Monthly Archives: July 2020

Cognitive deficiency tests, AI, and self-driving cars.

Seems like the news recently has been filled with revelations about the taking of cognitive deficiency tests.

This is especially being widely noted by some prominent politicians that appear to be attempting to vouch for having mental clarity upon reaching an age in life whereby cognitive decline often surfaces.

Such tests are more aptly referred to as cognitive assessment tests rather than deficiency oriented tests, though the notion generally being that if a score earned is less than what might be expected, the potential conclusion is that the person has had a decline in their mental prowess.

Oftentimes also referred to as cognitive impairment detection exams, the person seeking to find out how they are mentally doing is administered a test consisting of various questions and asked to answer the questions. The administrator of the test then grades the answers as to correctness and fluidity, producing a score to indicate how the person overall performed.

The score is then compared to the scores of others that have taken the test, trying to gauge how the cognitive capacity of the person is rated or ranked in light of some larger population of test-takers.

Also, if a person takes the test over time, perhaps say once per year, their prior scores are compared to their most recent score, attempting to measure whether there is a difference emerging as they age.

There are some crucial rules-of-thumb about all of this cognitive test-taking.

For example, if the person takes the same test word-for-word, repeatedly over time, this raises questions about the nature of the test versus the nature of the cognitive abilities of the person taking the test. In essence, you can potentially do better on the test simply because youve seen the same questions before and likely also had been previously told what the considered correct answers are.

One argument to be made is that this is somewhat assessing your ability to remember having previously taken the test, but thats not usually the spirit of what such cognitive tests are supposed to be about. The idea is to assess overall cognition, and not merely be focused on whether you perchance can recall the specific questions of a specific test previously taken.

Another facet of this kind of cognitive test-taking consists of being formally administered the test, rather than taking the test entirely on your own.

Though there are plenty of available cognitive tests that you can download and take in private, some would say that this is not at all the same as taking a test under the guiding hands and watch of someone certified or otherwise authorized to administer such tests.

A key basis for claiming that the test needs to be formally administered is to ensure that the person taking the test is not undermining the test or flouting the testing process. If the test taker were to ask a friend for help, this obviously defeats the purpose of the test, which is supposed to focus on your solitary cognition and not be a collective semblance of cognition. Likewise, these tests are usually timed, and a person on their own might be tempted to exceed the normally allotted time, plus the person might be tempted to look-up answers, use a calculator, etc.

Perhaps the most important reason to have a duly authorized and trained administrator involves attempting to holistically evaluate the results of the cognition test.

Experts in cognitive test-taking are quick to emphasize that a robust approach to the matter consists of not just the numeric score that a test taker achieves, but also how they are overall able to interact with a fully qualified and trained cognitive-test administrator.

Unlike taking a secured SAT or ACT test that you might have had to painstakingly sit through for college entrance purposes, a cognitive assessment test is typically intended to assess in both a written way and in a broader manner how the person interacts and cognitively presents themselves.

Imagine for example that someone aces the written test, yet meanwhile, they are unable to carry on a lucid conversation with the administrator, and similarly, they mentally stumble on why they are taking the test or otherwise have apparent cognitive difficulties surrounding the test-taking process. Those facets outside of the test itself should be counted, some would vehemently assert, and thus would be unlikely to be valued if a person merely took the test on their own.

Despite all of the foregoing and the holistic nuances that Ive mentioned, admittedly, most of the time all that people want to know is what was their darned score on that vexing cognitive test.

You might be wondering whether there is one standardized and universal cognitive test that is used for these purposes.

No, there is not just one per se.

Instead, there are a bewildering and veritable plethora of such cognition tests.

It seems like each day there is some new version that gets announced to the world. In some cases, the cognitive test being proffered has been carefully prepared and analyzed for its validity. Unfortunately, in other cases, the cognitive test is a gimmick and being fronted as a moneymaker, whereby those pushing the test are aiming to get people to believe in it and hoping to generate gobs of revenue by how many take the test and charge them fees accordingly.

Please do not fall for the fly-by-night cognitive tests.

Sadly, sometimes a known celebrity or other highly visible person gets associated with a cognitive test promotion and adds a veneer of authenticity to something that does not deserve any bona fide reputational stamp-of-approval.

Some cognitive tests have lasted the test of time and are considered the dominant or at least well-regarded for their cognitive assessing capacity and validity.

On a related note, if a cognitive test takes a long time to complete, lets say hours of completion time, the odds are that it is not going to be overall well-received and considered onerous for testing purposes. As such, the popular cognitive tests tend to be the ones that take a relatively short period to undertake, such as an hour or less, and in many cases even just 15 minutes or less (these are usually depicted as screening tests rather than full-blown cognitive assessment tests).

Some decry that only requiring a few minutes to take a cognitive test is rife with problems and seems like a fast-food kind of approach to tackling a very complex topic of measuring someones cognition. Those in this camp shudder when these quickie tests are used by people that then go around touting how well they scored.

The counter-argument is that these short-version cognitive tests are reasonable and amount to using a dipstick to gauge how much gasoline there is in the tank of your car. The viewpoint is that it only takes a little bit of measurement to generally know how someone is mentally faring. Once an overall gauge is taken, you can always do a follow-up with a more in-depth cognitive test.

Given all of the preceding discussion, it might be handy to briefly take a look at a well-known cognitive test that has been around since the mid-1990s and continues to actively be in use today, including having been the test that reportedly President Trump took in 2018 (according to news reports).

The Famous MoCA Cognitive Test

That test is the Montreal Cognitive Assessment (MoCA) test.

Some mistakenly get confused by the name of the test and think that it is maybe just a test for Canadians since it refers to Montreal in the naming, but the test is globally utilized and was named for being initially developed by researchers in Montreal, Quebec.

Generally, the MoCA is one-page in size (see example here), which is handily succinct for doing this kind of testing, and the person taking the test is given 10 minutes to answer the questions. There is some leeway often allowed in the testing time allotted, and also some latitude related to having the person first become oriented to the test and its instructions.

Nonetheless, the person taking the test should not be provided say double the time or anything of that magnitude. The reason why the test should be taken in a prescribed amount of time is that the aspect of time is considered related to cognitive acuity.

In other words, if the person is given more time than others have previously gotten, presumably they can cognitively devote more mental cycles or effort and might do better on the test accordingly.

A timed test is not just about your cognition per se, but also about how fast you think and whether your thinking processes are as fluid as others that have taken the test.

If it took someone an hour and they got a top score, while someone else got a top score in ten minutes, we would be hard-pressed to compare their results. You might liken this to playing timed chess, whereby the longer you have, the more chess moves you can potentially mentally foresee, which is fine in some circumstances, but when trying to make for a balanced playing field, you put a timer on how long each player has to make their move.

That being said, the time allotted for a given test should not be so short as to shortchange the cognitive opportunities, which would once again presumably hamper the measurement of cognition. A chess player that has to say just two seconds to make a move will likely randomly take a shot rather than try to devote mental energy to the task.

In theory, the amount of time provided should be the classic Goldilocks amount, just enough time to allow for a sufficient dollop of mental effort, and not so much time that it inadvertently extends the cognition and perhaps enables a lesser cognitive capacity to use time as a crutch to imbue itself (assuming thats not what the test is attempting to measure).

I am about to explain specific details of the MoCA cognitive test, so if you want to someday take the test, please know that I am about to spoil your freshness (this is a spoiler alert).

The test attempts to cover a lot of cognitive ground, doing so by providing a variety of cognition tasks, including the use of numbers, the use of words, the use of sentences, the use of the alphabet, the use of visual cognitive capabilities such as interpreting images and composing writing, and so on.

Thats worth mentioning because a cognitive test that only covered say counting and involved the addition of numbers would be solely focused on your arithmetic cognition. We know that humans have a fuller range of cognitive abilities. As such, a well-balanced cognitive test tries to hit upon a slew of what are considered cognitive dimensions.

Notably, this can be hard to pack into one short test, and raises some criticisms by those that argue it is dubious to have someone undertake a single question on numbers and a single question on words, and so on, and then attempt to generalize overall about their cognition within each respective entire dimension of cognitive facets.

Lets try out a numbers and arithmetic related question.

Are you ready?

You are to start counting from 100 down to 0 and do so by subtracting 7 each time rather than by one.

Okay, your first answer should be 93, and then your next would be 86, and then 79, and so on.

You cannot use a pencil and paper, nor can you use a calculator. This is supposed to be off the top of your head. Using your fingers or toes is also considered taboo.

How did you do?

Try this next one.

Remember these words: Face, Velvet, Church, Daisy, Red.

I want you to look away from these words and say them aloud, without reading them from the page.

In about five minutes, without looking at the page to refresh your memory, try to once again speak aloud what the words were.

What do those cognitive tests signify?

The counting backward is usually a tough one for most people as they do not normally count in that direction. This forces your mind to slow down and think directly about the numbers and the doing of arithmetics in your head (this is also partially why the same kind of quiz is used for DUI roadway sobriety assessment). If I had asked you to count by sevens starting at zero and counting upward, you would likely do so with much greater ease, and the effort would be less cognitively taxing on you.

For the word memorization, this is an assessment of your short-term memory capacity. It is only five words versus if I had asked you to remember ten words or fifty words. Some people will try to memorize the five words by imagining an image in their minds of each word, while others might string together the words into making a short story that will allow them to recall the words.

Either way, this is an attempt to exercise your cognition around several facets, involving short-term memory, the ability to follow and abide by instructions, a semblance of encoding words in your mind, and has other mental leveraging cerebral components.

Some of the questions on these cognitive tests are considered controversial.

In the case of MoCA, there is typically a clock drawing task that some cognitive test experts have heartburn about.

You are asked to draw a clock and indicate the time on the clock as being a stated time such as perhaps 10 minutes past 7. In theory, you would draw a circle or something similar, you would write the numbers of 1 to 12 around the oval to represent each hour, and you would then sketch a short line pointing from the center toward the 7, and a longer mark pointing from the center to the 2 position (since the marks for minutes are normally representative of five minutes each).

Why is this controversial as a cognitive test question?

One concern is that in todays world, we tend to use digital clocks that display numerically the time and are less likely to use the conventional circular-shaped clock to represent time anymore.

If a person taking the cognitive test is unfamiliar with oval clocks, does it seem appropriate that they would lose several cognition points for poorly accomplishing this task?

This brings up a larger scope qualm about cognitive tests, namely, how can we separate knowledge versus the act of cognition.

I might not know what a conventional clock is and yet have superb cognitive skills. The test is unfairly ascribing knowledge of something in particular to the act of cognition, and so it is falsely measuring one thing that is not necessarily the facet that is being presumably assessed.

Suppose I asked you a question about baseball, such as please go ahead and name the bases or what the various player positions are called. If perchance you know about baseball, you can answer the question, while otherwise, you are going to fail that question.

Do the baseball question and your corresponding answer offer any reasonable semblance of your cognitive capabilities?

In any case, the MoCa cognitive test is usually scored based on a top score of 30, for which the scale typically used is this:

Score 26-30: No cognitive impairment detected

Score 18-25: Mild cognitive impairment

Score 10-17: Moderate cognitive impairment

Score00-09: Severe cognitive impairment

Research studies tend to indicate that people with demonstrative Alzheimers tend to score around 16, ending up in the moderate cognitive impairment category. Presumably, a person with no noticeable cognitive impairment, at least per this specific cognitive test, would score at 26 or higher.

Is it possible to achieve a score in the top tier, the score of 26 or above (suggesting that one does not possess any cognitive impairment), and yet still nonetheless have some form of cognitive deficiency?

Yes, certainly so, since this kind of cognitive test is merely a tiny snapshot or sliver and does not cover an entire battery or gamut of cognition, plus as mentioned earlier there is the possibility of being a priori familiar with the test and/or actively prepare beforehand for the test which can substantively boost performance.

Is it possible to score in the mild, moderate, or severe categories of cognitive impairment and somehow not truly be suffering from cognitive impairment?

Yes, certainly so, since a person might be overly stressed and anxious in taking the test, thus perform poorly due to the situation at hand, or could find the given set of tasks unrelated to their cognition prowess such as perhaps someone that is otherwise ingeniously inventive and cognitively sharp, but find themselves mentally cowed when doing simple arithmetic or memorizing seemingly nonsense words.

All told, it is best to be cautious in interpreting the results of such cognitive tests (and, once again, reinforces the need for a more holistic approach to cognitive assessments).

AI And Cognitive Tests

Another popular topic in the news and one that is seemingly unrelated to this cognitive testing matter is the emergence of AI (hold that thought, for a moment, well get back to it).

You are likely numbed by the multitude of AI systems that seem to keep being developed and released into and affecting our everyday lives, including the rise of facial recognition, the advent of Natural Language Processing (NLP) in the case of AI systems such as Alexa and Siri, etc.

On top of that drumbeat, there are the touted wonders of AI, entailing a lot of (rather wild) speculation about where AI is headed and whether AI will eclipse human intelligence, possibly even deciding to take over our planet and choosing to enslave or wipe out humanity (for such theories, see my analysis at this link here).

Why bring up AI, especially if it presumably has nothing to do with cognitive tests and cognitive testing?

Well, for the simple fact that AI does have to do with cognitive testing, very much so.

The presumed goal for AI is to achieve the equivalent of human intelligence, as might somehow be embodied in a machine. We do not yet know what the machine will be, though likely to consist of computers, but the specification does not dictate what it must be, and thus if you could construct a machine via Legos and duct tape that exhibited human intelligence, more power to you.

In brief, we want to craft artificial cognitive capabilities, which are the presumed crux of human intelligence.

Logically, since thats what we are attempting to accomplish, it stands to reason that we would expect AI to be able to readily pass a human-focused cognitive test since doing so would illustrate that the AI has arrived at similar cognitive capacities.

I dont want to burst anyones bubble, but there is no AI today that can do any proper semblance of common-sense reasoning, and we are a long way away from having sentient AI.

Bottom-line: AI today would essentially flunk the MoCA cognitive test and any others of similar complexity too.

Some might try to argue and claim that AI and computers can countdown from 100, and can memorize words, and do the other stated tasks, but this is a misleading assertion. Those are tasks undertaken by an AI system that has been constructed for and contrived to perform those specific tasks, and inarguably is a far cry from understanding or comprehending the test in a manner akin to human capacities and misleadingly anthropomorphize the matter (for more details, see my analysis at this link here).

There is not yet any kind of truly generalizable AI, which some are now calling Artificial General Intelligence (AGI).

As added clarification, there is a famous test in the AI field known as the Turing Test (see my explanation at this link here). No AI of today and nor in the foreseeable near future could pass a fully ranging Turing Test, and in some respects, being able to pass a cognitive test like those of MoCA is a variant of a Turing Test (in an extremely narrow way).

AI Cognition And Self-Driving Cars

Another related topic entails the advent of AI-based true self-driving cars.

We are heading toward the use of self-driving cars that involve AI autonomously driving the vehicle, doing so without any human driver at the wheel.

Some wonder whether the AI of today, lacking any kind of common-sense reasoning and nor any inkling of sentience, will be sufficient for driving cars on our public roadways. Critics argue that we are going to have AI substituting for human drivers and yet the AI is insufficiently robust to do so (see more on this contention at my analysis here).

Others insist that the driving task does not require the full range of human cognitive capabilities and thus the AI will do just fine in commanding self-driving cars.

Do you believe that the AI driving you to the grocery store needs to be able to first pass a cognitive test and showcase that it can adequately draw a clock and indicate the time of day?

For now, all we can say is that time will tell.

Go here to see the original:

How To Flunk Those Cognitive Deficiency Tests And What This Means Too For AI Self-Driving Cars - Forbes

A new study from West Virginia University (WVU) Rockefeller Neuroscience Institute (RNI) uses a wearable device and artificial intelligence (AI) to predict COVID-19 up to 3 days before symptoms occur. The study has been an impressive undertaking involving over 1000 health care workers and frontline workers in hospitals across New York, Philadelphia, Nashville, and other critical COVID-19 hotspots.

The implementation of the digital health platform uses a custom smartphone application coupled with an ura smart ring to monitor biometric signals such as respiration and temperature. The platform also assesses psychological, cognitive, and behavioral data through surveys administered through a smartphone application.

We know that wearables tend to suffer from a lack of accuracy, particularly during activity. However, the ura ring appears to take measurements while the user is very still, especially during sleep. This presents an advantage as the accuracy of wearable devices greatly improves when the user isnt moving. RNI noted that the ura ring has been the most accurate device they have tested.

Given some of the early warning signals for COVID-19 are fever and respiratory distress, it would make sense that a device able to measure respiration and temperature could be used as an early detector of COVID-19. In fact, weve seen a few wearable device companies attempt much of what RNI is doingas well as a few DIY attempts. RNIs study has probably been the most thorough work released so far, but were sure that many more are upcoming.

The initial phase of the study was deployed among healthcare and frontline workers but is now open to the general public. Meanwhile the National Basketball Association (NBA) is coordinating its re-opening efforts using uras technology.

We hope to see more results emerge from RNIs very important work. Until then, stay safe Hackaday.

Go here to read the rest:

Detect COVID-19 Symptoms Using Wearable Device And AI - Hackaday

Company recognized among rising stars of artificial intelligence for its AI-guided robots transforming the recycling industry

Forbes has named AMP Robotics Corp. ("AMP"), a pioneer and leader in artificial intelligence (AI) and robotics for the recycling industry, one of Americas most promising AI companies. The publications annual "AI 50" list distinguishes private, U.S.-based companies that are wielding some subset of artificial intelligence in a meaningful way and demonstrating real business potential from doing so. To be included on the list, companies needed to show that techniques like machine learning, natural language processing, or computer vision are a core part of their business model and future success.

"Earlier this year, we notched a milestone of one billion picks over 12 months that demonstrates the productivity, precision, and reliability of our AI application for the recycling industry. Its an honor to be deemed one of the countrys most promising AI companies, and were just getting started," said Matanya Horowitz, AMP founder and chief executive officer. "Theres growing appreciation for the role of recycling in the domestic supply chain, in terms of keeping resources flowing and products on shelves, and resultant momentum around supportive policy initiatives that are putting some real wind in the sail for the industry. Were pleased to play a role in enabling better efficiency, safety, and transparency to help transform recycling."

AMPs technology recovers plastics, cardboard, paper, metals, cartons, cups, and many other recyclables that are reclaimed for raw material processing. AMPs AI platform uses computer vision to visually identify different types of materials with high accuracy, then guides high-speed robots to pick out and recover recyclables at superhuman speeds for extended periods of time. The AI platform transforms images into data to recognize patterns, using machine learning to train itself by processing millions of material images within an ever-expanding neural network of robotic installations.

"We consider AMP a category-defining business and believe its artificial intelligence and robotics technology are poised to solve many of the central challenges of recycling," said Shaun Maguire, partner at Sequoia Capital and AMP board member. "The opportunity for modernization in the industry is robust as the demand for recycled materials continues to swell, from consumers and the growing circular economy."

AMPs "AI 50" recognition comes on the heels of receiving a 2020 RBR50 Innovation Award from Robotics Business Review for the companys Cortex Dual-Robot System. Earlier this year, Fast Company named AMP to its "Worlds Most Innovative Companies" list for 2020, and the company captured a "Rising Star" Company of the Year Award in the 2020 Global Cleantech 100.

Since its Series A fundraising in November, AMP has been on a major growth trajectory as it scales its business to meet demand. The company announced a 50% increase in revenue in the first quarter of 2020, a rapidly growing project pipeline, a facility expansion in its Colorado headquarters, and a new lease program that makes its AI and robotics technology even more attainable for recycling businesses.

About AMP Robotics Corp.

AMP Robotics is applying AI and robotics to help modernize recycling, enabling a world without waste. The AMP Cortex high-speed robotics system automates the identification and sorting of recyclables from mixed material streams. The AMP Neuron AI platform continuously trains itself by recognizing different colors, textures, shapes, sizes, patterns, and even brand labels to identify materials and their recyclability. Neuron then guides robots to pick and place the material to be recycled. Designed to run 24/7, all of this happens at superhuman speed with extremely high accuracy. With deployments across the United States, Canada, Japan, and now expanding into Europe, AMPs technology recycles municipal waste, e-waste, and construction and demolition debris. Headquartered and with manufacturing operations in Colorado, AMP is backed by Sequoia Capital, Closed Loop Partners, Congruent Ventures, and Sidewalk Infrastructure Partners ("SIP"), an Alphabet Inc. (NASDAQ: GOOGL) company.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200710005481/en/

Contacts

Media Contact Carling Spelhaugcarling@amprobotics.com

Read more:

AMP Robotics Named to Forbes AI 50 - Yahoo Finance

Take the latest VB Survey to share how your company is implementing AI today.

The past year has seen remarkable change. As innovation in the field of AI and real-world applications of its constituent technologies such as machine learning, natural language processing, and computer vision continue to grow, so has an understanding of their social impacts.

At our AI-focused Transform 2020 event, taking place July 15-17 entirely online, VentureBeat will recognize and award emergent, compelling, and influential work in AI through our second annual VB AI Innovation Awards.

Drawn both from our daily editorial coverage and the expertise, knowledge, and experience of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.

Our nominating committee includes:

Claire Delaunay, Vice President of Engineering, Nvidia

Claire Delaunay is vice president of engineering at Nvidia, where she is responsible for the Isaac robotics initiative and leads a team to bring Isaac to market for use by roboticists and developers around the world.

Prior to joining Nvidia, Delaunay was the director of engineering at Uber, after it acquired Otto, a startup she cofounded. She was also the robotics program lead at Google and founded two other companies, Botiful and Robotics Valley.

Delaunay has 15 years of experience in robotics and autonomous vehicles leading teams ranging from startups and research labs to Fortune 500 companies. Sheholds a Master of Science in computer engineering from cole Prive des Sciences Informatiques (EPSI).

Asli Celikyilmaz, Principal Researcher, Microsoft Research

Asli Celikyilmaz is a principal researcher at Microsoft Research (MSR) in Redmond, Washington. She is also an affiliate professor at the University of Washington. She received her Ph.D. in information science from the University of Toronto, Canada, and continued her postdoc study in the Computer Science Department at the University of California, Berkeley.

Her research interests are mainly in deep learning and natural language (specifically language generation with long-term coherence), language understanding, language grounding with vision, and building intelligent agents for human-computer interaction. She serves on the editorial boards of Transactions of the ACL (TACL) as area editor and Open Journal of Signal Processing (OJSP) as associate editor. She has received several best of awards, including at NAFIPS 2007, Semantic Computing 2009, and CVPR 2019.

The award categories are:

Natural Language Processing/Understanding Innovation

Natural language processing and understanding have only continued to grow in importance, and new advancements, new models, and more use cases continue to emerge.

Business Application Innovation

The field of AI is rife with new ideas and compelling research, developed at a blistering pace, but its the practical applications of AI that matter to people right now, whether thats RPA to reduce human toil, streamlined processes, more intelligent software and services, or other solutions to real-world work and life problems.

Computer Vision Innovation

Computer vision is an exciting subfield of AI thats at the core of applications like facial recognition, object recognition, event detection, image restoration, and scene reconstruction and thats fast becoming an inescapable part of our everyday lives.

AI for Good

This award is for AI technology, the application of AI, or advocacy or activism in the field of AI that protects or improves human lives or operates to fight injustice, improve equality, and better serve humanity.

Startup Spotlight

This award spotlights a startup that holds great promise for making an impact with its AI innovation. Nominees are selected based on their contributions and criteria befitting their category, including technological relevance, funding size, and impact in their sub-field within AI.

As we count down to the awards, well offer editorial profiles of the nominees on VentureBeats AI channel The Machine and share them across our social channels. The award ceremony will be held on the evening of July 15 to conclude the first day of Transform 2020.

Go here to read the rest:

Announcing the second annual VentureBeat AI Innovation Awards at Transform 2020 - VentureBeat

Take the latest VB Survey to share how your company is implementing AI today.

VentureBeat readers likely noticed this week that our site looks different. On Thursday, we rolled out a significant design change that includes not just a new look but also a new brand structure that better reflects how we think about our audiences and our editorial mission.

VentureBeat remains the flagship brand devoted to covering transformative technology that matters to business decision makers and now, our longtime GamesBeat sub-brand has its own homepage of sorts, and definitely its own look. And weve launched a new sub-brand. This one is for all of our AI content, and its called The Machine.

By creating two distinct brands under the main VentureBeat brand, were leaning hard into what weve acknowledged internally for a long time: Were serving more than one community of readers, and those communities dont always overlap. There are readers who care about our AI and transformative tech coverage, and there are others who ardently follow GamesBeat. We want to continue to cultivate those communities through our written content and events. So when we reorganized our site, we created dedicated space for games and AI coverage, respectively, while leaving the homepage as the main feed.

GamesBeat has long been a standout sub-brand under VentureBeat, thanks to the leadership of managing editor Jason Wilson and the hard work of Dean Takahashi, Mike Minotti, and Jeff Grubb. Thus, giving it a dedicated landing page makes logical sense. We want to give our AI coverage the same treatment, which is why we created The Machine.

We chose to take a long and winding path to selecting The Machine as the name for our AI sub brand. We could have just put our heads together and picked one, but wheres the fun in that? If youre going to come up with a name for an AI-focused brand, you should use AI to help you do it. And thats what we did.

First, we went through the necessary exercises to map out a brand: We talked through brand values, created an abstract about its focus and goals, listed the technologies and verticals we wanted to cover, and so on. Then, we humans brainstormed some ideas for names. (None stood out as clear winners.)

Armed with this data, we turned to Hugging Faces free online NLP tools, which require no code you just put text into the box and let the system do its thing. Essentially, we ended up following these tips to generate name ideas.

There are a few different approaches you can take. You can feed the system 20 names, lets say, and ask it to generate a 21st. You can give it tags and relevant terms (like machine learning, artificial intelligence, computer vision, and so on) and hope that it converts those into something that would be worthy of a name. You can enter a description of what you want (like a paragraph about what the sub-brand is all about) and see if it comes up with something. And you can tweak various parameters, like model size and temperature, to extract different results.

This sort of tinkering is a delightful rabbit hole to tumble down. After incessantly fiddling both with the data we fed the system and the various adjustable parameters, we ended up with a long and hilarious list of AI-generated names to chew on.

Here are some of our favorite terrible names that the tool generated:

This is a good lesson in the limitations of AI. The system had no idea what we wanted it to do. It couldnt, and didnt, solve our problem like some sort of name vending machine. AI isnt creative. We had to generate a bunch of data at the beginning, and then at the end, we had to sift through mostly unhelpful output (we ended up with dozens and dozens of names) to find inspiration.

But in the detritus, we found some nuggets of accidental brilliance. Here are a few NLP-generated names that are actually kind of good:

Its worth noting that the system all but insisted on AIBeat. No matter what permutations we tried, AIBeat kept resurfacing. It was tempting to pluck that low-hanging fruit it matched VentureBeat and GamesBeat, and theres no confusion about what beat wed be covering. But we humans decided to be more creative with the name, so we moved away from that construction.

We took a step back and used the long list of NLP-generated names to help us think up some fresh ideas. For example, We the Machine stood out to some of us as particularly punchy, but it wasnt quite right for a publication name. (Hello, I write for We the Machine doesnt exactly roll off the tongue.) But that inspired The Machine, which emerged as the winner from our shortlist.

The Machine has multiple layers. Its a play on machine learning, and its a wink at the persistent fear of sentient robots. And it frames our AI team as a formidable, well-oiled content machine, punching well above our weight with a tiny roster of writers.

And so, I write for The Machine. Bookmark this page and visit every day for the latest AI news, analysis, and features.

Read the original:

AI Weekly: Welcome to The Machine, VentureBeats AI site - VentureBeat

Amid the Covid-19 gloom, many cutting-edge technology companies have quietly been getting on with raising finance with artificial intelligence emerging as a particular focus for investors. Last month, for example, London-based Temporall raised 1m of seed investment to continue developing its AI- and analytics-based workplace insights platform. It was just the latest in a string of AI businesses to successfully raise finance in recent months despite the uncertainties of the pandemic.

That extends a trend seen last year. In September, a report from TechNation and Crunchbase revealed that UK AI investment reached a record level of $1bn in the first half of 2019, surpassing the total amount of new finance raised during the whole of the previous year.

The UKs AI industry has been boosted by a supportive public sector environment: the UK government is leading the way on AI investment in Europe and has become the third biggest spender on AI in the world. In the private sector, meanwhile, many British companies offer world-leading technologies. Take just five of the most innovative AI start-ups in the UK:

Synthesized

Founded in 2017, by Dr Nicolai Baldin, a machine-learning researcher based at the University of Cambridge, Synthesized has created an all-in-one data provisioning and preparation platform that is underpinned by AI.

In just 10 minutes, its technology can generate a representative synthetic dataset incorporating millions of records, helping an organisation to share insights safely and efficiently while automatically complying with data regulations. In March, Synthesized raised $2.8m in funding with the aim of doubling the number of its employees in London and accelerating the companys rapid expansion.

Onfido

With more than $180m in funding, Onfido is on a mission to help businesses verify people's identities. Founded in 2012, it uses machine learning and AI technologies, including face detection and character recognition, to verify documents such as passports and ID cards, and to help companies with fraud prevention.

Onfido is headquartered in London and now employs more than 400 employees across seven offices worldwide. In 2019 the company had over 1,500 customers including Revolut, Monzo and Zipcar.

Benevolent AI

Aiming disrupt the pharmaceutical sector, Benevolent AIs goal is to find medicines for diseases that have no treatment. Benevolent AI applies AI and machine learning tools together with other cutting-edge technologies to try to reinvent the ways drugs are discovered and developed.

The business was founded in 2013 and has raised almost $300m in funding. Its software reduces drug development costs, decreases failure rates and increases the speed at which medicines are generated. Right now, it is focusing on searching for treatments for Covid-19.

Plum Fintech

Plum is an AI assistant that helps people manage their money and increase their savings. It uses a mix of AI and behavioural science to help users change the way they engage with their finances for example, it points out savings they can afford by analysing their bank transactions.

Plum also allows its users to invest the money saved, as well as to easily switch household suppliers to secure better deals - the average customer can save roughly 230 a year on regular bills it claims.

Poly AI

After meeting at the Machine Intelligence Lab at the University of Cambridge, Nikola Mrki, Pei-Hao Su and Tsung-Hsien Wen a group of conversational AI experts started Poly AI. CEO Mrki was previously the first engineer at Apple-acquired VocalIQ, which became an essential part of Siri.

Poly AI helps contact centres scale. The companys technology not only understands customers queries, but also addresses them in a conversational way, either via voice, email or messaging. The company doesnt position itself as a replacement to human contact centre agents, but as an enhancement that works alongside them. Poly AI has secured $12m in funding to date and works as a team of seven out of its London headquarters.

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Five Of The Leading British AI Companies Revealed - Forbes

Team members at Adobe have built a new way to use artificial intelligence to automatically personalize a blog for different visitors.

This tool was built as part of the Adobe Sneaks program, where employees can create demos to show off new ideas, which are then showcased (virtually, this year) at the Adobe Summit. While the Sneaks start out as demos, Adobe Experience Cloud Senior Director Steve Hammond told me that 60% of Sneaks make it into a live product.

Hyman Chung, a senior product manager for Adobe Experience Cloud, said that this Sneak was designed for content creators and content marketers who are probably seeing more traffic during the coronavirus pandemic (Adobe says that in April, its own blog saw a 30% month-over-month increase), and who may be looking for ways to increase reader engagement while doing less work.

So in the demo, the Experience Cloud can go beyond simple A/B testing and personalization, leveraging the companys AI technology Adobe Sensei to suggest different headlines, images (which can come from a publishers media library or Adobe Stock) and preview blurbs for different audiences.

Image Credits: Adobe

For example, Chung showed me a mocked-up blog for a tourism company, where a single post about traveling to Australia could be presented differently to thrill-seekers, frugal travelers, partygoers and others. Human writers and editors can still edit the previews for each audience segment, and they can also consult a Snippet Quality Score to see the details behind Senseis recommendation.

Hammond said the demo illustrates Adobes general approach to AI, which is more about applying automation to specific use cases rather than trying to build a broad platform. He also noted that the AI isnt changing the content itself just the way the content is promoted on the main site.

This is leveraging the creativity youve got and matching it with content, he said. You can streamline and adapt the content to different audiences without changing the content itself.

From a privacy perspective, Hammond noted that these audience personas are usually based on information that visitors have opted to share with a brand or website.

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Adobe tests an AI recommendation tool for headlines and images - TechCrunch

A coalition of AI groups is forming to produce a comprehensive data source on the coronavirus pandemic for policymakers and health care leaders.

Why it matters: A torrent of data about COVID-19 is being produced, but unless it can be organized in an accessible format, it will do little good. The new initiative aims to use machine learning and human expertise to produce meaningful insights for an unprecedented situation.

Driving the news: Members of the newly formed Collective and Augmented Intelligence Against COVID-19 (CAIAC) announced today include the Future Society, a non-profit think tank from the Harvard Kennedy School of Government, as well as the Stanford Institute for Human-Centered Artificial Intelligence and representatives from UN agencies.

What they're saying: "With COVID-19 we realized there are tons of data available, but there was little global coordination on how to share it," says Cyrus Hodes, chair of the AI Initiative at the Future Society and a member of the CAIAC steering committee. "That's why we created this coalition to put together a sense-making platform for policymakers to use."

Context: COVID-19 has produced a flood of statistics, data and scientific publications more than 35,000 of the latter as of July 8. But raw information is of little use unless it can be organized and analyzed in a way that can support concrete policies.

The bottom line: Humans aren't exactly doing a great job beating COVID-19, so we need all the machine help we can get.

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A new AI tool to fight the coronavirus - Axios