaster set out before him. 'Thrilled to announce our first major breakthrough in applying AI to a grand challenge in science,' writes Demis Hassabis, the company's 44-year-old founder says in reaction to the news But while he loved the game and what it taught him about his own thought processes that brought such success, the youngster realised the game of chess was not what actually interested him. 'It got me into thinking about the process of thought: what is intelligence, how is my brain coming up with these ideas?' Hassabis finished his A-levels at 15, and although he was accepted into Cambridge he would have to wait until he was old enough to enrol.
A long-standing and incredibly complex scientific problem concerning the structure and behaviour of proteins has been effectively solved by a new artificial intelligence (AI) system, scientists report. DeepMind, the UK-based AI company, has wowed us for years with its parade of ever-advancing neural networks that continually trounce humans at complex games such as chess and Go. All those incremental advancements were about much more than mastering recreational diversions, however. In the background, DeepMind's researchers were seeking to coax their AIs towards solving much more fundamentally important scientific puzzles – such as finding new ways to fight disease by predicting infinitesimal but vitally important aspects of human biology. Now, with the latest version of their AlphaFold AI engine, they seem to have actually achieved this very ambitious goal – or at least gotten us closer than scientists ever have before. For about 50 years, researchers have strived to predict how proteins achieve their three-dimensional structure, and it's not an easy problem to solve.
ServiceNow Inc. is beefing up its artificial intelligence development capabilities with the acquisition today of a company called Element AI Inc. that's widely known as one of the pioneers in the field. Montreal-based Element AI launched back in 2016 as a professional services firm focused on helping traditional enterprises implement machine learning. The startup garnered significant industry attention from the outset thanks in part to its high-profile co-founder, the well-known deep learning researcher Yoshua Bengio, who won the Turing Award in 2018 for his contributions to the field. Element AI has gradually expanded its focus since its launch by creating a fund to support fellow machine learning companies and introducing ready-made AI tools. The company's offerings include Knowledge Scout, a search engine for manufacturers that speeds up the diagnosis and repair of production line issues by giving technicians relevant information about previous incidents with similar characteristics.
A COMBINATION of recent events has seen a rapid acceleration in the adoption and incorporation of technologies by a wide range of firms and institutions in the global financial sector. Whether this adoption has been spurred on by the global financial crisis of 2008; the need to adhere to regulation; or the immediate need to pivot and handle the consequences of Covid-19 and its impact on customers and staff, firms in the finance industry are embracing financial technologies (fintech) into their daily processes. Designed to drive enhancement in services and improve efficiencies in back-office operations, it has seen a thriving sector developed beyond traditional'Wall Street' financing. The prospect of the part that machine learning (ML) could play is generating a lot of momentum. The financial sector is well-placed to benefit from machine learning, with large volumes of historical structured and unstructured data to learn from.
Debug is starting to be rethought and retooled as chips become more complex and more tightly integrated into packages or other systems, particularly in safety- and mission-critical applications where life expectancy is significantly longer. Today, the predominant bug-finding approaches use the ubiquitous constrained random/coverage driven verification technology, or formal verification technology. But as designs become more complex, new methodologies and approaches need to be applied to ensure quality over time. This can include everything from AI/ML to simply updating methodologies to include more automation and less bug tracking in a notebook or spreadsheet. "For constrained random, the usual approach is to create a coverage model from the verification plan and objectives, which serves as a map of where to target the stimulus to test the important design features thoroughly," said Mike Stellfox, a Cadence fellow. "In a constrained random verification scenario, the tests are generating all kinds of random stimulus that exercise different paths through the design that you wouldn't necessarily think of as a human, and bugs will be found. Because you have coverage goals, you know whether you exercised what you thought were the important things. What typically happens is a bug will be found, such as a certain behavior, either triggered by a checker identifying that this didn't behave correctly, or another second-order effect. This could include not having a direct checker, a state machine hung, or another erroneous behavior occurred. Once the bug is found, a root cause analysis will be done with the designer to determine the cause, and that's where you go through the debug process."
The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare.