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) …
You go around a curve, and suddenly see something in the middle of the road ahead. Of course, the answer depends on what that'something' is. A torn paper bag, a lost shoe, or a tumbleweed? You can drive right over it without a second thought, but you'll definitely swerve around a pile of broken glass. You'll probably stop for a dog standing in the road but move straight into a flock of pigeons, knowing that the birds will fly out of the way.
Machine Learning (ML), the subset of artificial intelligence, is gaining fresh momentum. Powerful and affordable computational processing, growing volumes of huge data sets, affordable data storage options and the ability to automatically apply complex mathematical calculations to big data faster than before are the factors responsible for the resurging interest. ML is basically the idea of training machines to recognize patterns in data and apply them to particular problems. The iterative aspect of machine learning is important because when models are exposed to new data, they are able to independently adapt. They learn from previous computations and predictions to produce reliable, repeatable decisions and results with minimal or no human intervention.
Believe it or not, AI is here to make life better. While some people still struggle with that idea, AI-driven technologies are busy flowing beyond the borders of tech organisations and into the business world. For now, that flow is still a trickle, but it's filtering down to some of the real corporate heavyweights, from Amazon and its AI-powered Kiva warehouse robots, to GE and its predictive maintenance that keeps industrial equipment running like a dream.
The introduction and application of Artificial Intelligence (AI) in the realm of marketing has spawned the era of the customer-centric data-driven modern marketer. At a time when brands generate gargantuan amounts of customer data that is seldom leveraged optimally, AI comes as a timely shot in the arm for marketers endeavouring to deliver exceptional customer experiences at scale. Prima facie, the computational levers and infrastructure that go behind embedding AI into the paradigm of technology-enabled marketing might seem overwhelming, creating the impression that it is the "machines" that erode the human element, but that's far from the truth. AI actually makes modern marketing more human by disrupting traditional monolithic marketing that relied heavily on mass broadcast communication with an inadequate, and often, inaccurate understanding of the target market. These practices relied more on generic trial-and-error where campaigns either created resonance with certain segments or breathed a brand disconnect with other segments, owing to the impersonal nature of the communication.
Artificial intelligence is everywhere: it promises to power fleets of self-driving vehicles, open up endless new business opportunities and even be the key to global power. Amid all the bluster it's easy to lose sight of the genuine opportunity AI provides. The term "artificial intelligence" was coined back in 1956, at the Dartmouth Conference. It was intended to encapsulate the idea that every aspect of learning, or any other feature of intelligence, can be so precisely described that a machine can be made to simulate it. Fast forward 60-plus years: today, AI has become one of the hottest -- and overhyped -- tech concepts on the planet.
Business today is more than simply matching traditional competitors, it's about exploiting digital technologies to create new opportunities, and being able to repeat this. The economy is quickly going digital and Australian businesses must evolve into Modern Digital Businesses (MDBs) which strategically use intelligence assets to improve operations and deploy new products and services, in order to stay competitive and create value for their customers. A group of digital business leaders recently gathered at ThoughtWorks Live in Sydney and Melbourne, to share their insights into how organisations can take advantage of data to adapt and thrive in the digital economy. This report includes strategic and practical advice taken from the event for any business leader – regardless of their organisation's digital maturity – on best practices for taking advantage of data and driving change. A Continuous Intelligence (CI) framework starts with the process of acquiring data and, with the help of analytics and machine learning, derive insights from it to be able to make confident decisions and actions – which are in turn reviewed and validated, to ensure the organisation continuously improves its decision-making capabilities. Steps organisations can take to apply CI to building an MDB, which is agile and technology-driven are also covered.
To train this AI, we only need to input articles, books, languages, and internet sources. Therefore, the bias and complexity could be reduced and minimized. The model could be used for general purpose selection. Apart from that, the existing voice recognizers and chatbots can support better candidate communication. The aim of first calls by contingent firms is simply laying out the basic job descriptions and asking for a YES or NO, which chatbots could easily handle.
When we talk about technology and innovation, India's growth story has always been inspiring. Today, start-ups have evolved, and innovation is the hot button that is driving the nation's business ecosystem. From a business perspective, concepts like e-cars, ridesharing, cab aggregation, hotel aggregation or any kind of shared economy is gaining significant focus with its growing user adoption. These domains have achieved maturity in the growth curve and thus provide the industry with a new set of opportunities and challenges. That is not all, even the stakeholders, customers and the employees in this ecosystem are also in the maturity cycle of the growth curve.
BOSTON – The excitement around artificial intelligence is genuine. The promise and potential of what it can do for healthcare is very real. But there's still a lot of unrealistic expectation of what AI and machine learning will mean for care delivery, said Dr. Anthony Chang. Chang, chief intelligence and innovation officer at Children's Hospital and Orange County, delivered the Day 2 keynote address at the HIMSS Machine Learning & AI for Healthcare event here in Boston. There's already a lot going on with the technology in hospitals, as evidenced by the numerous case studies presented here these past two days.
After years of hype around AI and machine learning, skepticism, and a focus on practical applications of the technology are now taking center stage. In the security industry, this was abundantly clear at the recent RSA Conference where 45,000 people and a thousand vendors descended on San Francisco to discuss industry challenges and debate over the best solutions. Despite the many voices contending for attention at the show, there was little to no dispute that the cybersecurity skills gap continues to be one of the industry's biggest challenges. But, here's what is next for AI. An (ISC)² report released during the conference says there are 2.93 million cybersecurity positions open and unfilled around the world.