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) …
Its revenue jumped 50 per cent to $166.5 million for the year to December 31, underlying earnings before interest, tax, depreciation and amortisation rose 62 per cent to $28.1 million and underlying net profit after tax rose 86 per cent to $19.7 million. RBC Capital Markets equity research vice-president Paul Mason said Appen had historically been conservative in its forecast numbers at the full-year end and often ended up upgrading its estimates following its half-year results – which he believed could be contributing to the company's recent share price surge. "Its guidance in February shocked the market and was way ahead of consensus. At the AGM a month ago it said it's trending to the upper end of its guidance and that was predicated on a currency number, which means it could exceed it further," he said. "The market had been too bearish and it's realising this and re-examining the stock right now."
If you've spent much time on crypto Twitter within the past couple of days, you may have seen some discussion and even hysteria regarding a recently-mined block. Block #528249's six leading characters were "21e800", a phrase that has grabbed the attention of conspiracists and curious individuals throughout the cryptocurrency space. "An Exceptionally Simple Theory of Everything" is often referred to as E8 Theory, the mathematical model on which it is based. The Theory of Everything asserts that the interaction of all forces within the universe can be explained through a single mathematical model. It was first introduced in 2007, and remains unproven.
You can find links to all of the posts in the introduction, and a book based on the R series on Amazon. This blog post is a brief introduction to using the Keras deep learning framework to solve classic (shallow) machine learning problems. It presents a case study from my experience at Windfall Data, where I worked on a model to predict housing prices for hundreds of millions of properties in the US. I recently started reading "Deep Learning with R", and I've been really impressed with the support that R has for digging into deep learning. However, now that I'm porting my blog series to Python, I'll be using the Keras library directly, rather than the R wrapper.
Few user companies and organisations are putting artificial intelligence (AI) to work at significant scale, according to a McKinsey Global Institute (MGI) discussion paper. It shows AI adoption outside the technology sector to be exiguous and experimental, deployed commercially in only 12% of 160 use cases. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address.
The evolution of computing and cost efficiency has made commercial devices capable of running full-on operating systems and complex algorithms, right in the office. IoT platforms in 2018 are continuing to push for the fastest connectivity. That's of course where the concept of Edge Computing comes in, where workload is processed on the edge of the network where the IoT connects the Cloud with the physical world. A key part of this progression is the fast and effective integration between IoT and the Cloud, locating many of the processes onboard the devices themselves and connecting them with the Cloud for the most essential functions. As machine learning algorithms evolve and advance, there are a few things we can expect.
By redirecting focus, wealth managers can successfully respond to challenges brought on by digital disruption, demographic shifts, and tighter regulation. Wealth managers have seen their fair share of ups and downs in recent years, and while challenges remain, advisers can drive business and growth by paying attention to demographic segmentation, how investors are using technology, and changes in regulation. In this episode of the McKinsey Podcast, Simon London first speaks with PriceMetrix chief customer officer Patrick Kennedy and McKinsey partner Jill Zucker about the North American wealth-management industry; he follows that with a discussion with senior partner Joe Ngai, on the industry in China. Simon London: Welcome to the McKinsey Podcast with me, Simon London. Today, we're going to be talking about financial advice and the people who provide it: financial advisers, or as they're sometimes known, wealth managers. Wealth management is a very big business--and also a business facing a number of challenges, such as new technology, changing demographics, and tighter regulation in a lot of countries. A little later, we're going to be getting a perspective on China. But we're going to start here in North America. For the first part of the conversation, I'm joined on the line by Jill Zucker, a McKinsey partner based in New York, and Patrick Kennedy, who's based in Toronto. Pat is chief customer officer for PriceMetrix, which provides data and analytics to the wealth-management industry.
With the power of artificial intelligence (AI) and machine learning, banks and other FIs have an opportunity to help consumers manage their budgets and plan for big life events. Find out how the industry's most innovative companies are using these technologies to build financial solutions and apps to attract and engage today's evolving consumer. In our everyday lives, it can be difficult to monitor our spending habits and see how they impact our overall financial health. According to Abe AI founder and chief operating officer Keith Armstrong, that's the kind of burden AI can help alleviate. Abe AI works with financial institutions to offer AI-powered voice and chat applications for customers who are looking for a virtual coach.
The automotive sector will see more transformation in the coming decade than in the last 50 years combined. Although the industry is already adapting to trends such as electric mobility, autonomous vehicles, and digitalization, the unprecedented pace of change shows no sign of slowing. As income streams switch away from hardware to software-based solutions, and blockchain-powered marketplaces for buying and selling data become increasingly prominent, we predict that data monetization will be a key revenue driver for the automotive industry in the future. By 2027, big data is expected to be worth up to one trillion dollars. Over the same timeframe, technologies based on the blockchain (a highly secure register of digital financial transactions that works without any central authority) may have become so ubiquitous that they store more than ten percent of global GDP.
The young companies were all selected for their potential to become leaders, based on founder experience, investment history, growth and general "buzz." The remaining 11 companies on the list fell in a variety of verticals, including drones, fintech, education, healthcare and business software. The three dominant technology fields occupy unique market maturities relative to one another. In the wake of major breaches and increased threats, cybersecurity has emerged as one of the top priorities for business leaders. An overloaded marketplace filled with too many vendors makes it difficult for companies to stand out and customers to choose an offering.
AI-as-a-service (AIaaS) is becoming increasingly popular, with the likes of Amazon AI (which includes Rekognition), Clarifai, Google Cloud Vision, IBM Watson, and Microsoft Cognitive Services gaining traction. One of the main economic drivers within AIaaS is the prevalence of microtransactions. Visual cognition startup CloudSight has announced that it will now support Bitcoin Lightning payments, accepting microtransactions to gather and share visual knowledge to allow AI to learn from AI. CloudSight utilizes data to train deep learning neural networks to automatically caption images. With a database of over half a billion images and all the associated metadata, CloudSight says its image recognition API is one of over 30 patents pending worldwide. The incorporation of Bitcoin Lightning means that microtransactions between devices can happen at great speed, unlocking an exchange of information that was previously difficult.