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) …
The Entrepreneurship Centre at Cambridge Judge supports new programme that uses artificial intelligence to tackle major NHS challenges. The Entrepreneurship Centre at Cambridge Judge Business School and the Eastern Academic Health Science Network (Eastern AHSN) are sponsoring a new programme that brings together the health sector, universities and the tech industry to use artificial intelligence to tackle major challenges in the National Health Service. Applications are now open for the programme, called MedTechBOOST, which has been launched by business network Central Working and innovation specialists Studio Zao. The five day, free-to-enter programme starts on 2 September 2019 at The Bradfield Centre, the tech hub at Cambridge Science Park. It is open to startups, technologists, researchers, PhDs, consultants and other clinicians who would like the opportunity to come together with supporters and partners to co-create solutions that address two of the biggest challenges facing the NHS today – mental health and healthy ageing.
The most important step is undoubtedly the preparation. As much as 80% of a Data Scientist's time is spent in connecting the data, cleansing it, normalising and preparing it for different use cases, reviewing code that doesn't work as it should, across multiple programing and modelling platforms and subsequently questioning their career choice. The remaining 20% is building something new and exciting, Gartner now defines AI as "applying advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action". True AI should solve problems you don't know exist, but until that is the case you can use AI to identify problems better and faster and then separate to the issues solve them faster but you need Data Scientist to tie the issue and the solution together.
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.
In the modern workplace, technology is the real game changer. The majority of businesses today are inclined towards investing in automated systems that are powered by artificial intelligence. The idea is to access data in real-time that boosts employee productivity. Application of automated data engineering is no longer a vision. It is a reality that has long-term advantages by transforming employee performance within a business environment.
"The big difference," says Stephen Schwarzman, the impossibly rich boss of the private equity group Blackstone (personally worth an estimated $13bn), "is speed." He's talking about industrial revolutions, the latest of which is widely held to be driven by the ever-growing capability and decision-making power of Artificial Intelligence (AI). Unlike the social transformations wrought by steam, or steel, or the internal combustion engine, he says, we only have a single blink-and-you've-missed-it opportunity to direct AI for good. By contrast "it took a long time to have an industrial revolution in the 19th century." He is speaking on the day it is announced that he has donated £150m to Oxford University...
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.
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.