Artificial intelligence (AI) and robots continue to creep into our working lives. Not only are truck drivers, janitors, and bricklayers facing obsolescence in the near future but AI is also influencing knowledge work including paralegal, medical diagnostic and other professions that require less physical manipulation and more interpretation of data. Job automation has also extended to journalism and the creation of news and other content. It is possible that robots might infiltrate the scientific communication ecosystem in the same way. Robotics has been employed in newsrooms for several years, starting small but becoming much more sophisticated lately.

New compliance technology such as AI and intelligent tagging has the power to change compliance. Our webinar which brought together in-house experts and external subject matter specialists has shed light on the latest cutting-edge technologies and how they can help solve the many day-to-day challenges faced by compliance professionals across the globe. In today's rapidly changing regulatory landscape, it is critically important for banks and financial institutions to respond to new regulations with agility, while ensuring that the customer experience does not suffer. These dual demands put pressure on compliance departments. Compliance technology in the form of end-to-end controls capable of mitigating a multitude of financial crime risks can help to alleviate this pressure.

As J.J. Kardwell, founder and CEO of predictive marketing software company EverString, puts it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales." According to research done by Accenture, artificial intelligence is projected to improve labor productivity by as much as 40 percent. It is no surprise that investment in artificial intelligence has increased to $14.9 billion, as of 2014, according to a study conducted by the Bank of America Merrill Lynch. This is projected to increase further by at least 50 percent per year. The same study claims that the innate limitations of humans will make AI the core technology of the so-called "internet of things."

Artificial intelligence (AI) technology is progressing at a rapid pace, as is the application of the technology to solve real-world problems. While the market for chipsets to address deep learning training and inference workloads is still a new one, the landscape is changing quickly – in the past year, more than 60 companies of all sizes have announced some sort of deep learning chipset or intellectual property (IP) design. A new report from Tractica finds that virtually every prominent name in the technology industry has acknowledged the need for hardware acceleration of AI algorithms and the semiconductor industry has responded by offering a wide variety of solutions. Tractica forecasts that the market for deep learning chipsets will increase from $1.6 billion in 2017 to $66.3 billion by 2025. System-on-a-chip (SoC) accelerators such as those found in mobile devices will lead the market in terms of sheer volumes by the end of the forecast period, followed by application-specific integrated circuits (ASICs) and graphics processing units (GPUs).

Sure, the formerly-cutting-edge virtual assistant has become the butt of many a tech joke, but Apple's Siri problem is only getting bigger. As Amazon and Google develop artificial-intelligence assistants that can do tasks small (set multiple cooking timers) and large (hold conversations with you and others), Siri has been stuck saying, "Here's what I found on the Web for that." At Apple's biggest software event of the year Monday, just four months after HomePod's release, the speaker was mentioned only once. Apple does have some new ideas about what Siri should do for us when iOS 12 launches for iPhones and iPads in the fall: We'll be able to program strings of tasks. A single custom phrase will, say, shut off the lights, turn down the thermostat and launch a podcast. But it feels like the ultimate marketing fake out.

Sony has unveiled the RX100 VI 20.1-megapixel premium compact camera with a new telephoto lens that will make it a lot more interesting for travel photography. T lens of the last model, the Mark VI packs a hugely longer, optically stabilized 24-200 f/2.8-4.5 T mm 8.3X zoom lens. T lens, but the extra range will be worth it for many folks, and Sony has boosted other specs to make up for it. The RX100 VI is equipped with a 1.0-type Exmor RS CMOS sensor with a built in DRAM chip to maximize speed. It can can shoot at 24 fps with full AF/AE tracking as before, but now focuses in just 0.03 seconds compared to 0.05 seconds. Moreover, eye-tracking Eye AF focus is twice as fast as on the RX100 V.

Even though I just skimmed it so far, I think this is a great paper! I think the structure of this framework seems very clean and reasonable. However, I wonder what benefits there are in using this over probabilistic programs. As far as I can tell programs have far greater expressive power, but I can see that it's easier for a computer to reason about a graph than a program, and therefore optimize it further.

Here is the June 2018 issue of Computer Vision News, the magazine of the algorithm community published by RSIP Vision: 46 pages worth reading about Computer Vision, Image Processing, Deep Learning and Artificial Intelligence.

Would you set the dropout probability for every layer to the same value? I've seen this in some papers and I assume it is because this mininizes the hyperparameters which need to be optimized. What do you think about this?

The 18th-century Irish philosopher Bishop George Berkeley concluded that, since all we know of the universe is what our senses convey to us, things in the world exist only to the extent that we perceive them. They have no material reality, but are phenomena in and of our minds, or the mind of God. Samuel Johnson famously countered this philosophy by kicking a large stone and saying, "I refute it thus!" Two hundred years later, while American campuses roiled with protests against the Vietnam War, the philosopher, historian, and physicist Thomas Kuhn met with a grad student at Princeton's legendary Institute for Advanced Study to discuss the student's paper. The professor and student disagreed on some fundamental ideas, and the conversation grew heated.