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) …
French technology consulting firm Atos has launched an artificial intelligence (AI) software suite for businesses across the world, which intends to make it simpler for teams to build AI-based applications using a combination of intellectual properties, a report said. Called Atos Codex AI Suite, the software package also intends to make it easier for teams of developers and data scientists to collaborate on the development and training of AI models, The Economic Times reported. The report also added that, with the help of the suite, the apps can be deployed and relocated across multiple environments like public cloud, on-premises or edge computing. "Atos Codex AI Suite tackles new enterprise, scientific and industry challenges, such as precision medicine, advanced prescriptive maintenance and prescriptive security, with a new generation of cognitive applications," said Arnaud Bertrand, senior vice-president, strategy and innovation BDS (Big Data and cybersecurity), Atos. Atos Codex AI Suite can either be purchased as a standalone software platform or together with a server infrastructure, the company added.
The last few months have witnessed a rise in the attention given to Artificial Intelligence (AI) and robotics. The fact is that robots have already become a part of society; in fact, it is now an integral part. Big data is also definitely a buzzword today. Enterprises worldwide generate a huge amount of data. The data doesn't have a specified format.
The digital experience (DX) era is here, and AI is one of the primary technologies to fuel productivity and innovation in the retail and consumer goods industry. Brands that take a wait and see approach may find themselves quickly outpaced by their competitors. And by competitors, I mean not just born-in-the-cloud e-commerce players, but forward-thinking omnichannel retailers who focus on winning customers and evolving retail at scale. Within the spectrum of digital transformation, AI is not a new technology. It is moving from its research roots to entering the mass market.
And it won't replace your radiologist. That stated, I agree with Curtis Langlotz, MD, PhD of Stanford, who stated at RSNA this year that radiologists who use AI will replace radiologists who don't. So, what is the path toward making AI a key enabler for medicine? AI-powered healthcare requires three key factors: sound data science, sharp focus and strategic deployment. And, it requires the patience to balance the excitement of advanced digital technology with the practical realities of how healthcare operates.
Business metaphors often contain biological references. For example, we refer to "product families" and talk about the "next generation." We talk about businesses "evolving" and "product lifecycles." We find some companies "on the bleeding edge" of new technologies. In the Digital Age, we find data running through veins of companies and the Internet of Things providing the nervous system of the digital enterprise.
Julia is a free open source, high-level, high-performance, dynamic programming language for numerical computing. It has the development convenience of a dynamic language with the performance of a compiled statically typed language, thanks in part to a JIT-compiler based on LLVM that generates native machine code, and in part to a design that implements type stability through specialization via multiple dispatch, which makes it easy to compile to efficient code. In the blog post announcing the initial release of Julia in 2012, the authors of the language--Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman--stated that they spent three years creating Julia because they were greedy. They were tired of the trade-offs among Matlab, Lisp, Python, Ruby, Perl, Mathematica, R, and C, and wanted a single language that would be good for scientific computing, machine learning, data mining, large-scale linear algebra, parallel computing, and distributed computing. In addition to being attractive to research scientists and engineers, Julia is also attractive to data scientists and to financial analysts and quants.
Industry 4.0 is characterized by applying cloud and cognitive computing to current automated and computerized industrial systems resulting in the ability to create smart factories that monitor physical processes, identify issues or optimizations, and perform iterative refinement or proactive maintenance and updates. A recent study was released by Emory University and Presenso called The Future of IIoT Predictive Maintenance. The study is focused on predictive maintenance current state, implementation, resulting impact, and future needs identified within smart factories. Over 100 operations and maintenance professionals across Europe, North America, and Asia Pacific participated. The results showed that while there was good satisfaction with existing predictive maintenance environments, the modeling and machine learning aspects are lagging behind where spreadsheet based statistical modeling has not been replaced by more advanced capabilities.
Whether you call it cognitive computing, machine learning, deep learning or artificial intelligence (AI), the era of collaborative human-machine intelligence has begun, and the implications for healthcare are enormous. Without the leverage of AI, there's just simply no other way to turn the massive volumes of data coming from diverse and rapidly growing sources into the meaningful insights so critically needed to move into the new age of precision medicine and rise of healthcare consumerism. In fact, according to a PwC report, 54 percent of healthcare consumers worldwide are already open to receiving AI-enabled healthcare. Humans are critical to this next wave. The humans of healthcare--physicians, caregivers, researchers, administrators, policy makers--will increasingly rely on thinking machines to uncover patterns and inform decisions that benefit patients, populations, health systems and society at large.
This article focuses on the political and geopolitical consequences of the feedback relationship linking Artificial Intelligence (AI) in its Deep Learning component and computing power – hardware – or rather high performance computing power (HPC). It builds on a first part where we explained and detailed this connection. There we underlined notably three typical phases where computation is required: creation of the AI program, training, and inference or production (usage). We showed that a quest for improvement across phases, and the overwhelming and determining importance of architecture design – which takes place during the creation phase – generates a crucial need for ever more powerful computing power. Meanwhile, we identified a feedback spiral between AI-DL and computing power, where more computing power allows for advances in terms of AI and where new AI and the need to optimize it demand more computing power.
Yes, the quotes are intentional. I'm a marketer, so I understand that rebranding might drive up the price, so some programmers and analysts are calling themselves data scientists. What that means is that business people, from executives to HR to line managers, should have a better understanding of the claim and reality. The entire purpose of business software systems has always been to provide business decision makers the KPI's they need to make informed decisions. That was the goal with mainframes and reports on ribbon paper, and it remains the goal on today's smartphones.