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) …
"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models. This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful.
Future AI systems are ramped up into medical research and evolving the best practices in the healthcare sector. With constant improvement in a different paradigm, patient-centered service is a leading focus. This aims for improving between good health care and the healthcare that people actually receive. Using advanced human-generated data devices, multiple office visits for checkups, and routine treatment are replaced with remote monitoring. Providers can save time and increase the accuracy of their diagnoses when this tracking is combined with online consultations, driven by AI.
With algorithms doing all sort of chores, which were once considered intelligent (list includes stock trading, driving, playing games like go and chess, image recognition, music creation), it's imperative we try and find an objective definition for intelligence. Is it recognising patterns; if so any quantizable pattern can be learnt by an algorithm and it can start recognising it. Is it the ability to find cause and affect relationships in patterns; that too can be learnt. Mathematicians devised concept of cross correlation centuries ago. Is it ability to tell a narrative about certain patterns?;
The research company Capgemini investigated how artificial intelligence affects sectoral life. Research shows that 60 percent of companies that make decisions on artificial intelligence have gone through a legal review process. The research also reveals what kind of artificial intelligence systems consumers want. Capgemini, a consultancy company based in France, conducted a research on the reflection of artificial intelligence on the sectors. Research has revealed that 90 percent of companies that start using artificial intelligence-supported systems face various ethical problems.
"I want to meet, in my lifetime, an alien species," said Hod Lipson, a roboticist who runs the Creative Machines Lab at Columbia University. "I want to meet something that is intelligent and not human." But instead of waiting for such beings to arrive, Lipson wants to build them himself -- in the form of self-aware machines. To that end, Lipson openly confronts a slippery concept -- consciousness -- that often feels verboten among his colleagues. "We used to refer to consciousness as'the C-word' in robotics and AI circles, because we're not allowed to touch that topic," he said.
Roger Perlmutter, the head of research and development at Merck, is joining the board of Insitro, a firm focused on using artificial intelligence to discover drugs. Insitro, backed with $243 million in venture capital from firms including Casdin Capital and ARCH Venture Partners, was founded by Daphne Koller, known for co-founding Coursera, the online learning firm, and working at Calico, a drug discovery arm of Alphabet. The company has a research partnership with Gilead Sciences. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.
Machine learning techniques don't just have the capability to make our computers, software, and devices more'intelligent' -- enhancing systems with the ability to predict, personalize, analyze, and more. More direct, physical advantages include their ability to reduce the amount of power these systems use. Engineers at the Swiss Center for Electronics and Microtechnology (CSEM) develop a new machine-learning method capable of cutting energy use in real-life scenarios, such as the heating, ventilation, and air conditioning (HVAC) systems in buildings. HVAC systems are typically responsible for a significant proportion of total building energy consumption, and subsequently, a large volume of total energy consumption in any industry. The engineers' research was published in IEEE Transactions on Neural Networks and Learning Systems.
In a move to expand its business into the logistics and delivery segment, ride-hailing startup Via today announced that it acquired Fleetonomy for an undisclosed sum. Via, which says it plans to apply Fleetonomy's expertise in demand prediction and fleet utilization to support fully integrated, digitally powered logistics solutions, says the pandemic has highlighted the growing need for essential services and goods delivery. Tel Aviv-based Fleetonomy, which was founded in 2017 by CEO Israel Duanis and CTO Lior Gerenstein, taps AI to analyze data and deliver insights with the goal of maximizing inventory and promoting proactive maintenance. The company provides white label ride-sharing and on-demand car subscription services that can accommodate semiautonomous and autonomous fleets. With Fleetonomy's cloud-based suite of tools, managers can simulate services before deploying cars on the road, adjusting for factors such as fleet size, parking, charging locations, demand, and more.
Deep learning has been successfully showing promising results in plant disease detection, fruit counting, yield estimation, and gaining an increasing interest in agriculture. Deep learning models are generally based on several millions of parameters that generate exceptionally large weight matrices. The latter requires large memory and computational power for training, testing, and deploying. Unfortunately, these requirements make it difficult to deploy on low-cost devices with limited resources that are present at the fieldwork. In addition, the lack or the bad quality of connectivity in farms does not allow remote computation. An approach that has been used to save memory and speed up the processing is to compress the models. In this work, we tackle the challenges related to the resource limitation by compressing some state-of-the-art models very often used in image classification. For this we apply model pruning and quantization to LeNet5, VGG16, and AlexNet. Original and compressed models were applied to the benchmark of plant seedling classification (V2 Plant Seedlings Dataset) and Flavia database. Results reveal that it is possible to compress the size of these models by a factor of 38 and to reduce the FLOPs of VGG16 by a factor of 99 without considerable loss of accuracy.