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) …
We've heard the fable of "the self-made billionaire" a thousand times: some unrecognized genius toiling away in a suburban garage stumbles upon The Next Big Thing, thereby single-handedly revolutionizing their industry and becoming insanely rich in the process -- all while comfortably ignoring the fact that they'd received $300,000 in seed funding from their already rich, politically-connected parents to do so. In The Warehouse: Workers and Robots at Amazon, Alessandro Delfanti, associate professor at the University of Toronto and author of Biohackers: The Politics of Open Science, deftly examines the dichotomy between Amazon's public personas and its union-busting, worker-surveilling behavior in fulfillment centers around the world -- and how it leverages cutting edge technologies to keep its employees' collective noses to the grindstone, pissing in water bottles. In the excerpt below, Delfanti examines the way in which our current batch of digital robber barons lean on the classic redemption myth to launder their images into that of wonderkids deserving of unabashed praise. This is an excerpt from The Warehouse: Workers and Robots at Amazon by Alessandro Delfanti, available now from Pluto Press. Besides the jobs, trucks and concrete, what Amazon brought to Piacenza and to the dozens of other suburban areas which host its warehouses is a myth: a promise of modernization, economic development, and even individual emancipation that stems from the "disruptive" nature of a company heavily based on the application of new technology to both consumption and work.
Financial services were one of the first industries to see the potential of the Big Data revolution and the wave of new technology that has accompanied it – including AI. AI is a powerful technology that is already being used extensively in the financial services industry. It has a lot of potentials to make a big difference if firms use it with enough caution, wisdom, and care. Artificial intelligence (AI) is on its way to becoming mainstream in the financial services industry shortly. FinTech firms are more likely to utilize AI to develop new goods and services, whereas incumbents are more likely to improve current ones. An increasing number of FinTechs are approaching AI deployment from a product standpoint, offering AI-enabled services as a service.
New Delhi [India], November 26 (ANI/NewsVoir): Artificial intelligence has emerged as one of the biggest disruptors and game changers in the real estate landscape today, enabling a strategic, and empowered buying and selling experience. With the potential to carry out massive technological reforms across the sector AI is driving change with a technology-led immersive experience made possible just at the click of a button. These views were expressed by eminent leaders from the industry at'Leveraging AI in the Real Estate Landscape', a webinar organized by Techarc, a leading technology analytics, research and consultancy firm in association with Compass, the overseas development centre of Urban Compass Inc., a US-headquartered technology platform leading change with new age technologies such as AI & ML in the real estate industry. The panel called for leveraging the power of AI and its potential to transform the real estate landscape especially in India with appropriate investments. Incorporating data and AI based algorithms is enabling leading real estate platforms like Compass, in decision making process and at the same time is also assisting them in managing the substantial volumes of historic data that has been generated within the industry over the years and monitor bespoke KPIs in order to expedite procedures and extract useful data.
In recent years, artificial intelligence programs have been prompting changes in computer chip designs, and novel computers have made new kinds of neural networks in AI possible. There is a powerful feedback loop going on. In the center of that loop sits software technology that converts neural net programs to run on novel hardware. And at the center of that sits a recent open-source project gaining momentum. Apache TVM is a compiler that operates differently from other compilers.
The paper "World Models" is available here: https://arxiv.org/abs/1803.10122 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dennis Abts, Emmanuel, Eric Haddad, Esa Turkulainen, Geronimo Moralez, Lorin Atzberger, Malek Cellier, Marten Rauschenberg, Michael Albrecht, Michael Jensen, Nader Shakerin, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Torsten Reil.
No news or research item is a personal recommendation to deal. All investments can fall as well as rise in value so you could get back less than you invest. Nvidia reported record third quarter revenue of $7.1bn, up 50% year-on-year, with particularly strong growth in data centres and professional visualization. Operating profits rose 91% to nearly $2.7bn, with only a modest increase in sales, general & administrative expense. The group plans to pay dividends of $0.04 cents per share in the final quarter.
When we started our IDP business, our assumption was that cost savings would be the main driver for purchasing decisions -- in other words, how we can help our customers save 30–50 percent on data processing costs. But the last six years have taught us those cost savings are the number two priority for most companies looking for IDP solutions. Our customers have taught us that the number one thing is being able to effectively scale their operations. Today, data processing is heavily dependent on manual labor. We've worked with customers who were worried about their business picking up more volume because they just did not have the capacity to handle that increase.
Artificial intelligence (AI), where machines can effectively mimic the cognitive function of the human mind, is the driving force behind machine and deep-learning technology. Simply put, AI is a set of digital tools that make machines smarter, allowing them to perform certain automated activities that would normally require human intelligence.
A new research tool launched by buyer's agency network BuyersBuyers promises to take the guesswork out of suburb selection by using artificial intelligence to match a purchaser's budget with their best prospects for capital growth. BuyersBuyers co-founder Pete Wargent said the unique Where to Buy tool provided answers on which location and what sort of property would be the best choice for investors or owner-occupiers under a specific budget. "We've created a simple online process that improves the customer journey, and can help buyers to reduce time, cost and stress in their search," Mr Wargent said. The tool, which was developed in collaboration with RiskWise Property Research, assesses metrics including housing supply, median values, 12-month price growth and vacancy rates to determine whether the locations would provide risky or rewarding prospects for investment. RiskWise Property Research chief executive Doron Peleg said the new offering would complement a suite of research tools developed in conjunction with BuyersBuyers that were free for subscribers. "For example, for 2022, we ran a list of thirty suburbs which are expected to perform well for investors with a budget of up to around $1 million," Mr Peleg said.
As artificial intelligence (AI) becomes more ubiquitous, organisations are starting to encounter a big issue: explainability. European law requires that organisations need to be able to explain decisions about individuals, such as whether to grant a loan, extend a line of credit, or even to start a fraud investigation. This is straightforward when the decisions are being made by people following a set of rules. They can pinpoint the precise reason for the outcome. It is also relatively straightforward when you are using algorithms that follow rules: again, you can easily identify the sticking point.