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) …
It's a challenge that every large company across the globe is facing. As they transform into digital entities, tasks are getting automated with artificial intelligence (AI) taking away repetitive jobs. While, for the individual it finally boils down to how many of their individual tasks are automated, at the corporate level it is a question of human capital management (HCM) that is becoming critical. HCM is becoming all the more important as while companies reduce the workforce with jobs getting automated, on the other hand, they are not getting key talent for specialized jobs. The War for Talent that global consultancy McKinsey had identified nearly two decades ago is now a reality.
Cybercrime is on the rise, and organizations across a wide variety of industries -- from financial institutions to insurance, health care providers, and large e-retailers -- are rightfully worried. In the first half of 2017 alone, over 2 billion records were compromised. After stealing PII (personally identifiable information) from these hacks, fraudsters can gain access to customer accounts, create synthetic identities, and even craft phony business profiles to commit various forms of fraud. Naturally, companies are frantically looking to beef up their security teams. A large skills gap is causing hiring difficulties in the cybersecurity industry, so much so that the Information Systems Audit and Control Association found that less than one in four candidates who apply for cybersecurity jobs are qualified.
Silicon Valley's start-ups have always had a recruiting advantage over the industry's giants: Take a chance on us and we'll give you an ownership stake that could make you rich if the company is successful. Now the tech industry's race to embrace artificial intelligence may render that advantage moot -- at least for the few prospective employees who know a lot about A.I. Tech's biggest companies are placing huge bets on artificial intelligence, banking on things ranging from face-scanning smartphones and conversational coffee-table gadgets to computerized health care and autonomous vehicles. As they chase this future, they are doling out salaries that are startling even in an industry that has never been shy about lavishing a fortune on its top talent. Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.
Turns out that only two of these four actions merit human intelligence and human interactions (Simplify and Leverage), while the other two present opportunities to reduce demand for support (Eliminate, via root cause analysis to remove confusing or mistake-ridden processes or tools, and Automate, via self-service or proactive alerts. Over 9 years since The Best Service is No Service debuted a lot has changed surrounding the need for and ability to provide human interactions, automated solutions, and shape experiences using predictive analytics. In some cases good old human intelligence in human interactions is still needed, and often the best path. However what I am seeing is the ability now to apply that human intelligence to deliver highly impactful automated solutions and predictive models. One big reason for this is the rapid onset of analytics, robotics, AI (artificial intelligence), Big Data, and machine learning to augment, and in some places replace, human intervention.
Intelligent machines are taking over thousands of jobs, and being qualified is no longer enough to keep your job. Even specialized professions like medicine, law and banking are feeling the heat of Artificial Intelligence (AI). So while "smarter computers are one key to success, doing a smarter job of humans and machines working together is far more important". Recognizing the importance of this skill is Geoff Colvin in his book Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will.
Deere & Company (NYSE: DE) has signed a definitive agreement to acquire Blue River Technology, which is based in Sunnyvale, California and is a leader in applying machine learning to agriculture. As an innovation leader, Blue River Technology has successfully applied machine learning to agricultural spraying equipment and Deere is confident that similar technology can be used in the future on a wider range of products, May said. Blue River has designed and integrated computer vision and machine learning technology that will enable growers to reduce the use of herbicides by spraying only where weeds are present, optimising the use of inputs in farming – a key objective of precision agriculture. "Blue River is advancing precision agriculture by moving farm management decisions from the field level to the plant level," said Jorge Heraud, co-founder and CEO of Blue River Technology.
There's no longer a debate as to whether companies should invest in machine learning (ML); rather, the question is, "Do you have a valid reason not to invest in ML now?" While appreciating the rewards of ML may be difficult, we do know the risks: ML has already disrupted several industries, including e-commerce, autonomous driving and customer engagement. Thus, big data has helped us answer questions we already knew to ask, questions such as, "What more can I sell to my customers?" Machine Learning Uses Data We Don't Yet Have Analytics and business intelligence extract information from structured data (i.e., data stored in databases: customer information, purchase history, etc.).
I started biking on a regular basis last summer. Most of the work in machine learning is focused on things like smart home integrations, automated cars, Facebook bots, and apps that make it easier to travel or check the weather forecast. It plans to focus on what Jake Sigal, the CEO for Tome Software, told me is the most common type of accident -- a car hitting a bike from the side or behind. It will correlate several data sets -- in the most common areas where a bike could be in danger, the AI will examine factors like time of day (bright sun at dawn), road characteristics (such as the speed limit and berm width), and existing crash data from cars and cyclists in that area.
To look for the culprit for such disappointing results, one need only look for the strong (if not direct) correlation between user experience, retention, revenue, and app performance. This shift means digital teams are currently struggling to deliver high-quality digital experiences that delight users in a way that's fast and engaging. It turns out, in testing and monitoring of the digital experience, AI and analytics can be critical – but perhaps not in the way you may be thinking. AI and analytics will be the catalysts to deliver true test automation that recommends the tests to carry out, learns continuously, enabling it to predict business impacts, and enabling dev teams to fix issues before they occur.
We are at the limits of the data processing power of traditional computers and the data just keeps growing. That's why there's a race from the biggest leaders in the industry to be the first to launch a viable quantum computer that would be exponentially more powerful than today's computers to process all the data we generate every single day and solve increasingly complex problems. Once one of these industry leaders succeed at producing a commercially viable quantum computer, it's quite possible that these quantum computers will be able to complete calculations within seconds that would take today's computers thousands of years to calculate. That will be critical if we are going to be able to process the monumental amount of data we generate and solve very complex problems.