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) …
The innovation and rate of growth in artificial intelligence (AI) and high-performance computing (HPC) applications are staggering. Autonomous cars, fraud detection, business intelligence, affinity marketing, personalized medicine, Alexa, Siri, smart cities and the Internet of Things (IoT) are just a few of the commercial and consumer-driven applications exploding in use on a global scale. Underlying these offerings are dense computing platforms and IT infrastructures that require highly specialized data center environments in order to perform reliably and scale efficiently. Legacy on-premise or colocation data centers, built years ago to support general-purpose computing servers, are becoming obsolete in certain markets due to the intense power and cooling requirements of the HPC and AI servers. These servers are built upon processing-intensive components such as graphics processing unit (GPU) cards and dual central processing unit (CPU) architectures.
Peter Ruffley, chairman at Zizo, discusses how we can best use artificial intelligence and what its role is within the data centre. The interest in adding AI and machine learning to business models is fast gaining momentum as organisations look to find patterns within their data that can deliver greater business and customer intelligence, and predict future trends. As Gartner highlights, the number of enterprises implementing AI tripled in the past year. However, Gartner also claims that more than 30% of data centres that don't deploy AI and machine learning won't be operationally and economically feasible by 2020. At present, the IT industry is doing itself no favours by promising the earth with emerging technologies, without having the ability to fully deliver them.
Picterra, a Swiss AI-based SaaS platform allows users to interactively create a personalized AI detecting, localizing and counting any objects from satellite and aerial imagery. The company aims to democratize geospatial mapping, and its platform bridges the gap between Earth Observation (EO) imagery, cloud processing and geospatial insights by commoditizing Machine Learning technology. From precision agriculture to utilities and infrastructure, Picterra serves a wide variety of clients and provides customized services. Its main partners are geospatial and UAV mapping professionals looking to derive insights and actionable information for specific verticals based off large or heavy EO imagery set. The Picterra platform allows users to seamlessly integrate cutting edge machine learning technology into their existing workflow, so they can focus on their core business while achieving quick return on investment.
Thanks to recent advancements in electronics and the programmed logic that provides gadgets with an incipient ability to make decisions and take actions, humans are starting to have meaningful interactions with machines. We are on the cusp of a robotics Golden Age, which I believe will increase our standard of living to the degree that mass production, electricity and transistors did for our ancestors. The term "smart manufacturing" refers to business processes that feature machines in decision making roles. Artificial intelligence (AI) provides the decisions, and machine learning (ML) enables an ongoing refinement of those decisions based on zettabytes of data that the internet of things (IoT) makes available to a global network of devices. Improvements in computer networking technologies are key to realizing autonomy among collaborating devices, cooperative productivity featuring little to no direction from human operators.
Though it's still several years away from widespread deployment, 5G is a key component in the evolution of cloud-computing ecosystems toward more distributed environments. Between now and 2025, the networking industry will invest about $1 trillion worldwide on 5G, supporting rapid global adoption of mobile, edge, and embedded devices in practically every sphere of our lives. It will be a proving ground for next-generation artificial intelligence (AI), offering an environment within which data-driven algorithms will guide every cloud-centric process, device, and experience. Just as significant, AI will be a key component in ensuring that 5G networks are optimized from end to end, 24 7. AI will live at every edge in the hybrid clouds, multiclouds, and mesh networks of the future. Already, we see prominent AI platform vendors--such as NVIDIA--making significant investments in 5G-based services for mobility, Internet of Things (IoT) and other edge environments.
In an always advancing cyber threat landscape where antivirus programming and firewalls are viewed as tools of antiquity, companies are currently searching for all the more technologically advanced methods for protecting classified and sensitive data. Artificial intelligence (AI) is accepting the situation as a warrior against digital threats over the globe. It has gotten mainstream in military space, yet security organizations are likewise consolidating AI technologies for using deep learning to discover likenesses and differences within a data set. Organizations like Microsoft are putting 1 billion USD in AI-based organizations, for example, Open AI. As indicated by ESG research, 29% of security experts would like to utilize AI innovation to accelerate the virus detection process.
Yesterday, the Netflix team announced to open-source Metaflow, a Python library that helps scientists and engineers build and manage real-life data science projects. The Netflix team writes, "Over the past two years, Metaflow has been used internally at Netflix to build and manage hundreds of data-science projects from natural language processing to operations research." Metaflow was developed by Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to deep learning. It provides a unified API to the infrastructure stack required to execute data science projects, from prototype to production. Models are only a small part of an end-to-end data science project.
AI, Machine Learning, IoT and Cloud-Based Services Must Deliver Value From Their Data. With growing attention devoted to AI, machine learning, and IoT, what we've come to know as big data has become an even broader version of itself. In recent years, big data was seen as an unstoppable force of nature that would either overwhelm enterprises or propel them to new heights. This next generation of big data -- we'll call it expansive data, pulsing through systems in real-time, powering processes unseen to human eyes, and adapting and learning as it goes along -- is going to reshape enterprises in ways not even anticipated. This requires attention to new types of tools, platforms, and approaches to deliver value to today's data-hungry businesses.
Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...