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) …
Apple is working on technology for the perfect selfie. The tech giant acquired Spectral Edge, a UK-based AI startup that uses machine learning to make smartphone pictures crisper, with more accurate colors. The system captures and blends an infrared shot with a standard shot to enhance a photograph's overall depth, detail and color. The startup uses a process that completely relies on machine learning that can be combined with both hardware and software to improve pictures. The news was first revealed by Bloomberg, which obtained secret documents'that Apple now controls Spectral.'
In the past few years, you might have noticed the increasing pace at which vendors are rolling out "platforms" that serve the AI ecosystem, namely addressing data science and machine learning (ML) needs. The "Data Science Platform" and "Machine Learning Platform" are at the front lines of the battle for the mind share and wallets of data scientists, ML project managers, and others that manage AI projects and initiatives. If you're a major technology vendor and you don't have some sort of big play in the AI space, then you risk rapidly becoming irrelevant. But what exactly are these platforms and why is there such an intense market share grab going on? The core of this insight is the realization that ML and data science projects are nothing like typical application or hardware development projects.
From parts suppliers to vehicle manufacturers, service providers to rental car companies, the automotive and related mobility industries stand to gain significantly from implementing machine learning at scale. We see the big automakers investing in proof-of-concept projects at various stages, while disruptors in the field of autonomous driving are trying to build entirely new businesses on a foundation of artificial intelligence and machine learning. There are huge opportunities for machine learning to improve both processes and products all along the automotive value chain. But where do you focus? And how can you make sure your investments in machine learning aren't just expensive, "one-and-done" applications?
If you work with data in any capacity, go ahead and do yourself a favor: download KNIME Analytics Platform right here. I could dive into a quick start tutorial or show off some of the more advanced capabilities, but it's honestly very intuitive to use. KNIME Analytics Platform is 100% free. Those features allow you to automate workflow deployment, execute workflows remotely from another service, and create an interactive hub for users. The ability to automate workflows makes KNIME Server Medium an attractive option.
Marc Andreessen famously said that "Software is eating the world" and everyone gushed into the room. This was as much a writing on the wall for many traditional enterprises as it was wonderful news for the software industry. Still no one actually understood what he meant. "Today, the world's largest bookseller, Amazon, is a software company -- its core capability is its amazing software engine for selling virtually everything online, no retail stores necessary. On top of that, while Borders was thrashing in the throes of impending bankruptcy, Amazon rearranged its web site to promote its Kindle digital books over physical books for the first time. Now even the books themselves are software."
Hopefully, this app helps you to accelerate your next-generation AI or ML initiative leveraging Splunk's Data-To-Everything-Platform and your favourite frameworks or open source libraries. You find many Juypter notebook examples and a predefined workflow that should help you to get started easily! Even if the desired set of ML libraries is not there yet, you can easily extend the app with your custom MLTK Container. Rebuild the existing MLTK Container images or build your own custom images with the open-source repository on GitHub. Most recently, we finally released the latest version of Deep Learning Toolkit 3.0 which is compatible with Splunk 8.0 and Machine Learning Toolkit 5.0 based on Python 3.
More than 13,000 artificial intelligence mavens flocked to Vancouver this week for the world's leading academic AI conference, NeurIPS. The venue included a maze of colorful corporate booths aiming to lure recruits for projects like software that plays doctor. Google handed out free luggage scales and socks depicting the colorful bikes employees ride on its campus while IBM offered hats emblazoned with "I A ." Tuesday night, Google and Uber hosted well-lubricated, over-subscribed parties. At a bleary 8:30 the next morning, one of Google's top researchers gave a keynote with a sobering message about AI's future. Blaise Aguera y Arcas praised the revolutionary technique known as deep learning that has seen teams like his get phones to recognize faces and voices.
Artificial Intelligence is on everyone's lips right now. It is the fastest growing branch of the high-tech industry. The German government sees AI as a key strategy for mastering some of the greatest challenges of our time, such as climate change and pollution. It is difficult to establish a clear differentiation of Artificial Intelligence or even a precise definition. AI is often used in connection or sometimes even synonymous with the terms machine learning, big data, or deep learning.
Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT (IIoT). For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? I find it essential that the implementation of the industrial internet is incorporated into the company's strategy and business development.