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) …
With populations growing, health systems are quickly becoming overburdened, under-resourced and not equipped for the challenges they face in today's fast-paced world. In the U.K., 24% of emergency admissions can be avoided through effective community care and case management solutions. And 58% of these are respiratory conditions alone, costing up to £165 billion. Delayed diagnosis can be both life-threatening and life-changing, but how can this be solved? U.K.-based technology startup Feebris thinks it's found the answer.
Do you want to have a huge impact on the largest eCommerce website? Are you interested in solving cutting edge research problems while impacting all eBay advertising channels? Does working with Big Data, cloud computing, large-scale optimization, probabilistic inference, and machine learning excite you? If you answered yes, the Marketing Science team at eBay is the right place for you. We are looking for rockstar Data Scientists to join our team.
On professional sites, eggheads are discussing how technology such as machine learning or ML has impacted businesses and the prospects of career growth in India. Some others are racking their brains, trying to dissect how machine learning is going to shape the employment scenario in India. The point is that it will certainly impact the employment scenario; the only debate is about just how much and to what extent? Not unnaturally, machine learning is currently a topic of discussion among industry leaders, students, and in academia. The impact is such that ML is consistently in the list of LinkedIn's top emerging jobs every year.
Technology's advance into all industries and jobs tends to send ripples of worry with each evolution. It started with computers and continues with artificial intelligence, machine learning, IoT, big data and automation. There are conflicting views on how new technology will impact the future of jobs. But it's becoming clear that humans will need to work with technology to be successful -- especially as it relates to the hiring process. There's a great example of this explained by Luke Beseda and Cat Surane, talent partners for Lightspeed Ventures.
This dissertation proposes efficient algorithms and provides theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine learning. Specifically, the focus is on two types of non-convex optimization problems: learning the parameters of latent variable models and learning in deep neural networks. Learning latent variable models is traditionally framed as a non-convex optimization problem through Maximum Likelihood Estimation (MLE). For some specific models such as multi-view model, it's possible to bypass the non-convexity by leveraging the special model structure and convert the problem into spectral decomposition through Methods of Moments (MM) estimator. In this research, a novel algorithm is proposed that can flexibly learn a multi-view model in a non-parametric fashion.
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.
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.
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.