Category Archives: Ai

It has been oft-quoted albeit humouredly that the ideal of medicine is the elimination of the physician. The emergence and encroachment of artificial intelligence (AI) on the field of medicine, however, puts an inconvenient truth on the aforementioned witticism. Over the span of their professional lives, a pathologist may review 100,000 specimens, a radiologist more so; AI can perform this undertaking in days rather than decades.

Visualise your last trip to an NHS hospital, the experience was either one of romanticism or repudiation: the hustle and bustle in the corridors, or the agonising waiting time in A&E; the empathic human touch, or the dissatisfaction of a rushed consultation; a seamless referral or delays and cancellations.

Contrary to this, our experience of hospitals in the future will be slick and uniform; the human touch all but erased and cleansed, in favour of complete and utter digitalisation. Envisage an almost automated hospital: cleaning droids, self-portered beds, medical robotics. Fiction of today is the fact of tomorrow, doesnt quite apply in this situation, since all of the above-mentioned AI currently exists in some form or the other.

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But then, what comes of the antiquated, human doctor in our future world? Well, they can take consolation, their unemployment status would be part of a global trend: the creation displacing the creator. Mechanisation of the workforce leading to mass unemployment. This analogy of our friend, the doctor, speaks volumes; medicine is cherished for championing human empathy if doctors arent safe, nobody is. The solution: socialism.

Open revolt against machinery seems a novel concept set in some futuristic dystopian land, though, the reality can be found in history: The Luddites of Nottinghamshire. A radical faction of skilled textile workers protecting their employment through machine destruction and riots, during the industrial revolution of the 18th century. The now satirised term "Luddite", may be more appropriately directed to your fathers fumbled attempt at unlocking his iPhone, as opposed to a militia.

What lessons are to be learnt from the Luddites? Much. Firstly, the much-fictionalised fight for dominance between man and machine is just that: fictionalised. The real fight is within mankind. The Luddites fight was always against the manufacturer, not the machine; machine destruction simply acted as the receptacle of dissidence. Secondly, government feeling towards the Luddites is exemplified through 12,000 British soldiers being deployed against the Luddites, far exceeding the personnel deployed against Napoleons forces in the Iberian Peninsula in the same year.

Though providing clues, the future struggle against AI and its wielders will be tangibly different from that of the Luddite struggle of the 18th century, next; its personal, its about soul. Our higher cognitive faculties will be replaced: the diagnostic expertise of the doctor, decision-making ability of the manager, and (if were lucky) political matters too.

Boston Dynamics describes itself as 'building dynamic robots and software for human simulation'. It has created robots for DARPA, the US' military research company

Google has been using similar technology to build self-driving cars, and has been pushing for legislation to allow them on the roads

The DARPA Urban Challenge, set up by the US Department of Defense, challenges driverless cars to navigate a 60 mile course in an urban environment that simulates guerilla warfare

Deep Blue, a computer created by IBM, won a match against world champion Garry Kasparov in 1997. The computer could evaluate 200 million positions per second, and Kasparov accused it of cheating after the match was finished

Another computer created by IBM, Watson, beat two champions of US TV series Jeopardy at their own game in 2011

Apple's virtual assistant for iPhone, Siri, uses artificial intelligence technology to anticipate users' needs and give cheeky reactions

Xbox's Kinect uses artificial intelligence to predict where players are likely to go, an track their movement more accurately

Boston Dynamics describes itself as 'building dynamic robots and software for human simulation'. It has created robots for DARPA, the US' military research company

Google has been using similar technology to build self-driving cars, and has been pushing for legislation to allow them on the roads

The DARPA Urban Challenge, set up by the US Department of Defense, challenges driverless cars to navigate a 60 mile course in an urban environment that simulates guerilla warfare

Deep Blue, a computer created by IBM, won a match against world champion Garry Kasparov in 1997. The computer could evaluate 200 million positions per second, and Kasparov accused it of cheating after the match was finished

Another computer created by IBM, Watson, beat two champions of US TV series Jeopardy at their own game in 2011

Apple's virtual assistant for iPhone, Siri, uses artificial intelligence technology to anticipate users' needs and give cheeky reactions

Xbox's Kinect uses artificial intelligence to predict where players are likely to go, an track their movement more accurately

The monopolising of AI will lead to mass unemployment and mass welfare, reverberating globally. AI efficiency and efficacy will soon replace the error-prone human. It must be the case that AI is to be socialised and the means of production, the AI, redistributed: in other words, brought under public ownership. Perhaps, the emergence of co-operative groups made up of experienced individuals will arise to undertake managerial functions in their previous, now automated, workplace. Whatever the structure, such an undertaking will require the full intervention of the state; on a moral basis not realised in the Luddite struggle.

Envisaging an economic system of nationalised labour of AI machinery performing laborious as well as lively tasks shant be feared. This economic model, one of "abundance", provides a platform of the fullest of creative expression and artistic flair for mankind. Humans can pursue leisurely passions. Imagine the doctor dedicating superfluous amounts of time on the golfing course, the manager pursuing artistic talents. And what of the politician? Well, thats anyones guess

An abundance economy is one of sustenance rather than subsistence; initiating an old form of socialism fit for a futuristic age. AI will transform the labour market by destroying it; along with the feudalistic structure inherent to it.

Thought-provoking questions do arise: what is to become of human aspiration? What exactly will it mean to be human in this world of AI?

Ironically; perhaps it will be the machine revolution that gives us the resolution to age-old problems in society.

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AI technology will soon replace error-prone humans all over the world but here's why it could set us all free - The Independent

ARLINGTON, Va. U.S. military researchers are reaching out to industry to prevent enemy attempts to corrupt or spoof artificial intelligence (AI) systems by subtly altering or manipulating information the AI system uses to learn, develop, and mature.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) issued a solicitation on Wednesday (DARPA-PA-19-03-09) for the Reverse Engineering of Deceptions (RED) project, which aims at reverse engineering the toolchains of information deception attacks.

A deceptive information attack describes enemy attempts subtly to alters or manipulates information used by a human or machine learning system to alter a computational outcome in the adversarys favor.

Machine learning techniques are susceptible to enemy information warfare attacks at training time and when deployed. Similarly, humans are susceptible to being deceived by falsified images, video, audio, and text. Deception plays an increasingly central role in information warfare attacks.

Related: Research, applications, talent, training, and cooperation frame report on artificial intelligence (AI)

The Reverse Engineering of Deceptions (RED) effort will develop techniques that automatically reverse engineer the toolchains behind attacks such as multimedia falsification, enemy machine learning attacks, or other information deception attacks.

Recovering the tools and processes for such attacks provides information that may help identify an enemy. RED will seek to develop techniques that identify attack toolchains automatically, and develop scalable databases of attack toolchains.

RED Phase 1 will produce trusted-computing algorithms to identify the toolchains behind information deception attacks. The project's second phase will develop technologies for scalable databases of attack toolchains to support attribution and defense.

Related: Air Force researchers ask industry for SWaP-constrained embedded computing for artificial intelligence (AI)

The project also seeks to develop techniques that require little or no a-priori knowledge of specific deception toolchains; automatically cluster attack examples together to discover families of deception toolchains; generalize across several information deception scenarios like enemy machine learning and media manipulation; require just a few attacks to learn unique signatures; and scale to internet volumes of information.

Companies interested should upload 8-page proposals no later than 30 July 2020 to the DARPA BAA Website at https://baa.darpa.mil/. Email questions or concerns to Matt Turek, the DARPA RED program manager, at RED@darpa.mil.

More information is online at https://beta.sam.gov/opp/f108cad02f824285af5ca85e1f7481f4/view.

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trusted computing artificial intelligence (AI) information warfare - Military & Aerospace Electronics

Continuing with their legacy of innovation, Adobe has yet again brought a new way to personalize a blog post for different users with the help of artificial intelligence.

Known by the name of Adobe Sensei, the technology will recommend different headlines, images (taken from the library of Adobe Stock), and also preview blurbs based on improving the experience for the targeted audience.

The new tool has come out as a part of the Adobe Sneaks program which employees use to develop their new ideas in the form of proper demos and then showcase what they have made at the Adobe Summit every year. So, while a lot of people consider Sneaks as merely demos, Adobe Experience Cloud Senior Director Steve Hammond disagree by telling that almost 60% of the Sneaks turn out into real products later after the Summit. Furthermore, Hyman Chung, a senior product manager for Adobe Experience Cloud state that Sneaks, in particular, can be more useful for content creators and content marketers who are already enjoying a great hile in traffic during the coronavirus pandemic and may now be looking for more unique ways to make readers engage more by doing less work.

Chung showed the magic of Experience Cloud with a test blog based on a tourism company. One blog post about traveling to Australia was presented differently to thrill-seekers, frugal travelers, partygoers, and others in the demo. The feature also provides the liberty to writers and editors to make changes in the preview according to the desired audience and even go through the Snippet Quality Score for what Sensei recommends.

Hammond also explained that the demo only illustrates Adobes approach to AI as the company majorly focuses on delivering automation in specific user cases with AI rather than going for building bigger platforms. So, in Senseis case, AI will not change the content but only how it is promoted on the site.

For privacy matters, Hammond has clearly mentioned that the audience personas are only built on what kind of information the user decides to share with the website or brand.

Read next: This New AI-Based Algorithm Created By Microsoft Helps To Restore Old Photos

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Adobe's New AI Tool Can Recommend Different Headlines and Images To The Varying Audience Of A Blog - Digital Information World

Back to the OG video. Scientists at the University of Liverpool have unveiled a robotic colleague that has been working non-stop in their lab throughout lockdown. The 100,000 programmable inhuman researcher learns from its results to refine its experiments. "It can work autonomously, so I can run experiments from home," explained Benjamin Burger, PhD student at the University and one of the developers of the robots. Dr Burger jokingly added, "It doesn't get bored, doesn't get tired, works around the clock and doesn't need holidays." Such technology could make scientific discovery "a thousand-fold faster", scientists say. A new report by the Royal Society of Chemistry lays out a "post-COVID national research strategy", using robotics, AI and advanced computing as a part of a set of technologies that "must be urgently embraced" to assist socially distancing scientists continue their look for solutions to global challenges. Future science historians will mark the start of the 21st century as a time when robots took their place beside human scientists. Programmers have turned computers from extraordinarily powerful but fundamentally dumb tools, into tools with smarts. Artificially intelligent programs make sense of knowledge so complex that it defies human analysis. They even come up with hypotheses, the testable questions that drive science, on their own.

For better or worse the robots are about to replace many humans in their jobs, analysts say; coronavirus outbreak is just speeding up the method. "People usually say they need an individual's element to their interactions but Covid-19 has changed that," says Martin Ford, a futurist who has written about the ways robots are going to be integrated into the economy within the coming decades. "[Covid-19] goes to vary consumer preference and really open up new opportunities for automation." Companies large and little are expanding how they use robots to extend social distancing and reduce the quantity of staff that need to physically come to figure. Robots are also getting used to performing roles workers cannot do at home. Walmart is using robots to scrub their floors, fast-food chains like McDonald's have been testing robots as cooks and servers in a service where the health concern is highest. After all this, it is evident that the majority of the jobs that are available to the general people like us are temporary, insecure, and badly paid. Nevertheless, with the advent of using more robots in the workplace, there will be an unjust, unfair and unacceptable distribution of income. Just for the sake of health concerns, the use of robots increased exponentially. All of this is that version of future which haunts the experts of AI.

While automation is likely to foster overall economic prosperity, it comes at the price of increasing inequality. The COVID-19 pandemic is reinforcing both the trend towards automation and its effects. The main challenge here is to ensure that as many as possible will benefit from the positive economic and social effects of automation to prevent a situation in which a substantial part of society is disconnected from the gains brought by technological progress. There are still many things that they will never be able to do better than humans, and there are still more that they will not be able to do as cheaply. We are yet to discover the full range of these things, but we can already find out the key limitations to what robots and AI can do.

First, there appears to be a high quality in human intelligence that, for all its wonders, AI cannot match, namely its ability to influence the uncertain, the fuzzy, and the logically ambiguous.

Second, due to the innate nature of human intelligence, people are extremely flexible in being able to perform umpteen possible tasks, including those that were not foreseen at first.

Third, humans are social creatures instead of isolated individuals. Humans want to deal with other humans. Robots will never be better than humans at being human, and so I conclude- there is no risk for a post-pandemic near future.

Reference: 1. https://www.bbc.com/news/science-environment-53029854

2. https://www.bbc.com/news/technology-52340651

3. https://voxeu.org/article/covid-19-and-macroeconomic-effects-automation

4. Roger Bootle- The AI Economy Work, Wealth and Welfare in the Robot Age; Nicholas Brealey Publishing, Sept. 2019

Thumbnail credit: shutterstock.com

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CoVID-19 and the use of robots - AI Daily

Swedish supermarket Coop Sweden has a retail grocery assistant on its websites. Cooper, as the assistant is called, can help you with dietary requirements, suggest recipes and provide nutritional information. The idea behind Cooper is to increase interaction with consumers while providing a seamless shopping experience.

As consumers move online, implementing the technologies of the fourth industrial revolution (4IR) is becoming more crucial. Cooper is an example of the 4IR in practice. These technologies are changing the way we work, commute, communicate and, as Cooper will tell you, even shop. The 4IR is based on high-level technology such as artificial intelligence, automation, biotechnology, nanotechnology and communication technologies that permeates society. It is a combination of various technologies that can communicate with humans and interact with other devices and programs.

The lockdown necessitated by the Covid-19 pandemic has been an important yardstick for understanding behavioural changes in consumers as online options become more commonplace. A recent Nielsen study found that 37% of South Africans say they are shopping more online in this period. As Gareth Paterson, a lead retail analyst at Nielsen South Africa, put it, Amid the strange new world of Covid-19, online grocery shopping has been a lifeline for many South African consumers who have desperately sought out safe and secure shopping alternatives amidst the uncertainty of lockdown living. As a result, available online shopping platforms, especially for groceries, medicines, and other necessary items, have seen a surge in usage over the last few weeks as consumers prefer not to venture into stores and have increasingly opted for these reduced touchpoint alternatives.

According to data from the survey, Nielsen is anticipating that options such as click and collect and online personal shopping will grow exponentially, resulting in prolonged behavioural changes. Retailers have been quick to cotton on to this shift and have responded in innovative and effective ways. For instance, Checkers has launched an app called Checkers Sixty60, which has groceries delivered to you in 60 minutes. There are 5 000 groceries to choose from and options to substitute products if your first choice is not available.

In various industries, the coronavirus has been an important lesson where we are well equipped to deal with the 4IR and where we still have gaps. This will undoubtedly signal a shift in consumer behaviour and many will not return to traditional brick-and-mortar retail. We will increasingly see more retailers adapt to this way of operating. In fact, a report by global management consultancy Accenture last year suggested that South African retailers would see a knock to a business if they did not embrace e-commerce. The emphasis on traditional stores, Accenture argues, means that many retailers are losing out on the potential profits that come with online offerings. Yet, interestingly enough, the current pandemic may subvert this.

This is not to say that online shopping has not had somewhat of a watershed moment in recent years. Perhaps the best example that provides a holistic user experience is the Mr Price app. With it, you can shop online, find the stock in stores and even upload a picture of something you like for it to suggest similar items available on the app through the snap and shop feature. For instance, I could either take or upload a picture of a pair of brown formal shoes that I saw a colleague wear. The app will then pull any stock available at Mr Price that looks similar and provide a list of suggestions accompanied by pictures.

The starkest instance of the popularity of online retail is Black Friday, which has gained popularity in South Africa in the last few years. It is probably the biggest day of the year for retailers, particularly online retailers. In the week leading up to it, consumers receive hordes of massive Black Friday discounts. Some of them may have put together wish lists to check out at the stroke of midnight while others may have used their phones to search for discounts.

AI is tailoring the online experience and it is determining prices, inventory and making distribution far more efficient for your favourite retailers. Another example of this on Instagram is the move to online shopping with a new AR shopping feature that is being rolled out, which allows consumers to try on products digitally before buying them. For example, using your phone you could try on the latest shade of Mac lipstick to see how you would look. This followed a rollout of a checkout feature that allowed you to buy products directly on Instagram without ever leaving the app.

The try-on feature is limited to certain brands and is still in a trial phase, but it is as easy to use as the filters when you create a story that could give you dog ears and a tongue or freckles and blue eyes. The long-term vision is to roll this out with all retail, so, for example, you could see what a couch looks like in your living room. This is not the only technology Instagram has adopted. AI influencers have been introduced, which have been surprisingly popular.

According to consumer insight website LendEDU, three years ago 52.9% of millennials said Instagram has the most influence on them when making shopping decisions. For instance, many followers use the website LIKEtoKNOW.it, which sends a direct link to a product after a shopper likes a post. Creating completely digital influencers is a whole new avenue. Miquela is an AI influencer with 2.4-million followers. Just like any other influencer, her posts are perfectly planned, she has a themed feed, has sponsored content and gives her followers useful advice and brand recommendations. But she does not actually exist she is run with AI technology. This has not stopped her career from taking off.

Last year, she collaborated with Prada for Milan Fashion Week by posting 3D-generated gifs of herself at the Milan show venue wearing the spring/summer 2018 collection. On Pradas Instagram account, she gave their followers a mini-tour of the space, just like any influencer would for a brand. She is not an outlier there are many more like her. Balmain recently announced a Balmain Army made up entirely of computer-generated imagery (CGI) models. There is also a dedicated modelling agency for digital models called The Digital.

Amazon, the largest online retailer by revenue, has 45 000 robots at its warehouses to fulfil orders and a fleet of airborne drones into service for fast deliveries. It is not just online that retail is transforming with the 4IR. There is room to implement this kind of technology at brick-and-mortar level. The introduction of robotics has streamlined checkout processes, for instance. In the United Kingdom, you can self-checkout at grocery stores that weigh your goods to prevent theft. Similarly, there are robots akin to sales assistants in stores in the United States they can help you find an item either verbally or through the touch screen. Some robots can perform real-time inventory tracking.

Best Buy, the US-based electronics store, has an automated system much like the claw machine at the arcade that can retrieve products from shelves. There is scope to streamline and automate processes that will prove to be cost-effective for retailers in the long run. Accelerated adoption of technology will be a key strategic move that could lift retailers margins significantly. Retailers can introduce digital technologies and automation into their operations to reduce costs and enhance the customer experience. They can turn e-commerce from a threat to a growth opportunity, a McKinsey and Company report on the future of work in South Africa reads.

One of the grim realities of this era we are moving into is that there will be knock-on employment, particularly of low-skill workers. The caveat is that there will be demand for graduates and employees with higher skills levels, and we need to meet the demand for graduates not to fall into an even deeper unemployment crisis.

From a retail perspective, there is so much to be done that can augment consumers experiences. As industries vie to be a step ahead in the ever-changing context, consumers and business owners have to be open to these experiences and shifts. As physicist William Pollard once said: Without change there is no innovation, creativity, or incentive for improvement. Those who initiate change will have a better opportunity to manage the change that is inevitable.

Professor Tshilidzi Marwala is the vice-chancellor and principal of the University of Johannesburg and deputy chair of the Presidential Commission on the Fourth Industrial Revolution

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Cooper, the grocery assistant with AI, gives concierge service - Mail and Guardian

Imec, a world-leading research and innovation hub in nanoelectronics and digital technologies, and GLOBALFOUNDRIES, the worlds leading specialty foundry, today announced a hardware demonstration of a new artificial intelligence chip.

Based on imecs Analog in Memory Computing (AiMC) architecture utilizing GFs 22FDX solution, the new chip is optimized to perform deep neural network calculations on in-memory computing hardware in the analog domain. Achieving record-high energy efficiency up to 2,900 TOPS/W, the accelerator is a key enabler for inference-on-the-edge for low-power devices. The privacy, security, and latency benefits of this new technology will have an impact on AI applications in a wide range of edge devices, from smart speakers to self-driving vehicles.

Chinese Semiconductor Manufacturer SMIC to Introduce 7nm Node

Since the early days of the digital computer age, the processor has been separated from the memory. Operations performed using a large amount of data require a similarly large number of data elements to be retrieved from the memory storage. This limitation, known as the von Neumann bottleneck, can overshadow the actual computing time, especially in neural networks which depend on large vector-matrix multiplications. These computations are performed with the precision of a digital computer and require a significant amount of energy. However, neural networks can also achieve accurate results if the vector-matrix multiplications are performed with a lower precision on analog technology.

To address this challenge, imec and its industrial partners in imecs industrial affiliation machine learning program, including GF, developed a new architecture that eliminates the von Neumann bottleneck by performing analog computations in SRAM cells. The resulting Analog Inference Accelerator (AnIA), built on GFs 22FDX semiconductor platform, has exceptional energy efficiency. Characterization tests demonstrate power efficiency peaking at 2,900 tera operations per second per watt (TOPS/W). Pattern recognition in tiny sensors and low-power edge devices, which is typically powered by machine learning in data centers, can now be performed locally on this power-efficient accelerator.

Looking ahead, GF will include AiMC as a feature able to be implemented on the 22FDX platform for a differentiated solution in the AI market space. GFs 22FDX employs 22nm FD-SOI technology to deliver outstanding performance at extremely low power, with the ability to operate at 0.5 Volt ultralow-power and at 1 pico amp per micron for ultralow standby leakage. 22FDX with the new AiMC feature is in development at GFs state-of-the-art 300mm production line at Fab 1 in Dresden, Germany.

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Imec & GLOBALFOUNDRIES Partner Up And Announce Breakthroughs In AI Chip On IoT Devices - Wccftech

Scientists are developing light-activated nanoparticles to kill cancer cells Credit: Harry Gui on Unsplash

With news about the coronavirus pandemic developing daily, we want to make sure everyone affected by cancer gets the information they need during this time.

Were pulling together the latest government and NHS health updates from across the UK in a separate blog post, which were updating regularly.

A new combined therapy with the drug brentuximab has been approved for adults with a rare type of fast-growing lymphoma. Clinical trial evidence suggests the treatment could give people with this type of blood cancer more time before their disease progresses. More on this in our news report.

Boris Johnson is set to announce restrictions onmulti-buy and similar pricepromotions on a range offoodshigh in fat, sugar or salt in a bid to tackle rising obesity in the UK, according to The Times ()and Guardian. The announcement comes off the back of evidence showing that 40% of food spending goes on products on promotion. However, health campaigners have expressed their disappointment at the seeming lack of action on junk food marketing. According to the leaked documents, a 9pm watershed on junk food adverts is not on the cards at this time, although the plans may change in the coming weeks.

Scientists have developed light-activated nanoparticles that kill skin cancer cells in mice. The treatment involves linking tiny particles to short pieces of RNA that inhibit the production of essential proteins that cancer cells need to survive. Its early days yet, but scientists are hopeful that the light-activated technology can help to reduce side effects and make the treatment more targeted. Read more on this at New Atlas.

An excess of a protein thats essential to cell division PRC1 has been linked to many types of cancer, including prostate, ovarian and breast. Now scientists have found that the protein acts as a glue during cell division, precisely controlling the speed at which DNA strands separate as a single cell divides. These findings could help to explain why too much or too little PRC1 disrupts the division process and can be linked to cancer developing. Full story at Technology Networks.

Earlier this month, the Government announced over 16 million pounds in research funding to help improve the diagnosis of cancer and other life-threatening diseases, with Cancer Research UK putting in 3 million. Some of that money will go towards an Oxford-led project to improve lung cancer diagnosis. The team hope to use artificial intelligence to combine clinical, imaging and molecular data and make lung cancer diagnosis more accurate. More on this at Digital Health.

Weve partnered with Abcam to develop custom antibodies that could facilitate cancer research. Dr John Baker at Abcam said: We are proud to be working with Cancer Research UK to support their scientists and help them achieve their next breakthrough faster. Find out more at Cambridge Independent.

Technology Networks reports on a new study that has taken a closer look at how tiny bubble-like structures called vesicles can help cancer cells spread. Scientists found that vesicles from cancer cells contained high levels of proteins with lipid molecules attached, which are associated with the spread of cancer. Weve blogged before about how tumours spread.

Scarlett Sangster is a writer for PA Media Group

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News digest light-activated nanoparticles, Government's obesity plans, tumour 'glue' and AI research fund - Cancer Research UK - Science Blog

We all know what a person's voice sounds like when they have bad cold. But would you recognise the sound of some with Parkinson's disease? What about dementia or neck cancer?

Probably not, but researchers believe algorithms and artificial intelligence will soon be able to pick out signs of illness from someone's voice.

The technology could even be useful for diagnosing the outbreaks of the coronavirus.

"Each one of our internal organs is sort of a resonator, so if we have a problem with our lungs or our heart... this is reflected in our voice," says Giovanni Saggio, an electronics engineering professor at Rome's Tor Vergata University, researching in this area.

"The way our voice sounds also depends on who is directing the music, that is, the brain. If you have a cerebral problem, like Alzheimers or Parkinsons, the way you talk changes, and we can detect that."

The current novel coronavirus epidemic could be picked up this way, Saggio suggests, since the virus "compromises the lungs and airwaves, so the voice is definitely affected."

Suspect cases could be screened remotely, keeping them away from hospitals and reducing contagion risks, Saggio says, adding that he is trying to contact Italian hospitals dealing with the outbreak.

He's one of several researchers around the world looking into this topic. In Germany, a spin-off from Humboldt University, PeakProfiling, says it can "build algorithms which detect emotional states and physical conditions purely from sound data especially from the human voice."

Meanwhile, University of Washington researchers have published a study in Nature on how devices like Amazon's Alexa can successfully recognise the gasping sounds that often pre-announce cardiac arrest.

By Saggio's reckoning, more than 150 scientific papers have been published worldwide on the correlation between voice changes and various pathologies.

Using his own patented technology, Saggio tested 284 tuberculosis patients in Mumbai, measuring them against a healthy control group of 28, asking each person to record the same short sentences.

When voices were analysed, differences in acoustic parameters revealed who was healthy and who was not. Parkinson's sufferers were correctly detected in 95% of cases.

There may be even more applications, as Saggio imagines that voice recognition could help a parent understand why their baby is coughing or crying.

Saggio recalls there was even a diagnosis within his initial trials. "There was this man in the control group who was meant to be healthy," he says.

"But we told him: Looking at your data, we think you might have a temperature tomorrow, and he said, 'But I feel well!' The next day he had a temperature."

Saggio had his study published in the Journal Of Communication, Navigation, Sensing Aalznd Services in 2016 and is promoting his invention through a start-up, VoiceWise.

He has been working on it for around 10 years, and, aged 55, hopes to see its wider adoption before reaching retirement age. "It's going to be a matter of a few years," he says.

The technology could deliver significant public health benefits, as it would make it easier and cheaper to screen patients, including in developing countries, resulting in earlier diagnoses. dpa

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AI may soon be able to tell you're sick by the sound of your voice - The Star Online

In September 1955, John McCarthy, a young assistant professor of mathematics at Dartmouth College, boldly proposed that "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

McCarthy called this new field of study "artificial intelligence," and suggested that a two-month effort by a group of 10 scientists could make significant advances in developing machines that could "use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."

At the time, scientists optimistically believed we would soon have thinking machines doing any work a human could do. Now, more than six decades later, advances in computer science and robotics have helped us automate many of the tasks that previously required the physical and cognitive labor of humans.

But true artificial intelligence, as McCarthy conceived it, continues to elude us.

A great challenge with artificial intelligence is that it's a broad term, and there's no clear agreement on its definition.

As mentioned, McCarthy proposed AI would solve problems the way humans do: "The ultimate effort is to make computer programs that can solve problems and achieve goals in the world as well as humans," McCarthy said.

Andrew Moore, Dean of Computer Science at Carnegie Mellon University, provided a more modern definition of the term in a 2017 interview with Forbes: "Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence."

But our understanding of "human intelligence" and our expectations of technology are constantly evolving. Zachary Lipton, the editor of Approximately Correct, describes the term AI as "aspirational, a moving target based on those capabilities that humans possess but which machines do not." In other words, the things we ask of AI change over time.

For instance, In the 1950s, scientists viewed chess and checkers as great challenges for artificial intelligence. But today, very few would consider chess-playing machines to be AI. Computers are already tackling much more complicated problems, including detecting cancer, driving cars, and processing voice commands.

The first generation of AI scientists and visionaries believed we would eventually be able to create human-level intelligence.

But several decades of AI research have shown that replicating the complex problem-solving and abstract thinking of the human brain is supremely difficult. For one thing, we humans are very good at generalizing knowledge and applying concepts we learn in one field to another. We can also make relatively reliable decisions based on intuition and with little information. Over the years, human-level AI has become known as artificial general intelligence (AGI) or strong AI.

The initial hype and excitement surrounding AI drew interest and funding from government agencies and large companies. But it soon became evident that contrary to early perceptions, human-level intelligence was not right around the corner, and scientists were hard-pressed to reproduce the most basic functionalities of the human mind. In the 1970s, unfulfilled promises and expectations eventually led to the "AI winter," a long period during which public interest and funding in AI dampened.

It took many years of innovation and a revolution in deep-learning technology to revive interest in AI. But even now, despite enormous advances in artificial intelligence, none of the current approaches to AI can solve problems in the same way the human mind does, and most experts believe AGI is at least decades away.

The flipside, narrow or weak AI doesn't aim to reproduce the functionality of the human brain, and instead focuses on optimizing a single task. Narrow AI has already found many real-world applications, such as recognizing faces, transforming audio to text, recommending videos on YouTube, and displaying personalized content in the Facebook News Feed.

Many scientists believe that we will eventually create AGI, but some have a dystopian vision of the age of thinking machines. In 2014, renowned English physicist Stephen Hawking described AI as an existential threat to mankind, warning that "full artificial intelligence could spell the end of the human race."

In 2015, Y Combinator President Sam Altman and Tesla CEO Elon Musk, two other believers in AGI, co-founded OpenAI, a nonprofit research lab that aims to create artificial general intelligence in a manner that benefits all of humankind. (Musk has since departed.)

Others believe that artificial general intelligence is a pointless goal. "We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own," says Peter Norvig, Director of Research at Google.

Scientists such as Norvig believe that narrow AI can help automate repetitive and laborious tasks and help humans become more productive. For instance, doctors can use AI algorithms to examine X-ray scans at high speeds, allowing them to see more patients. Another example of narrow AI is fighting cyberthreats: Security analysts can use AI to find signals of data breaches in the gigabytes of data being transferred through their companies' networks.

Early AI-creation efforts were focused on transforming human knowledge and intelligence into static rules. Programmers had to meticulously write code (if-then statements) for every rule that defined the behavior of the AI. The advantage of rule-based AI, which later became known as "good old-fashioned artificial intelligence" (GOFAI), is that humans have full control over the design and behavior of the system they develop.

Rule-based AI is still very popular in fields where the rules are clearcut. One example is video games, in which developers want AI to deliver a predictable user experience.

The problem with GOFAI is that contrary to McCarthy's initial premise, we can't precisely describe every aspect of learning and behavior in ways that can be transformed into computer rules. For instance, defining logical rules for recognizing voices and imagesa complex feat that humans accomplish instinctivelyis one area where classic AI has historically struggled.

An alternative approach to creating artificial intelligence is machine learning. Instead of developing rules for AI manually, machine-learning engineers "train" their models by providing them with a massive amount of samples. The machine-learning algorithm analyzes and finds patterns in the training data, then develops its own behavior. For instance, a machine-learning model can train on large volumes of historical sales data for a company and then make sales forecasts.

Deep learning, a subset of machine learning, has become very popular in the past few years. It's especially good at processing unstructured data such as images, video, audio, and text documents. For instance, you can create a deep-learning image classifier and train it on millions of available labeled photos, such as the ImageNet dataset. The trained AI model will be able to recognize objects in images with accuracy that often surpasses humans. Advances in deep learning have pushed AI into many complicated and critical domains, such as medicine, self-driving cars, and education.

One of the challenges with deep-learning models is that they develop their own behavior based on training data, which makes them complex and opaque. Often, even deep-learning experts have a hard time explaining the decisions and inner workings of the AI models they create.

Here are some of the ways AI is bringing tremendous changes to different domains.

Self-driving cars: Advances in artificial intelligence have brought us very close to making the decades-long dream of autonomous driving a reality. AI algorithms are one of the main components that enable self-driving cars to make sense of their surroundings, taking in feeds from cameras installed around the vehicle and detecting objects such as roads, traffic signs, other cars, and people.

Digital assistants and smart speakers: Siri, Alexa, Cortana, and Google Assistant use artificial intelligence to transform spoken words to text and map the text to specific commands. AI helps digital assistants make sense of different nuances in spoken language and synthesize human-like voices.

Translation: For many decades, translating text between different languages was a pain point for computers. But deep learning has helped create a revolution in services such as Google Translate. To be clear, AI still has a long way to go before it masters human language, but so far, advances are spectacular.

Facial recognition: Facial recognition is one of the most popular applications of artificial intelligence. It has many uses, including unlocking your phone, paying with your face, and detecting intruders in your home. But the increasing availability of facial-recognition technology has also given rise to concerns regarding privacy, security, and civil liberties.

Medicine: From detecting skin cancer and analyzing X-rays and MRI scans to providing personalized health tips and managing entire healthcare systems, artificial intelligence is becoming a key enabler in healthcare and medicine. AI won't replace your doctor, but it could help to bring about better health services, especially in underprivileged areas, where AI-powered health assistants can take some of the load off the shoulders of the few general practitioners who have to serve large populations.

In our quest to crack the code of AI and create thinking machines, we've learned a lot about the meaning of intelligence and reasoning. And thanks to advances in AI, we are accomplishing tasks alongside our computers that were once considered the exclusive domain of the human brain.

Some of the emerging fields where AI is making inroads include music and arts, where AI algorithms are manifesting their own unique kind of creativity. There's also hope AI will help fight climate change, care for the elderly, and eventually create a utopian future where humans don't need to work at all.

There's also fear that AI will cause mass unemployment, disrupt the economic balance, trigger another world war, and eventually drive humans into slavery.

We still don't know which direction AI will take. But as the science and technology of artificial intelligence continues to improve at a steady pace, our expectations and definition of AI will shift, and what we consider AI today might become the mundane functions of tomorrow's computers.

Further Reading

See the article here:

What Is Artificial Intelligence (AI)? | PCMag

LinkedIn has been at the cutting edge of AI for years and uses AI in many ways users may not be aware of. I recently had the opportunity to talk to Igor Perisic, Chief Data Officer (CDO) and VP of Engineering at LinkedIn to learn more about the evolution of AI at LinkedIn, how its being applied to daily activities, how worldwide data regulations impact the company, and some unique insight into the changing AI-related work landscape and job roles.

Igor Perisic, Chief Data Officer and VP of Engineering at LinkedIn

The Evolution of AI at LinkedIn

Very early on at LinkedIn, data was identified as one of the companys core differentiating factors. Another differentiating factor was a core company value of members first (clarity, consistency, and control of how member data is used) and their vision to create economic opportunity for every member of the global workforce.

As LinkedIn began finding more and more ways to weave AI into their products and services, they also recognized the importance of ensuring all employees were well-equipped to work with AI as needed in their jobs. To that end, they created an internal training program called the AI Academy. Its a program that teaches everyone from software engineers to sales teams about AI at the level most suited to them, in order for them to be prepared to work with these technologies.

One of the very first AI projects was the People You May Know (PYMK) recommendations. Essentially, this is an algorithm that recommends to members other people that they may know on the platform and helps them build their networks. It is a recommendation system that is still central to their products, although now it is much more sophisticated than it was in those early days. PYMK as a data product began around 2006. It was started by folks that would eventually be known as one of the first data science teams in the tech industry. Back in those early days, no one referred to PYMK as an AI project, as the term AI was not yet a back in favor buzz word.

The other significant project which we started around the same time was of course search ranking, which was a classic AI problem at that time due to the emergence of Google and competition in the search engine space.

How AI is applied to daily activities

At LinkedIn, Igor says that we compare AI to oxygenit permeates everything we do. For example, for our members, it helps recommend job opportunities, organizes their feed, ensures that the notifications they receive are timely and informative, and suggests LinkedIn Learning content to help them learn new skills. With respect to LinkedIns enterprise products, he says AI helps salespeople reach members that have an interest in their products, marketers serve relevant sponsored content, and recruiters identify and reach out to new talent pools. The benefits of AI at Linkedin also operate in the background, from helping protect members from fraudulent and harmful content to routing internet connections to ensure the best possible site speed for our members.

Ensuring member safety on the platform is something that we take very seriously. Being a social network with a very strong professional intent, its important to act quickly in identifying and preventing abuse. Because abuse and threats are constantly changing, AI is certainly at the core of these efforts. LinkedIn has found machine learning very helpful in detecting inappropriate profiles.

Without AI, many of their products and services would simply not function. The economic graph they use to represent the global economy is simply too large and too nuanced to be understood without it.

AI is literally enhancing every experience. Starting from the notifications our members are getting about relevant items. But, probably, one of the most prominent ways through which our members experience AI is in the feed, which sorts and ranks a heterogeneous inventory of activities (posts, news, videos, articles, etc.). To ensure relevance in the feed, its important that the algorithms consider the different nuances of content recommendations and members preferences.

One interesting example Igor shares is that at the start of 2018, they discovered an uneven distribution of engagement in the feedgains in viral actions were accrued by the top 1% of power users, and the majority of creators were increasingly receiving zero feedback. The feed model was simply doing as it was told: sharing broad-interest, viral content that would generate lots of engagement. However, he says they realized that this optimization wasnt necessarily the most beneficial for all members. To combat the negative ecosystem effect that the AI had created, they incorporated creator-side optimization in their feed relevance objective function to help their creators with smaller audiences. With this update, the ranking algorithms began taking into consideration the value that would result for both viewer and creator in surfacing a specific item. For the viewer,they wanted to surface relevant content based on their preferences, and for the creator, they wanted to encourage high-quality content and help them reach their audiences. Igor says by tweaking our models to optimize for more than just viral sharing moments, our feed changed into a healthy mix of content from influencers as well as direct connections, which then improved engagement for both viewers and creators..

How worldwide data regulations impact LinkedIn

In recent years regions around the world have started to put in place laws around how companies are able to store and use user data. Laws such as the EUs General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) are intended to enhance privacy rights and consumer protection. For some companies, becoming compliant meant having to totally chance how they approach data. Luckily for LinkedIn, data was always considered an asset to the company and approached with respect as one of the companys core differentiating factors.

Even before GDPR, Igor says LinkedIn had an internal framework they call the 3Csclarity, consistency, and control. He says We believed then and still do today that we owed it to our members to provide clarity about what we do with their data, to be consistent in only doing as we say, and to give our members control over their data:. In that context, LinkedIn approached GDPR as an opportunity to reinforce their commitment to data privacy for all members globally. For example, LinkedIn extended GDPR Data Subject Rights to all members globally. They continue to be thoughtful in how they approach the use of members data throughout LinkedIn and in AI, and in how they review and update processes, to ensure privacy by design. Acting in the best interest of members continues to be LinkedIns north star, and they always felt that its their joint responsibility across the organization to protect members data.

The changing AI work landscape

As a very large professional social network, LinkedIn has the unique opportunity to see insights about changing job roles, popular positions, and regional popularity that other companies might not have as deep insights into. At the end of last year, LinkedIn released their third annual Emerging Jobs Report to identify the most rapidly growing jobs. AI specialist emerged as the #1 emerging job of that list, showing 74% annual growth over the past 4 years. Its especially exciting to see this growth beyond the tech industry. In 2017, they found that the education sector had the second-highest numbers of core AI skills added by members, showing that AIs growth is correlated with more research in the field.

More recently, amid the economic downturn caused by the pandemic, LinkedIn is still observing that the AI job market continues to grow. When normalized against overall job postings, AI jobs increased 8.3% in the ten weeks after the COVID-19 outbreak in the U.S. Even though AI job listings are growing slower than they did before the pandemic, and despite an overall slowdown in demand for talent, employers still appear to be open to hiring AI specialists.

Whats interesting about the field of AI is that LinkedIn is seeing an entire ecosystem of technical roles that support different stages of the AI lifecycle. If you go back to the Emerging Jobs Report at the end of last year, AI specialist roles (people who build and train models, etc.) are up, but that so-called AI-adjacent jobs are also on the rise. This means that youre seeing more demand for data scientists, data engineers, and cloud engineers. Youre also seeing this demand growing across multiple industries, not just the technology sector. It is across the entire spectrum.

Future Impact of AI

At the end of the day, AI is a tool, and its greatest potential lies in how it will augment human intelligence and how it will enable people to achieve more. LinkedIns current AI tools depend greatly on human input and can never fully be automated.

Igor strongly believes that the future of AI is in applications and especially how we leverage that tool to make us all smarter and to enable us to do more. To do so, AI needs to be much more accessible to a wider set of individuals than just AI experts. AI needs to become more of a plug-and-play, almost a point-and-click interface. Hes seeing the major cloud players get into this space, developing tools that help lower the barrier of entry into AI. Once AI is application-driven, it opens up human creativity to develop really cool and interesting use cases.

In that context, AI technologies are really fascinating across the entire spectrum; from algorithmic and mathematical developments to hardware and AI systems. Just think about the ingenuity researchers have shown in attempting to make their deep neural nets simply converge. In the AI landscape, it seems that there are treasures behind every bush or under every rock.

More:

How AI Is Impacting Operations At LinkedIn - Forbes