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) …
As the world is changing due to the advancement of technology, computers are advancing faster than ever before. The introduction of quantum computing has enabled many computers to effectively handle the enormous amount of data stored daily, increase speed when analyzing data, and expand artificial intelligence, machine learning, and programming capabilities. Below are some ways quantum computing will revolutionize artificial intelligence, big data, and machine learning. Quantum computing will enable memory storage capacities to increase through the modification of slow memory, random access memory (RAM), random only memory (ROM), and cache memory. These memory changes will improve the memory that's stored in databases on different computers.
APIs, or application processing interfaces, are packages of code critical to AI functionality in products and software. They can add more value to AI capabilities with descriptions, and call outs. The future of AI is marked with a race against time, as man strives to make machines more intelligent than humans! What was a fascinating aspect of science fiction has now become the most powerful technology disruptive everyday processes in industries and businesses, and human touchpoints? With continuous breakthroughs in AI research, across domains and use cases, AI is being implemented by one company after another, at a breakneck speed. Thus, AI is based on several disciplines that contribute to intelligent systems – mathematics, biology, logic/philosophy, psychology, linguistic, computer science, and engineering. You need to have a certain level of expertise in math, probability, statistics, algebra, calculus, logic, and algorithms.
Some call it "strong" AI, others "real" AI, "true" AI or artificial "general" intelligence (AGI)... whatever the term (and important nuances), there are few questions of greater importance than whether we are collectively in the process of developing generalized AI that can truly think like a human -- possibly even at a superhuman intelligence level, with unpredictable, uncontrollable consequences. This has been a recurring theme of science fiction for many decades, but given the dramatic progress of AI over the last few years, the debate has been flaring anew with particular intensity, with an increasingly vocal stream of media and conversations warning us that AGI (of the nefarious kind) is coming, and much sooner than we'd think. Latest example: the new documentary Do you trust this computer?, which streamed last weekend for free courtesy of Elon Musk, and features a number of respected AI experts from both academia and industry. The documentary paints an alarming picture of artificial intelligence, a "new life form" on planet earth that is about to "wrap its tentacles" around us. There is also an accelerating flow of stories pointing to an ever scarier aspects of AI, with reports of alternate reality creation (fake celebrity face generator and deepfakes, with full video generation and speech synthesis being likely in the near future), the ever-so-spooky Boston Dynamics videos (latest one: robots cooperating to open a door) and reports about Google's AI getting "highly aggressive" However, as an investor who spends a lot of time in the "trenches" of AI, I have been experiencing a fair amount of cognitive dissonance on this topic.
Recently, "The Economists" emphasized on the fact that data has become the most valuable commodity held by people. When small chunks of data are combined on a large scale, then it's termed as Big Data. While we are busy in securing Big Data from attacks, it is quietly contributing towards the growth of Artificial Intelligence. Well, Machine Learning, a section of AI is making exponential improvements and can be termed as "the information escalated strategy." Simply put, huge chunks of data are required to make, test and prepare AI.
A representation of a deep learning neural network designed to intelligently extract text-based information from cancer pathology reports. Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research.
Cloud Robotics is a term that was popularized by James Kuffner after he brought together researchers from different relevant fields (robotics, machine learning, and computer vision) to assist in coming up with the initial Cloud Robotics concept. Cloud robotics, as the name suggests is bringing together cloud computing and robotics. In essence, taking all the benefits of cloud computing and finding ways to apply them to robot software and robotics. The past couple of years have established cloud computing as the technology of now and the future. In 2017, spending on cloud services was $153.5bn, and this is expected to rise by 21.1% in 2018 to $184.4bn.
To compete in today's data-driven world, organizations need to accelerate the digital transformation process that puts technology at the heart of products, services and operations. Digital transformation enables both private and public entities to provide better outcomes and experiences for the people they serve -- from smarter vehicles to personalized healthcare, from customized shopping experiences to the prevention of credit card fraud. A common thread to these and countless other digital transformation use cases is artificial intelligence. AI applications and their underlying technologies, including machine learning and deep learning, enable organizations to train systems to use massive amounts of data to sense, learn, reason, make predictions and evolve. Under the hood, the engine that makes it all go is the blazingly fast processing power of high-performance computing (HPC) clusters.
For a very long time, women working in the fields of science, technology, engineering and math were unwelcome and underappreciated. Take for example the story of Katherine Johnson and her colleagues, who made remarkable contributions to the early years of NASA's space program. The world had not even heard of her name until two years ago, when the movie, Hidden Figures, hit the screens. Sadly, it is still a man's world in the STEM fields, and women struggle every day to find a strong foothold in it. The disparity between the number of men and women with successful careers in STEM is unfortunately large.
Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.
The Blockchain market is forecast to grow in a 61.5% Compound Annual Growth Rate (CAGR) between 2016 and 2021, developing from $.2B to $2.3B in 2021. The largest segments are in the company and financial services and technologies, telecom and media. The biggest protocols comprise Bitcoin, Ethereum, and Ripple. Deloitte discovered that banks have allegedly stored between $8B to12B annually with blockchain technology to enhance operational efficiencies. The Artificial Intelligence (AI) market is predicted to rise from $8B in 2016 to $72B from 2021, reaching a 55.1 percent CAGR.