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) …
Call them what you will, but in my experience, the millennials I work with are some of the most brave, creative and intelligent people I know. Recently, while in a text conversation with one of my assistants, (a millennial), regarding this article I was writing, he responded with this sentiment, "I wonder if one of the mechanics building the first fleet of Model-Ts felt like this. It's a strange, out of body feeling of'after we do this, a lot of life as we know it is going to change.'" We started riffing on how many people might have been freaking out about the replacement of horses, and all that comes with horseback being the primary vehicle for transportation, (such a loss of jobs for the blacksmith, the farriers, the growers of alfalfa, etc., etc.) I've talked a lot about how artificial intelligence, (AI), and machine learning is revolutionary for our world as a whole, however in this article, I wanted to open the discussion around one very specific application of the machines, (algorithms), that learn with very wide-ranging applications: AI powered marketing and sales. What my millennial assistant and I began to discuss, (all via back-and-forth text messaging), is that, in order to create value-adding autonomous systems, we have to feed those systems with massive amounts of data about ourselves.
If you're in the financial services industry or have an interest in predicting market movements with machine learning, you may be eager to learn how to move your trading signal and forecast generation code into the cloud. You can easily scale up your computational loads, distribute data processing pipelines to run in parallel on multiple machines, speed up the time required to run complex analytics, eliminate the need for management of data storage, and ultimately eliminate the need for multiple data centers. In this post, we'll show how to build a data processing pipeline that starts with a market data feed as the input and uses machine learning to generate real-time forecasts as the output, with all application components running natively in Google Cloud. In the sections below, you'll learn how to build a complete end-to-end application that subscribes to the Thomson Reuters FX (foreign exchange) data feed published on a Cloud Pub/Sub topic, incrementally trains a TensorFlow neural network model, generates real-time forecasts of FX rates, and saves the forecasts into BigQuery for subsequent analysis. First, we'll focus on Cloud Pub/Sub as the connector used to link multiple application components.
If nothing else, AI continues to climb the technology hype curve. It was impossible to read the news, browse the web, attend a conference, or even watch television without seeing a reference to how AI is making our lives better. Since Alan Turing declared "what we want is a machine that can learn from experience" in a 1947 lecture to the London Mathematical Society, the imaginations of computer scientists and engineers have run wild with visions of a computer that can answer questions on par with a human. Today, almost everyone in business is looking at how to leverage AI, and there is no shortage of vendors looking to capitalize on the trend. Venture Scanner currently tracks more than 2,000 AI startups that have received more than $26 billion in funding.
Qualcomm on Wednesday announced the Snapdragon 845 Mobile Platform, a System on a Chip built for immersive multimedia experiences including extended reality (XR), on-device artificial intelligence and high-speed connectivity. The SoC will power next-generation Android flagship smartphones and Windows 10 notebooks based on ARM architecture. Xiaomi reportedly will use it in its forthcoming Mi 7 flagship, to be released next year. The SoC incorporates Qualcomm's Spectra 280 image signal processor (ISP), its Adreno 630 visual processing subsystem, and the company's secure processing unit (SPU), which enables improved biometrics security and user or application data key management. The 845 supports Google's TensorFlow and Tensorflow Lite, and Facebook's Caffe/Caffe2 frameworks, as well as the new Open Neural Network Exchange.
Artificial Intelligence (AI) is starting to change how many businesses operate. The ability to accurately process and deliver data faster than any human could is already transforming how we do everything from studying diseases and understanding road traffic behaviour to managing finances and predicting weather patterns. For business leaders, AI's potential could be fundamental for future growth. With so much on offer and at stake, the question is no longer simply what AI is capable of, but where AI can best be used to deliver immediate business benefits. According to Forrester, 70% of enterprises will be implementing AI in some way over the next year.
Narayana develops AI applications that can cut down customer problem resolution by a factor of 10. This is what I heard and saw from Bejoy Narayana, CEO of BoodsKapper, at the recent SAP Financial Services Innovation Summit held at the SAP Leonardo Center in New York. The Texas-based startup develops AI applications on SAP Cloud Platform designed to not only ferret out what customers want quickly, but also communicate in their preferred medium – using any texting app or moving to a telephone conversation. "No one likes calling customer service, and we believe that experience can be much better by training the software to behave like the ideal customer service representative, getting to the point quickly to provide a solution for busy people," said Narayana. "Modeling the actions of a company's best customer agent, we can train the AI engine to be up and running in weeks just as you would a new employee.
With technological innovation occurring at a faster rate than ever before, many once expensive technologies are being made significantly cheaper. And nowhere is this more true than in the field of 3D machine vision. While machine vision is no new technological development, the emergence of cheaper 3D imaging cameras and sensors has recently been the catalyst for a host of new applications for the technology. The majority of machine vision systems traditionally used 2D imaging to identify the position of a products on a conveyor belt, for example. However, one of the biggest pit falls of using two dimensions, x and y, is that, in this example, the products size and shape would need to be known in order for the system to work.
So you want to learn how to program a quantum computer. Now, there's a toolkit for that. Microsoft is releasing a free preview version of its Quantum Development Kit, which includes the Q# programming language, a quantum computing simulator and other resources for people who want to start writing applications for a quantum computer. The Q# programming language was built from the ground up specifically for quantum computing. The Quantum Development Kit, which Microsoft first announced at its Ignite conference in September, is designed for developers who are eager to learn how to program on quantum computers whether or not they are experts in the field of quantum physics.
It's no secret today that all our applications and devices are generating tons of data; thus making data analytics a very hot topic. Microsoft Azure has all the tools necessary to ingest, manage, and process all this data, which is also known as Big Data. However, all this data in and of itself is not useful unless processed, interpreted, and visualized correctly. Another power behind the data acquired through the years is to make Predictive Analytics, that is, using the data to make forecasts and predictions. But, by only using the data gathered, it is difficult to make an analysis.
In the business world, there are many factors to consider when making the optimal decision. There are so many data points to consider that it becomes a combinatorial problem. For example, consider when and how to raise room rates across a hotel chain based on locations and current events or how best to optimize airline ticket prices given fluctuating fuel costs, factoring in seasonal conditions and local and/or global events. This flows over into our social and personal lives, as we rightly expect to find the nearest coffee shops located to the nearest public libraries or where to buy the cheapest gas closest to the supermarket that stocks the groceries we need. Decision optimization (DO) is the prescriptive element of the data science lifecycle and is key to delivering artificial intelligence, as machine learning (ML) and DO have somewhat of a symbiotic relationship.