We live in an era when rapid advances in robotics and artificial intelligence are colliding with an expanding conception of sexual identity. This comes quickly on the heels of growing worldwide acceptance of gay, trans and bisexual people. Now you may describe yourself as polyamorous or demisexual -- that last one is people who only feel sexual attraction in close emotional relationships. Perhaps you best identify as aromantic (that's people who don't feel romance) or skoliosexual (that's a primary attraction to people of no, or multiple, or complex genders). Self-identification is not the same as identity, and some classes of description now may be closer to metaphor.
Online retailers face a whole host of relatively unique search engine optimization (SEO) considerations most other websites don't deal with. Most discussions about those differences focus on issues such as tags, uniform resource locators (URLs), link structure, duplicate content and so on. In this post, I want to zoom out a bit and start talking about strategic approaches. I've decided to focus specifically on three factors that deserve special attention in developing an SEO strategy for online retailers: keyword research, mobile crawling and customer reviews. This is by no means an exhaustive list, but I believe it's a useful starting point.
A growing number of lenders are using artificial intelligence to digest growing volumes of data and find relationships between variables to determine creditworthiness. Last year, subprime auto lender Prestige Financial Services started working with artificial intelligence (AI) software developer ZestFinance to analyze about 2,700 borrower characteristics, instead of the several dozen the lender had on its risk-assessment scorecard. Draper, UT-based Prestige is among a growing number of lenders that view AI as a tool that can digest increasing volumes of data and find relationships between variables to determine creditworthiness. Prestige and ZestFinance developed a machine learning system that allowed Prestige to consider factors such as when a bankruptcy happened, previous car-payment records, and time spent living at a current residence. Said ZestFinance's Douglas Merrill, "If you're building an AI model, you can have hundreds or thousands" of such indicators, including whether people have defaulted on rent payments or cellphone bills.
Software developers generally want to work with reusable abstractions instead of single-purpose methods. With GraphQL, we define each piece of data once and define how it relates to other data in our system. When the consumer application fetches data from multiple sources, it no longer needs to worry about the complex business logic associated with data join operations. Consider the following example, we define entities in GraphQL exactly once: catalogs, creatives, and comments. We can now build the views for several pages from these definitions. One page on the client app (catalogView) declares that it wants all comments for all creatives in a catalog while another client page (creativeView) wants to know the associated catalog that a creative belongs to, along with all of its comments. The same graph model can power both of these views without having to make any server side code changes.
Facebook could soon set a new record -- just not the good kind. The Federal Trade Commission is reportedly considering a "record-setting" fine for the social network, as the result of its investigation into Facebook's privacy practices following the Cambridge Analytica scandal, which revealed Facebook had exposed the personal data of millions of users. The FTC confirmed it had opened an investigation into Facebook in March. Now, The Washington Post reports that investigation could result in a massive fine for the company. "The penalty is expected to be much larger than the $22.5 million fine the agency imposed on Google in 2012," according to The Post.
When our machines first began speaking to us, it was in the simple language of children. Some of those voices were even designed for kids -- my Speak & Spell was a box with a handle and a tiny green screen that tested my skills in a grating tone, but I still heard that voice sometimes in my dreams. Teddy Ruxpin's words played from cassette tapes popped into his back, but his mouth moved at just the right cadence, which made him feel almost alive. For adults, however, the clunky computerized voices of the 1980s, '90s, and early aughts were far from real. When the train's voice announced that the next stop was Port Chester using two words instead of "porchester" -- we knew: That was a machine.
In the world of science and technology, it is being said that we are at the beginning of the Fourth Industrial Revolution. The first that lasted from 1760 to 1840 brought in the age of mechanised production. It was the result of new materials like iron and steel, which combined with new energy resources of coal and steam, led to'mass production', and a factory system with division of labour. The second industrial revolution from 1870 to the early part of 20th century was the result of electricity, and the internal combustion engine. Both powered industrial machines and made transport possible.
If you want to see what the future of iron to support machine learning looks like, then perhaps the best place to look at what the hyperscalers and cloud builders who account for the vast majority of processing and applications in this field are deploying. Or, more precisely, look at the iron that their ODM partners are trying to peddle to other companies that is inspired by what the hyperscalers and cloud builders are using. Inspur, one of the upstart makers of infrastructure that is located in China but which is expanding outwards to North America and Europe, is a good case in point. The company has very good insight into what the Big Four in China – Alibaba, Baidu, Tencent, and either China Mobile or JD.com, depending on how you want to rank numbers four and five – are doing with their vast infrastructure, and it dominates some of these accounts. As we reported back in October 2018, when Inspur was making a push into Open Compute, Inspur has about half of the plain vanilla server shipments and about 80 percent of the GPU accelerated machine learning shipments to the hyperscalers and cloud builders in China.
A neural network looks at a picture of a turtle and sees a rifle. A self-driving car blows past a stop sign because a carefully crafted sticker bamboozled its computer vision. An eyeglass frame confuse facial recognition tech into thinking a random dude is actress Milla Jovovich. The hacking of artificial intelligence is an emerging security crisis. Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning.
There has been a lot of excited talk recently about the threat to jobs posed by automation, robots, and now Artificial Intelligence (AI): machines that can think like humans. We're told that ever more complex tasks can now be automated and perhaps done better as a result, and we should all be preparing for a world in which we're competing for work with computers. Is teaching one of the jobs put at risk by the emergence of AI? Or does AI have potential to enhance life in the classroom? A recent event organised by BESA, the industry body for education suppliers, provided plenty of food for thought about these questions. He argued that AI can help us move away from the "factory model of education" towards a more open-ended system focused on creativity and problem solving – and he said we're seeing early signs of what technology can bring us in innovations such as "no lecture hall" universities and courses offering "nanodegrees".