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) …
"An AI would provide the equivalent of a'Messiah' – having many orders of magnitude more processing elements than the brain" Mr Mitchell claims the same drive that compels people to believe in God and follow religions will work for Artificial Intelligence. He explained: "We [believe] there must be some higher power that causes lightning, sunsets, and crashing waves – or at least speaks to the bottom of our beings, rather than ignore them as ho-hum background." Dr. Stephen Thaler, the President and CEO of Imagination Engines and an AI and consciousness expert, has claimed people will rely on AI to provide solutions to society's problems. "An AI would provide the equivalent of a'Messiah' – having many orders of magnitude more processing elements than the brain, enabling it to gift us with solutions to the most daunting social, political, economic, and environmental challenges," he said.
Artificial intelligence has become a part of our life – its objectively huge potential is now obvious to everyone, more and more is being said about innovative products that give an idea of an AI-operated world of the future, but less so, about the risks associated with the introduction of such technologies – primarily because they are not considered relevant yet. Nevertheless, while developing solutions for data protection with the introduction of AI, we can confidently say that the relevance of these risks will become a problem not in years, but months from now. Starting with the fact that along with promising technological trends - machine learning and AI - the technologies of cyberthreats grow and develop at the same pace, if not faster – recent cases of WannaCry and NonPetya only prove that. AI algorithms, with all their advantages, have a fundamental problem – data sensitivity. The general weakness with most of the algorithms created so far is that they are trained not to understand information, but to recognize the right answers.
"We cannot be conscious of what we are not conscious of." Unlike the director leads you to believe, the protagonist of Ex Machina, Andrew Garland's 2015 masterpiece, isn't Caleb, a young programmer tasked with evaluating machine consciousness. Rather, it's his target Ava, a breathtaking humanoid AI with a seemingly child-like naïveté and an enigmatic mind. Like most cerebral movies, Ex Machina leaves the conclusion up to the viewer: was Ava actually conscious? In doing so, it also cleverly avoids a thorny question that has challenged most AI-centric movies to date: what is consciousness, and can machines have it?
Looking for a way to turn your home computer into a deep-learning AI super-monster? Nvidia has an expensive answer. The new Titan V GPU promises a crazy amount of processing for deep learning and AI applications. It's nine times more powerful -- at 110 teraflops -- than last year's Titan X, Nvidia's last massive desktop graphics processor aimed at machine learning applications. The Titan V is based on Nvidia's newer Volta chip architecture, which is also being used in Nvidia's Xavier self-driving car system and for data centers.
As we're nearing the end of 2017 (and coincidentally the first day of NIPS 2017), we've come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working in this field for quite a bit longer). It's been just 5 years and we've absolutely revolutionized the way we look at the capabilities of machines, the way we build software (Software 2.0), and the ways we think about creating products and companies (Just ask any VC or startup founder). Tasks that seemed impossible just a decade ago have become tractable, granted you have the appropriate labeled dataset and compute power of course. In this post, we'll overview the last couple years in deep learning, focusing on industry applications, and end with a discussion on what the future may hold.
ZDNet's Sandra Vogel posted a formal review of the Huawei Mate 10 Pro, giving it an outstanding 9/10 rating. I've been spending quality time with this business-ready powerhouse and think that the AI found in the camera is worth discussing in a bit more detail. Huawei's partnership with Leica has resulted in some fantastic cameras and performance that is tough to beat. DxOMark awarded the Mate 10 Pro it second highest overall score, 97, and best still image score, 100. Keep in mind, these scores are not scaled to 100.
Nvidia cards are the de facto standard for running machine learning workloads and today, the company added yet another high-end compute-centric card to its line-up: the Titan V. This card, which is based on Nvidia's Volta architecture, features 21.1 billion transistors on a 815 mm2 chip that can produce a full 110 teraflops of compute power. All of that power comes at a price, though. The card, which features 12GB of HBM2 memory, will retail for $2,999. For that, though, users will see a 9x increase in raw power compared to the Titan Xp, the card's predecessor, which retailed for "only" $1,299.
While the field of artificial intelligence (AI) has been around for some 60 years, it's now finally a part of our daily lives -- including how we work, bank, shop, interact, invest, drive and get insured. The term AI means different things to different people, but at PwC we think about it on a continuum, moving from assisted to augmented and, finally, autonomous intelligence. Here, I am primarily focusing on assisted intelligence -- applications that help us better perform tasks we're already doing today. This includes things like email filtering, automated processing of insurance claims and customer service chatbots, just to name a few applications. Of course when you're talking about AI, the question of automation and its potential to replace human jobs isn't far behind.
In 2017, the Robotic Process Automation / RPA market has matured. Learning from the evolution of RPA, in this post, we explore the wider implications for Enterprise AI i.e. the deployment of Artificial Intelligence to the Enterprise The post is based on my course on Implementing Enterprise Artificial Intelligence (AI) course where we explore these ideas in detail. For this article, we consider AI to be based on Deep Learning technologies. In contrast to Machine Learning, Deep Learning implies the automatic detection of features. Features could be either a large number of possible impacting characteristics or a hierarchical set of features.
Qualcomm on Wednesday announced the Snapdragon 845, its next-generation mobile processor headed to 2018 high-end flagship smartphones. New features including 4K HDR video capture, better battery life, and improved AI processing were revealed by Qualcomm at its second annual tech summit in Hawaii. Qualcomm revealed the Snapdragon 845 is made up of the Snapdragon X20 LTE modem, Wi-Fi, Hexagon 685 DSP, Aqstic Audio, Adreno 630 GPU, Spectra 280 image signal processing, Kryo 385 CPU, hardware-based security, and memory. This will lead to 30 percent faster graphics, 30 percent better power efficiency, and 2.5x faster display throughput, compared to the Snapdragon 835, Qualcomm said. The Hexagon 685 DSP will allow for faster machine learning and AI features, and is paired with the Aqstic Audio for better recognition of wake words like "Ok, Google."