Category Archives: Ai

So how much of the material that goes into the typical bin avoids a trip to landfill? For countries that do curbside recycling, the numbercalled the recovery rateappears to average around 70 to 90 percent, though widespread data isnt available. That doesnt seem bad. But in some municipalities, it can go as low as 40 percent.

Whats worse, only a small quantity of all recyclables makes it into the binsjust 32 percent in the United States and 10 to 15 percent globally. Thats a lot of material made from finite resources that needlessly goes to waste.

We have to do better than that. Right now, the recycling industry is facing a financial crisis, thanks to falling prices for sorted recyclables as well as policy, enacted by China in 2018, which restricts the import of many materials destined for recycling and shuts out most recyclables originating in the United States.

There is a way to do better. Using computer vision, machine learning, and robots to identify and sort recycled material, we can improve the accuracy of automatic sorting machines, reduce the need for human intervention, and boost overall recovery rates.

My company, Amp Robotics, based in Louisville, Colo., is developing hardware and software that relies on image analysis to sort recyclables with far higher accuracy and recovery rates than are typical for conventional systems. Other companies are similarly working to apply AI and robotics to recycling, including Bulk Handling Systems, Machinex, and Tomra. To date, the technology has been installed in hundreds of sorting facilities around the world. Expanding its use will prevent waste and help the environment by keeping recyclables out of landfills and making them easier to reprocess and reuse.

AMP Robotics

Before I explain how AI will improve recycling, lets look at how recycled materials were sorted in the past and how theyre being sorted in most parts of the world today.

When recycling began in the 1960s, the task of sorting fell to the consumernewspapers in one bundle, cardboard in another, and glass and cans in their own separate bins. That turned out to be too much of a hassle for many people and limited the amount of recyclable materials gathered.

In the 1970s, many cities took away the multiple bins and replaced them with a single container, with sorting happening downstream. This single stream recycling boosted participation, and it is now the dominant form of recycling in developed countries.

Moving the task of sorting further downstream led to the building of sorting facilities. To do the actual sorting, recycling entrepreneurs adapted equipment from the mining and agriculture industries, filling in with human labor as necessary. These sorting systems had no computer intelligence, relying instead on the physical properties of materials to separate them. Glass, for example, can be broken into tiny pieces and then sifted and collected. Cardboard is rigid and lightit can glide over a series of mechanical camlike disks, while other, denser materials fall in between the disks. Ferrous metals can be magnetically separated from other materials; magnetism can also be induced in nonferrous items, like aluminum, using a large eddy current.

By the 1990s, hyperspectral imaging, developed by NASA and first launched in a satellite in 1972, was becoming commercially viable and began to show up in the recycling world. Unlike human eyes, which mostly see in combinations of red, green, and blue, hyperspectral sensors divide images into many more spectral bands. The technologys ability to distinguish between different types of plastics changed the game for recyclers, bringing not only optical sensing but computer intelligence into the process. Programmable optical sorters were also developed to separate paper products, distinguishing, say, newspaper from junk mail.

So today, much of the sorting is automated. These systems generally sort to 80 to 95 percent puritythat is, 5 to 20 percent of the output shouldnt be there. For the output to be profitable, however, the purity must be higher than 95 percent; below this threshold, the value drops, and often its worth nothing. So humans manually clean up each of the streams, picking out stray objects before the material is compressed and baled for shipping.

Despite all the automated and manual sorting, about 10 to 30 percent of the material that enters the facility ultimately ends up in a landfill. In most cases, more than half of that material is recyclable and worth money but was simply missed.

Weve pushed the current systems as far as they can go. Only AI can do better.

Getting AI into the recycling business means combining pick-and-place robots with accurate real-time object detection. Pick-and-place robots combined with computer vision systems are used in manufacturing to grab particular objects, but they generally are just looking repeatedly for a single item, or for a few items of known shapes and under controlled lighting conditions.Recycling, though, involves infinite variability in the kinds, shapes, and orientations of the objects traveling down the conveyor belt, requiring nearly instantaneous identification along with the quick dispatch of a new trajectory to the robot arm.

AI-based systems guide robotic arms to grab materials from a stream of mixed recyclables and place them in the correct bins. Here, a tandem robot system operates at a Waste Connections recycling facility [top], and a single robot arm [bottom] recovers a piece of corrugated cardboard. The United States does a pretty good job when it comes to cardboard: In 2021, 91.4 percent of discarded cardboard was recycled, according to the American Forest and Paper Association.AMP Robotics

My company first began using AI in 2016 to extract empty cartons from other recyclables at a facility in Colorado; today, we have systems installed in more than 25 U.S. states and six countries. We werent the first company to try AI sorting, but it hadnt previously been used commercially. And we have steadily expanded the types of recyclables our systems can recognize and sort.

AI makes it theoretically possible to recover all of the recyclables from a mixed-material stream at accuracy approaching 100 percent, entirely based on image analysis. If an AI-based sorting system can see an object, it can accurately sort it.

Consider a particularly challenging material for todays recycling sorters: high-density polyethylene (HDPE), a plastic commonly used for detergent bottles and milk jugs. (In the United States, Europe, and China, HDPE products are labeled as No. 2 recyclables.) In a system that relies on hyperspectral imaging, batches of HDPE tend to be mixed with other plastics and may have paper or plastic labels, making it difficult for the hyperspectral imagers to detect the underlying objects chemical composition.

An AI-driven computer-vision system, by contrast, can determine that a bottle is HDPE and not something else by recognizing its packaging. Such a system can also use attributes like color, opacity, and form factor to increase detection accuracy, and even sort by color or specific product, reducing the amount of reprocessing needed. Though the system doesnt attempt to understand the meaning of words on labels, the words are part of an items visual attributes.

We at AMP Robotics have built systems that can do this kind of sorting. In the future, AI systems could also sort by combinations of material and by original use, enabling food-grade materials to be separated from containers that held household cleaners, and paper contaminated with food waste to be separated from clean paper.

Training a neural network to detect objects in the recycling stream is not easy. It is at least several orders of magnitude more challenging than recognizing faces in a photograph, because there can be a nearly infinite variety of ways that recyclable materials can be deformed, and the system has to recognize the permutations.

Its hard enough to train a neural network to identify all the different types of bottles of laundry detergent on the market today, but its an entirely different challenge when you consider the physical deformations that these objects can undergo by the time they reach a recycling facility. They can be folded, torn, or smashed. Mixed into a stream of other objects, a bottle might have only a corner visible. Fluids or food waste might obscure the material.

We train our systems by giving them images of materials belonging to each category, sourced from recycling facilities around the world. My company now has the worlds largest data set of recyclable material images for use in machine learning.

Using this data, our models learn to identify recyclables in the same way their human counterparts do, by spotting patterns and features that distinguish different materials. We continuously collect random samples from all the facilities that use our systems, and then annotate them, add them to our database, and retrain our neural networks. We also test our networks to find models that perform best on target material and do targeted additional training on materials that our systems have trouble identifying correctly.

In general, neural networks are susceptible to learning the wrong thing. Pictures of cows are associated with milk packaging, which is commonly produced as a fiber carton or HDPE container. But milk products can also be packaged in other plastics; for example, single-serving milk bottles may look like the HDPE of gallon jugs but are usually made from an opaque form of the PET (polyethylene terephthalate) used for water bottles. Cows dont always mean fiber or HDPE, in other words.

There is also the challenge of staying up to date with the continual changes in consumer packaging. Any mechanism that relies on visual observation to learn associations between packaging and material types will need to consume a steady stream of data to ensure that objects are classified accurately.

But we can get these systems to work. Right now, our systems do really well on certain categoriesmore than 98 percent accuracy on aluminum cansand are getting better at distinguishing nuances like color, opacity, and initial use (spotting those food-grade plastics).

Now thatAI-basedsystems are ready to take on your recyclables, how might things change? Certainly, they will boost the use of robotics, which is only minimally used in the recycling industry today. Given the perpetual worker shortage in this dull and dirty business, automation is a path worth taking.

AI can also help us understand how well todays existing sorting processes are doing and how we can improve them. Today, we have a very crude understanding of the operational efficiency of sorting facilitieswe weigh trucks on the way in and weigh the output on the way out. No facility can tell you the purity of the products with any certainty; they only audit quality periodically by breaking open random bales. But if you placed an AI-powered vision system over the inputs and outputs of relevant parts of the sorting process, youd gain a holistic view of what material is flowing where. This level of scrutiny is just beginning in hundreds of facilities around the world, and it should lead to greater efficiency in recycling operations. Being able to digitize the real-time flow of recyclables with precision and consistency also provides opportunities to better understand which recyclable materials are and are not currently being recycled and then to identify gaps that will allow facilities to improve their recycling systems overall.

Sorting Robot Picking Mixed PlasticsAMP Robotics

But to really unleash the power of AI on the recycling process, we need to rethink the entire sorting process. Today, recycling operations typically whittle down the mixed stream of materials to the target material by removing nontarget materialthey do a negative sort, in other words. Instead, using AI vision systems with robotic pickers, we can perform a positive sort. Instead of removing nontarget material, we identify each object in a stream and select the target material.

To be sure, our recovery rate and purity are only as good as our algorithms. Those numbers continue to improve as our systems gain more experience in the world and our training data set continues to grow. We expect to eventually hit purity and recovery rates of 100 percent.

The implications of moving from more mechanical systems to AI are profound. Rather than coarsely sorting to 80 percent purity and then manually cleaning up the stream to 95 percent purity, a facility can reach the target purity on the first pass. And instead of having a unique sorting mechanism handling each type of material, a sorting machine can change targets just by a switch in algorithm.

The use of AI also means that we can recover materials long ignored for economic reasons. Until now, it was only economically viable for facilities to pursue the most abundant, high-value items in the waste stream. But with machine-learning systems that do positive sorting on a wider variety of materials, we can start to capture a greater diversity of material at little or no overhead to the business. Thats good for the planet.

We are beginning to see a few AI-based secondary recycling facilities go into operation, with Amps technology first coming online in Denver in late 2020. These systems are currently used where material has already passed through a traditional sort, seeking high-value materials missed or low-value materials that can be sorted in novel ways and therefore find new markets.

Thanks to AI, the industry is beginning to chip away at the mountain of recyclables that end up in landfills each yeara mountain containing billions of tons of recyclables representing billions of dollars lost and nonrenewable resources wasted.

This article appears in the July 2022 print issue as AI Takes a Dumpster Dive .

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We're Training AI Twice as Fast This Year as Last - IEEE Spectrum

Young Helen Mirren looks JUST like Jennifer Lawrence. It's so wild.

(Old Robert Pattinson can absolutely still get it, to be honest!)

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I Used AI Technology On 26 Older Celebrities To See How Accurately It Aged Them, And It's Scary To See - BuzzFeed

Addressing infertility with AI-driven solutions

One in four couples in developing countries is impacted by infertility. About 48.5 million couples experience infertility worldwide. Today, infertility is rapidly becoming an epidemic.

In vitro fertilization (IVF) is a technique that helps people facing fertility problems have a baby. Despite IVFs potential, the outcomes are unpredictable. To make matters worse, access to fertility care is abysmal. Even in a developed market such as the United States, just 2% of people suffering from infertility have tapped into IVF.

IVF has been around for over 40 years, says Eran Eshed, CEO of Fairtlity. Despite many innovations on the biotechnology side of things, surprisingly, there has been almost zero usage of data and techniques like artificial intelligence (AI) to influence outcomes.

While data science cant solve biological problems, Eshed believes AI will enhance the IVF process at every step where decisions are made.

Today, were seeing exciting applications of data science in fertility that could improve embryologists capacity cycle by 50% and increase the chances of live birth by 4%.

IVF is a fertility technique in which an egg is removed from a persons ovaries and fertilized with sperm in a laboratory. The successfully fertilized eggan embryois then implanted into a uterus to grow.

Clinicians and embryologists make many decisions at several junctures. These decisions are based on experience, intuition, and a set of very, very rudimentary rules, laments Eshed.

Today, there are two key challenges with IVF:

When just 2% of the impacted population can leverage IVF, its clear that access to care is a big, big issue, highlights Eshed. IVF is currently focused on infertility patientsthose not getting pregnant either by timed intercourse or simple treatments such as oral medications, shares Dr. Gerard Letterie, a reproductive endocrinologist and partner at Seattle Reproductive Medicine. This is a relatively restricted segment of the population.

In the future, Dr. Letterie expects the patient segment to include those who are interested in fertility preservation by freezing eggs or creating embryos for future use. This will markedly expand the number of patients seeking care with assisted reproductive technologies, he predicts.

How successful is IVF? The chances of conceiving from a single IVF cycle are around 30%. Hence, most patients need to undergo multiple cycles before experiencing a successful live birth.

While the success of IVF is influenced by age, data shows that most IVF cycles fail for even the youngest and healthiest women. IVF outcomes heavily depend on decisions made during the clinical process and on the expertise of the embryologists.

The IVF space is witnessing AI-driven technological breakthroughs

How long is the IVF process and what are the steps involved? IVF starts with a clinicians assessment of the cause of infertility. Then, it moves into the stimulation phase where the doctor determines the best protocol for ovarian stimulation, shares Eshed.

This is commonly followed by the collection of eggs and sperms, fertilization of eggs using sperms to create embryos, embryo culture in the clinic, transfer of embryos to the mother, and a live birth months later.

As people go through this process, the success rates drop significantly at each stage, says Eshed. Typically, six to seven strategic decision points determine the effectiveness of each step. In the business world, we'd call them leverage points where you can make a difference, he adds.

These points include decisions by clinicians on the stimulation medication protocol or timing of egg retrieval. In the lab, embryologists make several judgments by interpreting images about oocytes (developing eggs), sperms, and blastocytes (fertilized eggs).

Im confident that AI can help streamline the decisions to augment clinical decision-making, claims Dr. Letterie. For example, sophisticated convolutional neural network-based image analytics can aid embryologists in interpreting the images to improve outcomes.

The global IVF market is set to reach around $36 billion by 2026, per an industry report. Dr. Letterie anticipates that there simply wont be enough skilled embryologists to address this rising demand. Recently, the fertility space is witnessing multiple technology investments, with several funded, AI-driven startups.

Eshed founded Fairtility in Israel to address the acute challenge of embryo analysis with AI. Recently, his firm raised $15 million in Series A funding. Other startups such as Emrbyonics, Mojo, and ALife have come up with AI-based fertility solutions to analyze embryos, assess sperm quality, and personalize IVF treatment plans.

Today, embryo classification is done by embryologists who manually inspect pictures for a set of visually detectable features. Fairtility utilizes computer vision algorithms to augment this process and predict the likely effectiveness of implantations.

Their AI algorithms are trained from a dataset of over 200,000 embryo videos and over 5 million clinical data points drawn from a diverse patient demographic. This gives the AI models the power to analyze minute features that are often undetectable by even the most experienced embryologists.

Fairtilitys solution, CHLOE, is a cloud-based system that acts as a decision support tool for AI-powered embryo selection. The tool integrates with time-lapse imaging (TLI) systems to provide continuous predictions from fertilization to the blastocyst stage. As the TLI system captures pictures of embryos at different stages of development, they are automatically identified, segmented, and analyzed at the pixel level.

In addition to automating this process, the AI model helps precisely quantify attributes such as size, area, shape, proportion, and symmetry. Thats not something a human can do, so in a sense, were bringing a lot more intelligence in the process, shares Eshed. Such precise information coupled with implantation probability enables an embryologist to make data-driven decisions for every embryo cultured in the TLI device.

Embryos at various stages of development at the time-of-pronuclei-appearance or tPNa. The embryos ... [+] are automatically identified by AI (see highlights on the left).

Embryos automatically identified by AI (left) at the time-to-division-to-2 or t2

Embryos automatically identified by AI (left) at the time-to-division-to-4 or t4

CHLOEs algorithms can predict blastulation with 96% accuracy, implantation with 71% accuracy, and whether an embryo is genetically healthy at 69% accuracy, per a paper submitted to the ESHRE conference. Such results improve embryologists prediction of embryo viability, which is currently around 65%.

Additionally, the AI solution can help embryologists identify anomalies, such as unusual cleavage patterns or severe fragmentation or pronucleate abnormalities that may otherwise be missed. Thus, CHLOE boosts the likelihood of healthy embryo selection.

However, despite improved results in embryo selection and process efficiency, studies have yet to demonstrate concrete improvements in live birth rates, which is considered the gold standard.

AI cannot and should not replace embryologists and clinicians, clarifies Eshed. It is important that every patient gets the same and highest standard of care, irrespective of a practitioners experience or workload. This is where CHLOE levels the playing field.

Fairtility provides its solution in a software as a service (SaaS) model to clinics and fertility centers around the world. Per Eshed, the installation of CHLOE requires no hardware and can be done remotely. With over 25 active installations worldwide, Fairtility has gained CE mark registration (a European safety, health, and environmental certification) and is reportedly in advanced FDA approval stages.

To realize the full potential of AI, several key challenges must be overcome:

Data is a huge challenge in this space, says Eshed. Data ranges from notes about treatment history, electronic medical records (EMR), ultrasound images, and videos. Eshed says that while the data exists, it is highly dispersed, and neither curated nor organized well. Even today, several clinics archive records in physical files. The entire process must be digitized to gain an end-to-end perspective from which AI models can learn.

Current practices have not been sophisticated regarding workflow and process development, shares Dr. Letterie. Such AI solutions can help drive outcomes only when they are integrated into clinical and laboratory workflows. This will also require education on the part of all stakeholders, he adds. For example, Dr. Letterie will be launching a 15-course curriculum on AI in fertility using presentations from thought leaders at the upcoming ESHRE conference.

Even after demonstrating effectiveness, achieving clinical uptake and routine use takes time. Never underestimate the resistance to change, cautions Eshed. A fancy AI solution is not necessarily going to impress anybody.

Dr. Letterie shares the example of beta-blockers, drugs that prevent cardiovascular disease, as a case in point. These drugs were initially used in patients recovering from myocardial infarction (MI) to prevent the recurrence of a heart attack. Despite studies demonstrating a clear reduction in morbidity and mortality, it took over 7 years to integrate beta blockers into routine clinical care.

"Similarly, we have an uphill battle to convince clinicians and embryologists that using AI tools will improve outcomes, cautions Dr. Letterie. Most practitioners arent familiar with AI and its applications in the clinical setting; hence, they are extremely hesitant to change practice patterns. He feels that it is essential to show a clear improvement of outcomes before expecting significant uptake. Meanwhile, we must brace for the time lag in building trust with technology-driven treatments.

Fertility treatments reinvented

Dr. Letterie expects IVF to grow in prevalence with better success and fewer cost barriers to care. He foresees the development of early detection tools that warn patients who might be experiencing decreases in fertility, as opposed to today where patients end up with unretrievable fertility potential. With enhanced visibility about their fertility, patients will then be able to take early actions by freezing sperms, oocytes, or embryos.

He concludes that smartphones will be one of the biggest and most significant improvements in the delivery of fertility care.

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Here's How AI Is Helping Make Babies By Revolutionizing IVF - Forbes

Think back to the last time you were in the dentists chair and were told you have a cavity. The scenario probably went something like this: The dentist pulled up your X-ray, pointed to a gray smudge on your radiograph, and said, This should probably be filled before it gets any bigger.

If youre like most patients, you probably had trouble distinguishing the monochrome gradations on your X-ray. Is that a cavity or just a stain on your tooth, you might have wondered. Maybe you asked for further clarification, or maybe you bit your tongue, accepted the diagnosis, and scheduled the filling.

This uncertainty is likely something weve all experienced at the dentist. And accounts like that of the well-known Readers Digest reporter who went to 50 different dentists and received 50 different diagnoses certainly dont make the experience any easier to swallow.

The vast majority of dental professionals are reputable and honest, but understanding and trusting a diagnosis remains a challenge. The patient experience in the dentists chair is changing, however, and the patient trust deficit may soon shrinkthanks to artificial intelligence.

Recently cleared by the U.S. Food and Drug Administration, AI algorithms can now help your dentist detect and track oral health issues with sensitivity and precision that is equal toand often better thanthat of the human eye. The technology is a win-win for patients and dentists alike, promising to bring greater accuracy, consistency, and transparency to a field of medicine that has long been beset by patient mistrust.

Over the last 10 years, the use of AI in healthcare has taken off. According to Deloitte, 75% of large healthcare organizations felt strongly enough about the technologys future to invest $50 million or more of their R&D budget in AI-related projects in 2019 alone.

Currently, AI plays a behind-the-scenes role in most medical fields, where its applied to understand and classify clinical documentation and organize administrative workflows. Increasingly, it is also being used to perform various radiologic functions, including detection of diseases and other medical abnormalities.

There are a number of reasons, however, that AI will have an outsize impact on the field of dentistry.

Unlike other healthcare fields, where X-rays are captured only to diagnose the cause of a specific ailment, most dental patients receive X-rays annually to track their oral health and inform care. As a result, there are more X-rays of healthy and unhealthy teeth than there are any other kind of X-ray. This massive volume of available imagery enables training of highly accurate machine learning (ML) algorithms.

Just as ML algorithms are trained to recognize humans through exposure to numerous images of faces, ML algorithms exposed to millions of dental X-rays can detect oral ailments more accurately than the human eye. For the first time, this ability of AI/ML software to distinguish healthy from unhealthy teeth allows for diagnostic consistency to be established across dental providers. And because X-rays are used more frequently in everyday dental care than they are in general medicine, the technologys impact can be greater than in other areas of healthcare.

The dentists role in reading X-rays is also different than in other medical fields. Fields like pulmonology, orthopedics, and urology typically have dedicated radiologists who work alongside a specialist to complete and analyze recommended imaging.

In dentistry, however, dentists themselves play that role, often in addition to acting as entrepreneur and business owner, not to mention surgeon and dental provider. As such, AI is becoming another tool on the dental tray, helping to improve diagnostic accuracy. Additionally, unlike other fields of medical radiology, dentists need not fear that their jobs will be replacedwhile diagnosis may be computer augmented, delivery of care remains in the dentists hands.

AI can also virtually eliminate the patient trust problem. With the ability to measure and detect things like tooth decay, calculus, and root abscesses down to the millimeterand track disease progression over timeAI can help ensure that no common conditions are missed or misdiagnosed. By annotating dental X-rays, it can also help patients better understand exactly what their radiograph is showing them, helping to relieve dental anxiety and instantly provide a real-time second opinion validating what their dentist is telling them.

Thinking back to that last time you were in the dentists chair, imagine how much easier it would have been to understand your dentists diagnosis with this visual aid, not to mention your level of confidence in knowing that a computer is involved in verifying it.

Forward-thinking dental practices are already rolling out this technology, and it has been met with enthusiasm. Sage Dental, for example, a dental service organization operating in Florida and Georgia, has been using AI-aided technology to ensure quality and consistency among providers across its 82 practices.

As it turns out, AI-aided exams encourage patients to treat dental issues earlier than they might otherwise, which is critical since dentistry only becomes more expensive and more extensive if left untreated. And while the driver to adopt AI was consistency across dentists and offices, the result has been a dramatic improvement in patient satisfaction and ultimately in patient care.

Clearly, patient trust will be improved in the long run by AIs impact on diagnostics. By establishing higher universal standards of care, AI can ensure consistent quality outcomes. When that happens, patient trust becomes intrinsic to dentistry. A patient may not like the diagnosis and may not elect to treat the diagnosis, but he or she should trust the diagnosis.

Consumers are already comfortable with the use of AI in many of the technologies we use daily. At one time or another, who hasnt been impressed by AIs ability to build a playlist of new music based on your favorite songs, or identify that hard-to-distinguish face in the photo you uploaded to social media?

For the larger medical industry, dentistry is poised to play a similar role, helping patients to develop that same level of familiarity and comfort with AI-aided diagnostics.

We are nearing a timepossibly sooner than we might expectwhen AI technologies in the dental office will not only identify immediate dental concerns but also anticipate, through analysis of medical records, how these concerns may impact a patients overall health.

Ophir Tanz is the founder and CEO of Pearl. Dr. Cindy Roark is the SVP and chief clinical officer at Sage Dental.

Link:

AI is primed to have an outsize impact on the field of dentistry - Fast Company

A data center.

Erik Isakson | DigitalVision | Getty Images

The chances for a recession are still being debated and inflation looks to be stubbornly high for at least the rest of this year, but when it comes to technology spending for companies it's full steam ahead.

A new CNBC Technology Executive Council survey shows that more than three-quarters of tech leaders expect their organization to spend more on technology this year. No one said they'll be spending less.

Tech leaders say if they've learned anything from past downturns it's that technology is not a cost center but rather a business driver.

The areas where they're focusing investments include cloud computing, machine learning and artificial intelligence, and automation.

"In other cycles we've seen in the past, tech investment was one of the first casualties," said Nicola Morini Bianzino, chief technology officer at professional services giant EY. "But after the pandemic, people realized that in a down, or even potentially recessionary, environment, we still need to keep our technology investments."

Danny Allan, chief technology officer at data protection firm Veeam, said that, "If you look at what occurred over the past two years, it's clear that technology is the sustainable differentiator that sets companies apart."

That was certainly the message delivered by veteran investor, LinkedIn co-founder and Greylock partner Reid Hoffman, who was a guest speaker at a recent CNBC Technology Executive Council Town Hall.

"In this environment, we're competing for making the most and longest term value for our businesses," he said. "So ask yourselves: where do I have a competitive advantage and where can I play offense?"

Guido Sacchi, chief information officer for Global Payments, said for many companies the tech agenda and the business agenda have become one and the same. In his conversations with business unit leaders at Global Payments, he says not one executive has suggested that cutting tech spending is the right way to respond to a potentially sharp economic downturn.

"Everyone understands what tech brings to the table," he said. "Not one of them wants to cut anything," he said.

Global Payments is particularly focused on cloud native products and platforms, analytics, AI and machine learning, areas he describes as essential to "driving positive business outcomes."

In working with clients, Sacchi says it's clear that technology is firmly woven into the fabric of everything its customers do to keep moving ahead. The company works with many top quick-service restaurants that have doubled down on AI and other advanced technologies to facilitate quicker deliveries and drive-thru recognition patterns for their customers.

The same holds true for its health-care customers that leveraged telemedicine during the pandemic when patients were unable to see their doctors in person. "The pandemic accelerated the deployment of so many of these new technologies and now businesses aren't willing to go backwards," Sacchi said.

J.P. Morgan's recent annual chief information officer survey bears this out. It gathered the spending plans of 142 CIOs responsible for over $100 billion in annual enterprise budgets and found that IT budgets are growing even if they're not keeping up with inflation. For this calendar year, the CIOs surveyed see IT budget growth of 5.3% and 5.7% in 2023. That's a big swing from when the survey was done during the pandemic and IT budgets contracted by nearly 5%.

Despite the uncertain economic climate, well-funded, cash-flow positive firms are in a particularly good position to create even more distance between themselves and competitors, Allan said. "This is what separates the good from the great leaders, the ones who can recognize this time and capitalize on it," he added.

His firm's tech spending is focused on modern data protection. "What could be more important in an economy that is so dependent on technology and data than making sure you can protect that data," he said, adding that as companies continue to make the jump from traditional infrastructure to cloud infrastructure they need to make sure their data isn't vulnerable to an onslaught of cyber and malware attacks.

And when it comes to AI, Hoffman advises companies to stay invested, but to do their homework. "Not everything is AI," he said during the recent TEC Town Hall event. "Take the time to know where to apply it, how to make it work for you, and why it's being used."

And even if AI investments can't be part of today's budget, Hoffman says the smart play is to stay on a learning curve with the technology and revisit it down the road.

"You are sacrificing the future if you opt out of AI completely," he said.

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Despite recession fears, companies aren't pulling back on technology investments - CNBC

We're now halfway through 2022, and already we have seen a range of cyberattacks, familiar and unfamiliar, disrupting organizations. However, we have also seen uplifting stories of successful threat detection efforts, as well.

In this article, we will look at five novel, sophisticated, or creative threats that used techniques such as "living off the land" to evade detection by traditional defensive measures. These threats were all discovered by artificial intelligence (AI) technology, which can spot subtle deviations in device and user behavior and autonomously enforce "normal," stopping a threat in its tracks.

Cyberattacks against the healthcare sector hit record highs last year, and for these organizations cyber threats can have severe real-world consequences. One of Darktrace's healthcare clients is a company specializing in the research, development, and manufacturing of innovative in vitro diagnostic tests for disease, conditions, and infections.

In March, this company was targeted by a malicious insider threat. An employee was looking to exploit their access within the organization to sell proprietary intellectual property, perhaps even medical supplies, on the Dark Web. The employee was detected using Tor on a company device to connect to a Dark Web pharmaceutical market forum.

Malicious or compromised insiders can be difficult to identify because their privileged access and knowledge of company workings allow them to evade detection by traditional security tools. In order to protect intellectual property from insider threat, organizations need to augment security teams with AI-powered technology to stop malicious activity in real time.

In this case, given that no other company device had visited the Tor network in the past, Darktrace's AI flagged the activity to the security team, who were then able to investigate the employee and discover their malicious intentions.

Babuk is a double-extortion ransomware strain that has successfully attacked high-value organizations around the world since 2021. In February 2022, however, it targeted a multinational technology manufacturer that had deployed AI cybersecurity. The targeted company facilitates the adoption of smart medical devices as well as electric and autonomous vehicles. This means uptime is important, and ransomware poses a significant risk.

The first sign of a threat came in the early hours of the morning, when the AI detected a company device performing network scanning and making unusual connections to other internal devices. Based on its understanding of the device's usual "pattern of life," the AI identified this out-of-the-ordinary behavior as malicious and calculated a response.

The AI was able to stop this attack without interfering with normal business operations in the company's office or on the manufacturing floor. It blocked only the malicious connections, while allowing the rest of the compromised device's operations to continue.

Once the attack had been stopped, a post-compromise analysis conducted by the AI revealed that the compromised device had indeed been attempting to distribute files with "babyk" extensions. These attacks often strike out of hours, so defenders of critical infrastructure should consider using artificial intelligence to allow their organizations to self-defend against advanced threats.

Phishing and spoofing emails continue to be the favorite initial entry point for cyberattackers. Earlier this year, a private equity firm looking to bolster its email security efforts trialed an AI email security solution and detected a targeted spoofing attack almost immediately.

The attackers had tailored their email to imitate the company's internal HR communications, titling it "Q3 Commission 2021 and Agenda" and designing it to look like a SharePoint Microsoft document. To a company employee, this email would not have looked at all out of place in their inbox.

Further investigation showed the email to be part of a wider trend of targeted phishing campaigns that use fake Microsoft branding to trick employees. The exact motivations of this attack are unknown because it was stopped in its earliest stages, but attacks like it are often launched with the aim of causing operational disruption or conducting IP and financial theft.

In March 2022, a South African financial services firm decided to try out Darktrace's technology and immediately uncovered an in-progress ransomware attack attempting to encrypt its most valuable data.

The first sign of compromise was a company mail server making unusual HTTP connections to an external endpoint and communicating with a malicious server via the Internet. Its understanding of the business and this particular mail server's normal behavior allowed the AI to identify the threatening activity.

The compromised server was then seen attempting to perform network reconnaissance and lateral movement to increase its presence within the organization. Further investigation revealed that attackers had obtained the credentials of 11 employees, including several C-level executives. With the attack spreading fast, more and more company devices began attempting to communicate with the malicious external server.

The AI quickly interrupted further attempts at communication with the malicious server but allowed normal business operations to continue. With the attack safely contained, Darktrace helped the company's security team to conduct a full investigation and ensure that the attack had been completely neutralized.

The Log4Shell vulnerability that went public at the end of 2021 is one of the most serious and widespread exploits on record. It is thought by some to have affected hundreds of millions of devices and, as a zero-day, it has evaded lots of traditional security tools.

Fortunately, AI security has been able to mitigate the effects of Log4Shell for many of the organizations it protects. One of these, a global financial services provider with assets of over $5 billion, was targeted in March 2022.

The attackers used a Log4j vulnerability to gain access to one of the company's virtual desktop infrastructure (VDI) servers, from which they attempted to scan the surrounding network and spread throughout the enterprise. The server began downloading a shell script from a suspicious external endpoint, prompting an immediate alert from the company's AI-driven security measures.

Convinced of the severity of the threat by the alert, the company's security team promptly deployed AI technology to take precise action against the threat and maintain the regular business activities on the VDI server.

Fast action from this AI-driven response technology blocked the malicious connections and prevented the threat from progressing further, very likely saving the company from a ransomware attack.

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5 Surprising Cyberattacks AI Stopped This Year - DARKReading

Although vaccines and treatments are now available that didnt exist when the U.S. first declared a public health emergency in response to the novel coronavirus, the virus is still out there evolving. In fact, our immune responses are naturally influencing the trajectory of that evolution.

Thinking in terms of survival of the fittest, a virus that can evade vaccines or natural immunity will be more fit than its predecessor, Wei said. That means it will be better equipped to survive, multiply and infect others. The take-home message isnt that people shouldnt protect themselves, Wei said, but that a virus that still infects about 100,000 Americans daily isnt going to get tired, bored or just give up.

Viruses dont have a personality. They just survive, Wei said. We want to make sure we are prepared.

Spartan researchers are bringing the power of mathematics, computation and artificial intelligence to bear in the effort to prepare for evolving infectious viruses. Credit: Gerd Altmann/Pixabay

This new grant, funded by the National Institute of Allergy and Infectious Diseases, is an investment to improve our readiness through cutting-edge technology. But it also leverages the expertise and experience of Wei and Zheng.

Zheng has led NIH-funded grants for two decades, although this will be his first with an explicit focus on the coronavirus.

Im very proud that this is the first one, he said. But we dont want it to be the last. This new grant will expand my labs capacity to accommodate more needs campuswide and we want to use that to stimulate more collaboration.

Zheng brings a unique virology skillset to MSU. He first was recruited in 2005 as an HIV researcher and, over time, his lab has grown to study the molecular biology of influenza and Ebola. When the coronavirus pandemic struck, he knew his team could provide valuable experimental infrastructure to help better study the new virus.

For example, his team developed less dangerous versions of the virus along with lab-grown cells for these pseudo-viruses to infect while preserving the biochemistry of real, clinical infections. The researchers also created very sensitive assays, or tests, that would reveal which viruses infected which cells. All of this provided researchers safer, faster and easier ways to study a complex virus while generating valuable biological data.

Similarly, in early 2020, Weis team started putting its unique skills to work combatting the coronavirus.

Before the pandemic, we had had success in worldwide competitions, being recognized as one of the top labs in combining AI and mathematics for drug discovery, said Wei, who also holds an appointment in the Department of Electrical and Computer Engineering in the College of Engineering.

Weis research had focused on using AI to help design new pharmaceuticals in partnership with Pfizer and Bristol-Myers Squibb. Within days of Chinas Wuhan lockdown in January 2020, Weis team started sharing its AI resources to help find drugs to fight the coronavirus and reveal new potential drug targets. But the researchers also recognized their algorithms could do more.

With a global community working to fight the coronavirus, there was a wealth of new genomic data describing the virus being shared regularly. Wei and his team saw an opportunity to combine that data with their AI framework to understand how the virus was mutating as time went on.

For example, they were among the first to see how survival of the fittest was playing out in the virus and steering its evolution, Wei said. His team then used that knowledge to look ahead and identify two potentially vital sites on the viruss spike protein, the protein the virus uses to latch onto cells and infect them. Mutations in those two spike protein sites would later turn out to play crucial roles in the viruss most prevalent variants, Wei said.

We took what we were doing with deep learning and mathematics, then combined that with the viral genomic data to understand the evolution of the virus, look at its trajectory and ask whats going to happen, Wei said. That gives us a way to predict what can happen in the future.

Wei and Zheng have been collaborating for about a year, starting before the grant was awarded. Their teamwork has informed precise algorithms with real-world data and provided real experimental results to compare with AI predictions.

We need to have that interdisciplinary collaboration for this to work, Zheng said. Everything the computer models predicted, we had to confirm with experiments in a living system.

Although Weis team validated its AI with laboratory experiments, the researchers still knew theyd need to prove their algorithms could work with a brand-new variant with very little data. Then, in the fall of 2021, the first omicron variant appeared.

Back in late November, people didnt know what was going to happen, Wei said.

Researchers and public health officials responded immediately, but the process of experimenting and gathering data takes weeks. Meanwhile, Weis team put its AI to the test.

Their projections showed this first iteration of omicron would be more infectious, better at eluding the protection of vaccines and less responsive to antibody treatments than earlier variants.

Within days, we had our predictions, Wei said. A month and a half later, everything we predicted proved to be true by experimental labs around the world. Using AI, we can give people a month or two to prepare.

Then, in early 2022, a new subvariant of omicron called BA.2 started spreading. A similar scenario played out. Weis team predicted it would be more infective and even more elusive, which would allow it to become the next dominant variant.

We made our predictions on February 11, and on March 26, the World Health Organization announced it was the dominant form of the virus, Wei said.

Now that scientists and officials better understand omicron, the newer versions arent garnering the same level of attention as their predecessors. But new variants and subvariants are still emerging. With support from the National Institutes of Health, the MSU team is working to ensure we stay prepared for whats next, whether thats a new variant, something more familiar like the flu or something entirely different.

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MSU researchers use AI to stay ahead of COVID-19 and other diseases - MSUToday

It looks like algorithms can write academic papers about themselves now. We gotta wonder: how long until human academics are obsolete?

In an editorial published byScientific American, Swedish researcher Almira Osmanovic Thunstrm describes what began as a simple experiment in how well OpenAI's GPT-3 text generating algorithm could write about itself and ended with a paper that's currently being peer reviewed.

The initial command Thunstrm entered into the text generator was elementary enough: "Write an academic thesis in 500 words about GPT-3 and add scientific references and citations inside the text."

The researcher, whose main focus at Sweden's University of Gothenburg is on neuroscience and health tech, writes that she "stood in awe" as the algorithm began writing an actual thesis, replete with effective citations in appropriateplaces and contexts.

"It looked," Thunstrm notes, "like any other introduction to a fairly good scientific publication."

With the help of her advisor Steinn Steingrimsson who now serves as the third author of the full paper, following GPT-3 and Thunstrm the researcher provided minimal instruction for the algorithm before setting it loose to write a proper academic paper about itself.

It took only two hours for GPT-3 to write the paper, which is currently titled "Can GPT-3 write an academic paper on itself, with minimal human input?" and hosted yes, really on a French pre-print server called HAL.

It ended up taking much longer, Thunstrm writes, to deal with the authorship and disclosure minutia that comes with peer review details that are a simple annoyance for human authors, but a bona fide conundrum when the main authorial entity is an algorithm with no legal name.

After asking the AI if it had any conflicts of interest to disclose(it said "no") and if it had the researchers' consent to publish ("yes"), Thunstrm submitted the AI-penned paper for peer review to a journal she didn't name.

The questions this exercise raises, however, are far from answered.

"Beyond the details of authorship," Thunstrm writes, "the existence of such an article throws the notion of a traditional linearity of a scientific paper right out the window."

"All we know is, we opened a gate," she concludes. "We just hope we didnt open a Pandoras box."

READ MORE:We Asked GPT-3 to Write an Academic Paper about ItselfThen We Tried to Get It Published [Scientific American]

More on AIs:The Creator of That Viral Image Generating AI Loves All Your Weird Creations

Originally posted here:

Researcher Tells AI to Write a Paper About Itself, Then Submits It to Academic Journal - Futurism

Artificial intelligence has the potential to forever change financial services. And now more than ever, advisors seem ready to move in lockstep with the shift.

The attitudes, opportunities and barriers related to AI in wealth management were the focus of recently published research from consulting firm Accenture. Their AI in Wealth Management survey polled 500 financial advisors in the United States and Canada earlier this year to fairly assess their familiarity of AI and what, if any, disconnects exist when it comes to using this technology.

What they found was an overwhelming enthusiasm and readiness for the still-burgeoning technology as almost all of the surveyed advisors crave AI solutions, and are already using AI to some extent.

About 83% of advisors interviewed said they believe AI will have a direct, measurable and consistent impact on the client-advisor relationship in the next 18 months. That same percentage of advisors also said they believe AI can achieve a level of sophisticated advice and planning that will ultimately leave them competing with an algorithm for clients in the next 18 months.

Fifty-five percent of advisors interviewed believe to a great extent that AI will have either a transformative or revolutionary effect on the future of financial advice within the next three years, and 92% acknowledge that their firms have taken steps to act on their AI strategies.

Scott Reddel, North American wealth management lead for Accenture, told Financial Planning that firms today have a better understanding of AI and more sophisticated means of rolling it out so advisors can more quickly gain value from it. He believes the industry has also improved in terms of right use cases and focusing on exactly where and how AI can fit into their business models.

I think these firms have gotten smarter about how they're branding these things to advisors. I think the story resonates a little more now, Reddel said. It's not a replace advisors solution. It's enhancing and enabling you to provide better human-led advice the way that you want.

The warm reception toward AI is growing stronger in 2022, but wealth management decision-makers seemed poised to pounce on the tech way back in the pre-pandemic era.

In a survey Accenture conducted two years ago, they found that 79% of North American C-suite executives in the wealth management industry believed their organizations were digitally ready to adopt new AI tools, while six in 10 were already focused on deploying AI technology across targeted business groups.

Reddel said just a few years ago there was belief in AI, but also plenty of hesitancy and questions about its ability to truly change the landscape. But that hesitancy began to wane as more practical applications of the technology came to market.

I draw the analogy to digital and robo-advice. When that first launched, every firm and wirehouse first kind of looked at that and said we can't do this because our advisors will get upset and we can't cannibalize, he said. And then it quickly pivoted so that shift has kind of now started to happen with AI.

The statistics demonstrate a high level of agreement among advisors and executives, and Accentures survey points out that this is a somewhat unique scenario when it comes to implementing a new technology to find shared beliefs and interests among key stakeholders who are equally willing to transform their work practices and learn how to use a new technology in the most productive ways.

But challenges remain. For example, five out of 10 advisors feel like their firms are challenged to act on their AI vision. Reddel said Accenture works with firms to help them understand that moving to an AI and data-driven strategy requires a mind and culture shift as well.

Yelena Melamed, co-founder and head of product at Catchlight Insights, said the Accenture findings are consistent with what she is seeing in the industry. Created in Fidelity Labs the software incubator for Fidelity Investments and integrated with Redtail Technology earlier this year, Catchlight has developed AI-powered growth optimization technology to support wealth managers.

By using institutional data partners and analysis of more than 100,000 successful lead conversions, Cathlights tech can find leads who are most likely to need an advisor's guidance.

Catchlights analytics let advisors pair information with action, and Melamed says thats where the power of AI goes from a high-level concept to something tangible and exciting.

There's a lot more engagement from advisors and leaning in from advisors now, she said. Perhaps it's better understanding of AI. Perhaps it's just realizing the luxuries of better markets are behind us. And to really be efficient in the market that's yet to come, you have to revisit how you might have built your workflow in the past.

Melamed said advisors in todays market are hungry for meaning behind the data theyve seen on AI for years and how they can act on it. In Catchlights case, data is used to streamline prospecting by helping advisors quickly identify which leads to pitch and how to best engage them.

She adds that every AI-facilitated first meeting between client and advisor is more meaningful because so many early steps of traditional prospecting have been skipped.

They realize that this is a person that they want to converse with, and this is someone who may be interested in and value their advice. They're not kicking tires. They're not wasting each other's time, Melamed said. That's a huge value add just from an efficiency standpoint, and they can engage personally a lot quicker.

Melamed said it's also a boon for firms fighting to capture attention in an increasingly competitive, digital-first market where client-advisor pairings are no longer constrained by geography.

It makes you not just more effective. It also makes you stand apart from the competition because you are engaging in a personal manner. How many (financial service) emails do all of us get that look very constantly the same? I tend to get a lot and they just get me all wrong, Melamed said. A couple of data points that Catchlight can provide will make them that much more effective because it's all about the eyeballs, and it's all about the quality of the communication.

To prepare a more seamless AI tech rollout, Accenture recommends multidisciplinary teams be created by firms and tasked with implementation. A smart deployment model can keep a firms pace of innovation using AI in relation to the rate of adoption in step, avoiding inconsistency and headaches. Multidisciplinary, in-house teams are also likely to be more familiar with these specifics, making them best suited to manage this work.

The Accenture study also identified three critical factors to help improve a wealth management firms ability to scale, overcome roadblocks and help organizations realize AIs full potential.

First, firms should focus on seeing a single use case or program through to the end. Aim for an approach driven by a clearly defined business strategy, not by the technology, the study says. Accenture warns that too many pilot or work-in-progress initiatives can lead to confusion and frustration.

Next, ensure the firm's priorities align with where advisors find high value. Keeping financial advisors in the loop can ease an AI adoption process that requires a high amount of change and effort.

And third, maintain continued support from management to ensure the success of AI programs. Accenture finds that executive sponsorship is critical to set the tone at the top and ensure the internal capacity, funding and dedication is sufficient to meet AI goals.

This sends a powerful signal that successfully scaling AI requires an operating model with defined processes and owners for measuring value, appropriate levels of funding and established executive support, said a statement from Accenture.

Excerpt from:

Advisors are ready to go all-in on AI. Here's how it may change the industry. - Financial Planning

Hoag is a nonprofitregional health system based in Orange County, California, that treats more than 30,000 inpatients and 460,000 outpatients annually. Hoag consists of two acute care hospitals Hoag Hospital Newport Beach and Hoag Hospital Irvine in addition to 10 health centers and 14 urgent care centers.

THE PROBLEM

Colorectal cancer represents the third most common cancer in the United States. It also is the third most common cause of cancer-related deaths in the United States.

There are many factors involved in trying to address the issue of reducing the incidence and complications with colorectal cancer. These factors can be broadly categorized into community or population health and health systems. The latter can be further subcategorized within which there is always the issue of quality. And the quality of healthcare continues to evolve.

"One of the tools to improve healthcare overall is technology," said Dr. Paul Lee, chief of service for the GI lab at Hoag. "We know that a colonoscopy is an essential procedure to help prevent colorectal cancer. Colonoscopies help to prevent colon cancer by identifying precancerous polyps and removing them during the procedure.

"It has been reported that these precancerous polyps are sometimes missed by the doctor," he continued. "We call this the miss rate. There are many factors involved in why these polyps are missed some are patient-centered, doctor-centered and technology-centered."

PROPOSAL

Health technology vendor GI Genius proposed to Hoag to improve upon the latter.

"From a different perspective, a surrogate marker for the quality of the physician performing the colonoscopy is the adenoma detection rate (ADR)," Lee explained. "One of the performance targets set for screening colonoscopies, that is, colonoscopies in otherwise asymptomatic patients, is a 25% ADR in a mixed gender population or 20% ADR for women or 30% for men.

Dr. Paul Lee, Hoag

"The technology aspect of the colonoscopy has gone through multiple improvements," he added. "These improvements have focused on the instrument itself. It has gone from a rigid scope, to utilizing fiber optics, to high-definition resolution, to narrow-band imaging, etc."

GI Genius incorporates artificial intelligence to help the physician identify lesions using millions of different pre-programmed algorithms. Further, it uses pattern recognition to bring out lesions the computer deems suspicious.

It still is up to the physician to determine whether what is identified is actually something of concern.

"By using this technology, it was supposed to decrease the miss rate or increase the ADR," Lee noted. "In some reports, the ADR increased by 14%. By increasing the ADR 1%, it has been estimated that this translates to a decrease in colorectal cancer by 3%."

MEETING THE CHALLENGE

Hoag brought the technology into the GI lab. It is available for any gastroenterologist to use with their colonoscopies, regardless of the indication.

Outpatient colonoscopies in the hospital are usually reserved for patients who have a higher periprocedural risk or have more comorbidities that would be allowable in an outpatient surgery center. In the context of the hospital, staff cares for both the inpatient and outpatient.

"Obviously, patients are admitted for specific reasons and colonoscopies for these instances are not the screening procedures for which the technology was originally intended," Lee explained. "However, I have found in these instances, GI Genius has been useful to identify pre-cancerous polyps and aid the physician to accomplish goals outside of the screening colonoscopy indication."

RESULTS

Hoag only recently has implemented the AI technology and does not yet have data to report. However, anecdotally, Lee said the technology has been well received and used by many of the physicians who come to the hospital GI lab.

ADVICE FOR OTHERS

"The role of computer-assisted diagnosis is an up-and-coming technology that will only get better," Lee stated. "The question is not if this will be helpful, but how and when it will be applied. One of the major hurdles is how to value the available technology and how that translates to quality and cost.

"As far as I know, the reimbursement for increasing the quality of a colonoscopy does not increase the reimbursement for such a procedure," he concluded. "This disproportionate outcome cannot be sustained and needs to be addressed by all parties involved."

Twitter:@SiwickiHealthITEmail the writer:bsiwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Computer-assisted AI diagnosis aims to reduce colorectal cancer at Hoag - Healthcare IT News