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) …
In addition to designing, testing and maintaining engines in the digital realm, the IntelligentEngine vision sets out a future where an engine will be increasingly connected, contextually aware and comprehending, helping to deliver greater reliability and efficiency. Specifically, engines will connect with other engines and will be contextually aware, allowing them to respond to the environment around them without human intervention. Through the IntelligentEngine initiative, an aero engine will be able to learn from its own experiences and from its network of peers, and will adjust its behaviour to achieve best performance, according to Rolls-Royce. The initiative is supported by Rolls-Royce's R2 Data Labs, an acceleration hub for data innovation launched in December 2017. The lab provides a centre for advanced data analytics, industrial AI and machine learning research, along with product development.
Artificial intelligence: Since the term was first coined in 1955 by the late Stanford computer scientist John McCarthy, AI has steadily moved out of the realm of science fiction and now has significant impact on our everyday lives. Related: Is Artificial Intelligence Replacing Your Intelligence? Voice assistants such as Siri, Alexa and Google Assistant utilize voice recognition to transform the way we search for information and interact with our devices. Innovations such as self-driving cars and wearable tech are positioned to transform transportation and healthcare in the foreseeable future. The Harvard Business Review has predicted that AI will affect the economy and our lives on a magnitude similar to that of the steam engine, electricity and the industrial combustion engine.
Expensive flagship phones won't be the only way for you to play with advanced features like AR Emoji, Animoji and Face ID much longer. Qualcomm is making it easier for companies to create mid-range smartphones that pack those functions by launching a new mobile processor. The Snapdragon 710 will come with a multi-core AI Engine and support neural network processing, as well as image signal processors and graphics units that are typically found in higher-end chipsets. The 710 is the first of the 700-series, which was announced at MWC this year, and will sit above options like the 600- and 400-ranges but below top-tier chips like the Snapdragon 845. The Snapdragon 710 is a 10nm chipset that features a multi-core AR engine for on-device neural networking processing, as well as a Spectra 250 image signal processor that enables things like multi-frame noise reduction and AI camera features like video style transfer and active depth sensing for artificial bokeh.
Arm first announced Project Trillium machine learning IPs back in February and we were promised we'd be hearing more about the product in a few months' time. Project Trillium is unusual for Arm to talk about because the IP hasn't been finalised yet and won't be finished until this summer, yet Arm made sure not to miss out on the machine learning and AI "hype train" that has happened over the last 8 months in both the semiconductor industry and as well as particularly in the mobile industry. Today Arm details more of the architecture of what Arm now seems to more consistently call their "machine learning processor" or MLP from here on now. The MLP IP started off a blank sheet in terms of architecture implementation and the team consists of engineers pulled off from the CPU and GPU teams. With the MLP Arm set out to provide three key aspects that are demanded in machine learning IPs: Efficiency of convolutional computations, efficient data movement, and sufficient programmability.
About this course: We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine
Machine learning has become one of the hottest topics in marketing circles in 2018, as organisations strive to understand what it can do and how it works. But as with most emerging technologies, there is limited experience in market against which to make decisions. So when your company is one of the world's largest providers of machine learning technology for business purposes, it makes sense to be selling that technology from a position of practical application. For Oracle chief marketing officer for EMEA and JAPAC, Amanda Jobbins, her machine learning journey started with applying it to the vendor's lead scoring methodology. Although still at the testing phase, the results have been remarkable.
"Hey Siri, what is IIoT?" Every time you ask Siri, you get an accurate answer. Wonder how your virtual assistant suggests, thinks and talks like a human being? Because it has been taught that way through machine learning. Finance, retail and healthcare are just a few of the many industries that are benefitted by machine learning.
Some days ago I attended a private lecture by two extremely seasoned investment bankers, a leading M&A law-firm, a top-class M&A auditor, a private equity principal, and a consultant from Bain. They spent the majority of their time conversing about the effect AI will have on the investment banking industry and mergers & acquisitions in general. I have chosen the most interesting observations, quotes, and stories to illustrate the general industry thinking on artificial intelligence in M&A. The lecture turned into a general talk on AI and the financial industry right from the start. For a brief introduction to AI I would recommend BlackRocks post on Seeking Alpha and this medium piece to get a brief understanding of the vernacular.