Category Archives: Ai

Once viewed as less desirable than real data, synthetic data is now seen by some as a panacea. Real data is messy and riddled with bias. New data privacy regulations make it hard to collect. By contrast, synthetic data is pristine and can be used to build more diverse data sets. You can produce perfectly labeled faces, say, of different ages, shapes, and ethnicities to build a face-detection system that works across populations.

But synthetic data has its limitations. If it fails to reflect reality, it could end up producing even worse AI than messy, biased real-world dataor it could simply inherit the same problems. What I dont want to do is give the thumbs up to this paradigm and say, Oh, this will solve so many problems, says Cathy ONeil, a data scientist and founder of the algorithmic auditing firm ORCAA. Because it will also ignore a lot of things.

Deep learning has always been about data. But in the last few years, the AI community has learned that good data is more important than big data. Even small amounts of the right, cleanly labeled data can do more to improve an AI systems performance than 10 times the amount of uncurated data, or even a more advanced algorithm.

That changes the way companies should approach developing their AI models, says Datagens CEO and cofounder, Ofir Chakon. Today, they start by acquiring as much data as possible and then tweak and tune their algorithms for better performance. Instead, they should be doing the opposite: use the same algorithm while improving on the composition of their data.

DATAGEN

But collecting real-world data to perform this kind of iterative experimentation is too costly and time intensive. This is where Datagen comes in. With a synthetic data generator, teams can create and test dozens of new data sets a day to identify which one maximizes a models performance.

To ensure the realism of its data, Datagen gives its vendors detailed instructions on how many individuals to scan in each age bracket, BMI range, and ethnicity, as well as a set list of actions for them to perform, like walking around a room or drinking a soda. The vendors send back both high-fidelity static images and motion-capture data of those actions. Datagens algorithms then expand this data into hundreds of thousands of combinations. The synthesized data is sometimes then checked again. Fake faces are plotted against real faces, for example, to see if they seem realistic.

Datagen is now generating facial expressions to monitor driver alertness in smart cars, body motions to track customers in cashier-free stores, and irises and hand motions to improve the eye- and hand-tracking capabilities of VR headsets. The company says its data has already been used to develop computer-vision systems serving tens of millions of users.

Its not just synthetic humans that are being mass-manufactured. Click-Ins is a startup that uses synthetic AI to perform automated vehicle inspections. Using design software, it re-creates all car makes and models that its AI needs to recognize and then renders them with different colors, damages, and deformations under different lighting conditions, against different backgrounds. This lets the company update its AI when automakers put out new models, and helps it avoid data privacy violations in countries where license plates are considered private information and thus cannot be present in photos used to train AI.

CLICK-INS

Mostly.ai works with financial, telecommunications, and insurance companies to provide spreadsheets of fake client data that let companies share their customer database with outside vendors in a legally compliant way. Anonymization can reduce a data sets richness yet still fail to adequately protect peoples privacy. But synthetic data can be used to generate detailed fake data sets that share the same statistical properties as a companys real data. It can also be used to simulate data that the company doesnt yet have, including a more diverse client population or scenarios like fraudulent activity.

Proponents of synthetic data say that it can help evaluate AI as well. In a recent paper published at an AI conference, Suchi Saria, an associate professor of machine learning and health care at Johns Hopkins University, and her coauthors demonstrated how data-generation techniques could be used to extrapolate different patient populations from a single set of data. This could be useful if, for example, a company only had data from New York Citys more youthful population but wanted to understand how its AI performs on an aging population with higher prevalence of diabetes. Shes now starting her own company, Bayesian Health, which will use this technique to help test medical AI systems.

But is synthetic data overhyped?

When it comes to privacy, just because the data is synthetic and does not directly correspond to real user data does not mean that it does not encode sensitive information about real people, says Aaron Roth, a professor of computer and information science at the University of Pennsylvania. Some data generation techniques have been shown to closely reproduce images or text found in the training data, for example, while others are vulnerable to attacks that make them fully regurgitate that data.

This might be fine for a firm like Datagen, whose synthetic data isnt meant to conceal the identity of the individuals who consented to be scanned. But it would be bad news for companies that offer their solution as a way to protect sensitive financial or patient information.

The rest is here:

These creepy fake humans herald a new age in AI - MIT Technology Review

Portland, OR, June 10, 2021 (GLOBE NEWSWIRE) -- Leading Artificial Intelligence firm Synaptiq released its 2021 AI in Construction Industry Report, providing key insights and actionable recommendations for construction firms seeking to pursue AI-enabled innovation.

In the report, Synaptiq outlines industry leaders top objectives for business operations, productivity, and cost management, as well as emerging artificial intelligence opportunities across the project lifecycle. The report also provides insider insights, including the application of Intelligent Document Processing and a real-world Machine Vision solution currently being piloted by a large multinational construction company.

Synaptiq found that technological advancements are moving rapidly in the construction industry, touching all areas of the ecosystem. New construction technology companies are shaking up the industry, from the digital transformation of the design process to preconstruction estimation software, scheduling, predictive analytics, and asset management. With these new technologies and industry-wide digital transformation comes data and lots of it.

The deluge of data creates opportunities for AI to catalyze automation throughout the project lifecycle, from design management to preconstruction, prefabrication, construction, resource and equipment management, operations, scheduling and staffing, health and safety compliance, and project retrospectives.

Intelligent Document Processing (IDP) is a specific type of process automation where technologies such as Machine Vision, Natural Language Processing, and Optical Character Recognition (OCR) work together to improve output over time. IDP eliminates repetitive tasks that would otherwise be performed manually. It is particularly useful for construction companies managing a large volume of documents such as invoices, change orders, blueprints, contracts, and client correspondence, which cannot be fully processed by existing software. One immediate use-case for IDP in construction companies will be to respond to the dramatic fluctuations in the cost of wood, and handling the massive amounts of documents that result.

In Synaptiqs report, construction leaders also highlighted that getting the fundamentals right such as establishing data mastery and cultural readiness within their organizations was imperative. For some firms, it is an uphill battle to get their teams to embrace technology innovations, and many leaders are just beginning to wrap their heads around AIs potential themselves.

Regarding data, Synaptiq learned that it was not a problem of a lack of data but rather, how to find it, aggregate it, have a strategy around it, and what to do with it, according to Synaptiq CEO Stephen Sklarew.

For example, many construction companies already have cameras recording a treasure trove of data on sites, but they may not know what to do with their data, says Sklarew. By applying analytics to this stream of data, they can address productivity, compliance, and safety issues as they happen and not just retrospectively for audits. Solutions that help keep track of teams and prevent injuries can reduce a lot of stress, reduce risk, and keep projects on schedule and under budget.

Synaptiq recently developed a machine vision solution for construction companies to monitor site productivity and audit site safety where users are able to designate regions of interest in incoming video feeds, train the solution to detect any visual objects (such as people and equipment), and review reports. Detailed logs for each detected object are available as outputs to be integrated with existing back-end systems for analysis, reporting, real-time monitoring, and alerts related to project management, scheduling, health and safety, security, and other purposes.

For more information about this work or Synaptiqs other projects in construction and related industries, visit http://www.synaptiq.ai.

About Synaptiq

Founded in 2015, Synaptiq is a data science and AI consultancy with over 50 clients in more than 20 sectors worldwide. Our firm develops and deploys actionable solutions using machine learning, machine vision, natural language processing, and other data-driven techniques. We help clients discover, organize, and leverage the data they have to streamline processes, increase productivity, and drive further innovation.

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AI in Construction: Not a Heavy Lift and Needed More Than Ever - GlobeNewswire

The Defense Logistics Agency, has its origins way back in WWII. But its plunge into the world of artificial intelligence only surfaced in 2018, when the combat logistics support agency, which manages Americas massive global supply chain, hosted an enterprise-wide AI information-gathering session.

We brought about 70 people together to give them an introduction into AI, and then brainstormed ideas across the enterprise, across every business area, every different functional area to kind of get a scope on the type of projects that we may need to pursue, said Teresa Smith, DLAs Chief Data Officer, on Federal Monthly Insights Repurposing Manpower through Automation.

In partnership with DLAs Research & Development office, Smith said several projects took shape after a little brainstorming netted around 48 ideas.

David Koch, DLAs R&D chief, points to the opportunity for AI and machine learning to help improve the agencys demand-planning process as one of two big areas they intend to tackle.

When theres high demand for an item, its really easy to predict how much youre going to need and ensure you always have it on the shelf waiting for the warfighter to request it, said Koch on Federal Drive with Tom Temin. But really, when you get to low demand or infrequent-demand items, it becomes a lot harder to predict and we think that theres opportunity for artificial intelligence to help us with that journey.

DLA will be teaming up with the Joint Artificial Intelligence Center on demand planning. The other big area DLA started pursuing about a month ago is how AI might help reduce supply-chain risk.

The terms AI and machine learning are sometimes used interchangeably, but Smith points out that the first, AI, is predictive, while the other is there to constantly learn the mistakes and refine the logic going forward.

Artificial intelligence, the way we look at it is were using the machines and a host of data, and I cant emphasize enough as the chief data officer, that you cant do AI effectively without good accessible data, said Smith. But the way we look at it is AI is performing those tasks that require that human intelligence, using massive amounts of data, recognizing patterns, learning from that experience and drawing those conclusions to make predictions or to take action.

George Duchak, DLAs chief information officer, arrived at the agency in September 2019. Koch praised Duchak for the path on which he put DLA.

He had three priorities when he came in, said Koch. One of them was to improve the user experience. The second one was to create a DLA platform, so you think of kind of that single sign-on to do everything you need to do, instead of signing in multiple times for different applications. But the third one is really applicable to what were talking about today and that is creating a data architecture for AI, because as Teresa so rightly said, data is the key to being able to be successful with AI.

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DLA on the right AI path - Federal News Network

Machine learning models have already mastered Chess, Go, Atari gamesand more, but in order for it to ascend to the next level, researchers at Facebook intend for AI to take on a different kind of game: the notoriously difficult and infinitely complex NetHack.

We wanted to construct what we think is the most accessible grand challenge with this game. It wont solve AI, but it will unlock pathways towards better AI, said Facebook AI Researchs Edward Grefenstette. Games are a good domain to find our assumptions about what makes machines intelligent and break them.

You may not be familiar with NetHack, but its one of the most influential games of all time. Youre an adventurer in a fantasy world, delving through the increasingly dangerous depths of a dungeon thats different every time. You must battle monsters, navigate traps and other hazards, and meanwhile stay on good terms with your god. Its the first roguelike (after Rogue, its immediate and much simpler predecessor) and arguably still the best almost certainly the hardest.

(Its free, by the way, and you can download and play it on nearly any platform.)

Its simple ASCII graphics, using a g for a goblin, an @ for the player, lines and dots for the levels architecture, and so on, belie its incredible complexity. Because Nethack, which made its debut in 1987, has been under active development ever since, with its shifting team of developers expanding its roster of objects and creatures, rules, and the countless, countless interactions between them all.

And this is part of what makes NetHack such a difficult and interesting challenge for AI: Its so open-ended. Not only is the world different every time, but every object and creature can interact in new ways, most of them hand-coded over decades to cover every possible player choice.

NetHack with a tile-based graphics update all the information is still available via text.

Atari, Dota 2, StarCraft 2 the solutions weve had to make progress there are very interesting. NetHack just presents different challenges. You have to rely on human knowledge to play the game as a human, said Grefenstette.

In these other games, theres a more or less obvious strategy to winning. Of course its more complex in a game like Dota 2 than in an Atari 800 game, but the idea is the same there are pieces the player controls, a game board of environment, and win conditions to pursue. Thats kind of the case in NetHack, but its weirder than that. For one thing, the game is different every time, and not just in the details.

New dungeon, new world, new monsters and items, you dont have a save point. If you make a mistake and die you dont get a second shot. Its a bit like real life, said Grefenstette. You have to learn from mistakes and come to new situations armed with that knowledge.

Drinking a corrosive potion is a bad idea, of course, but what about throwing it at a monster? Coating your weapon with it? Pouring it on the lock of a treasure chest? Diluting it with water? We have intuitive ideas about these actions, but a game-playing AI doesnt think the way we do.

The depth and complexity of the systems in NetHack are difficult to explain, but that diversity and difficulty make the game a perfect candidate for a competition, according to Grefenstette. You have to rely on human knowledge to play the game, he said.

People have been designing bots to play NetHack for many years that rely not on neural networks but decision trees as complex as the game itself. The team at Facebook Research hopes to engender a new approach by building a training environment that people can test machine learning-based game-playing algorithms on.

NetHack screens with labels showing what the AI is aware of.

The NetHack Learning Environment was actually put together last year, but the NetHack Challenge is only just now getting started. The NLE is basically a version of the game embedded in a dedicated computing environment that lets an AI interact with it through text commands (directions, actions like attack or quaff)

Its a tempting target for ambitious AI designers. While games like StarCraft 2 may enjoy a higher profile in some ways, NetHack is legendary and the idea of building a model on completely different lines from those used to dominate other games is an interesting challenge.

Its also, as Grefenstette explained, a more accessible one than many in the past. If you wanted to build an AI for StarCraft 2, you needed a lot of computing power available to run visual recognition engines on the imagery from the game. But in this case the entire game is transmitted via text, making it extremely efficient to work with. It can be played thousands of times faster than any human could with even the most basic computing setup. That leaves the challenge wide open to individuals and groups who dont have access to the kind of high-power setups necessary to power other machine learning methods.

We wanted to create a research environment that had a lot of challenges for the AI community, but not restrict it to only large academic labs, he said.

For the next few months, NLE will be available for people to test on, and competitors can basically build their bot or AI by whatever means they choose. But when the competition itself starts in earnest on October 15, theyll be limited to interacting with the game in its controlled environment through standard commands no special access, no inspecting RAM, etc.

The goal of the competition will be to complete the game, and the Facebook team will track how many times the agent ascends, as its called in NetHack, in a set amount of time. But were assuming this is going to be zero for everyone, Grefenstette admitted. After all, this is one of the hardest games ever made, and even humans who have played it for years have trouble winning even once in a lifetime, let alone several times in a row. There will be other scoring metrics to judge winners in a number of categories.

The hope is that this challenge provides the seed of a new approach to AI, one that more fundamentally resembles actual human thinking. Shortcuts, trial and error, score-hacking, and zerging wont work here the agent needs to learn systems of logic and apply them flexibly and intelligently, or die horribly at the hands of an enraged centaur or owlbear.

You can check out the rules and other specifics of the NetHack Challenge here. Results will be announced at the NeurIPS conference later this year.

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Decades-old ASCII adventure NetHack may hint at the future of AI - TechCrunch

Protecting the Human: Ethics in AI

When we think about the future of our worldand what exactly that looks like,itseasy tofocus on the shiny objects andtechnologythat make our lives easier: flying cars,3D printers,digital currenciesand automated everything.In the opening scene of theanimated film WALL-E which takes place in the year 2805a song fromHello, Dolly! happily plays in the background, starkly contrastingthe glimpse we get of our future planet Earth:an abandoned wastelandwithheaping piles of trash around every corner.Humans had all evacuated Earth by this point and were living inaspaceship,where futuristic technology and automation left them overweight,lazyand completely oblivious to their surroundings.Machines do everything for them, from the hoverchairs that carry them around, to therobots that prepare their food.Glued to their screens all day, which have taken control of their lives and decisions,humansexhibitlazybehaviors like video chatting the person physically next to them.

While yes, this is an animated,fictitiousfilm, many speculate that this could be somewhat of an accurate depiction of our future, and I tend to agree. Advancements in AI and technology are meant to make our lives easier,yetthey posea threat to society whenthey arenot perfect.Today, businesses andindividuals face many challenges with AI:from techand social mediagiants controlling speech ontheir platformstoservices and technologiesthat speed up processes but apply unintentional bias.When we start relying on algorithms to make decisions for us, thats whenthings begin to take a turn for the worse, and we get one inch closer to living ina place not too far off from the environment we see in WALL-E.AIcantjustbe good enough for usto create a better world for ourselves itmustbe perfect.Hereswhy:

An overreliance on AI amplifiesthe biases that weshould be eliminating.

As each year passes, the global use of AI continues to grow. While advancements in AIshould be making our lives easier,theyrealsohighlightingsome of our implicit biases thatmany are working hard to eliminate.Astudy from MITfound that gender classification systems sold byseveral major tech companies had an error rate as much as 34.4 percentage points higher for darker-skinned females than lighter-skinned males.Likely due to skewed data sets,examples like this presentamyriadofproblems in decision making, especiallyin employmentrecruitingand criminal justice systems.Algorithms that exclude female candidates fortraditionally male-dominated jobs,oralgorithmsthat determine a criminals risk score heavily weighted in appearance versus actions,are only amplifying the biases that weshould be removing.

A black-box approach to AI puts our first amendment rights at risk.

A black box systemin which users lacktransparencyofalgorithm developmentandmodel trainingalong withknowledge as towhy models make the decisions that they doisveryproblematic inthe ethics of AI. We as humans all have blind spots, so the creation of models and algorithms shouldinvolveelevatedhuman contextandnot just more powerful machines.If we punt all of our decisionsto an algorithm andwe no longer knowwhatsgoing on behind the scenes,the use of AIrisksbecomingirresponsibleat best and unethical at worst, even puttingour first amendment rights at risk.One studyfrom the University of Washingtonfound that leading AI models foridentifyinghate speech were one-and-a-half times more likely to flag tweets as offensive or hateful when they were written by African Americans.Biasesin hate-speech tools have the potential tounfairly censor speech on social media, banning only select groups ofpeople or individuals.By implementing a human-in-the-loop approach,humans get the final say in decision making and black-box bias can be avoided.

Theethical use of AIisdifficult to regulate.

When we start relying on AI to make decisions for us, it often does more harm than good.Last year, WIRED published an article calledArtificial Intelligence Makes Bad Medicine Even Worse, which highlights howdiagnoses powered by AI arent always accurate, and when they are,theyrenot alwaysnecessary to treat.Imaginegetting screened for cancer without having any symptoms and being told that you do in fact have cancer, but later finding out thatit was just something that looks like cancer, and the algorithm was wrong.Whileadvancements in AI shouldbechanging healthcare for the better,AI in an industry like this absolutely must beregulated in a way where the human is making the final decision or diagnosis, rather than a machine.If we remove the human from the equationand fail to regulate ethical AI, we riskmaking detrimental errorsin crucial, everyday processes.

Protecting the Human: Ethics in AI

AIneeds tobe better than good.To protect the human, ithas tobe perfect.If we begin to rely on machines to make decisions for uswhenthe technology is good enough, weamplify biases,risk our first amendmentrightsand fail to regulate some of the most crucial decisions.An overreliance on less-than-perfect AI may make our lives easier, but itwill also make us lazier andpotentially accepting ofpoor decisions. At what point do we begin to rely on the machine for everything? And if we do, will we all end up evacuatingan uninhabitable planet Earth,relying on hoverchairs to carry us around andmachines to prepare our food for the rest of our lives just like in WALL-E?As AI advances, we must protect the human at all costs.Perfect is the enemy of good, but for AI, it needs to be the standard.

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Protecting The Human: Ethics In AI - Forbes

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Artificial Intelligence (AI) is changing how businesses used to work which is due to the high adoption of the technology in retail, financial, and technology industries. Business is advocating their efficiency and production rate increasing with artificial intelligence. Executives of organizations see artificial intelligence investment as a product with a high return on investment to the organization. Artificial intelligence is proving itself to be the backbone supporting the economic investments of the country.

In cybersecurity, Artificial Intelligence is the forefront fighter in the battle of saving organizations against hackers. According to MarketsAndMarkets report, artificial intelligence is projected to be a 38.2 billion USD industry by 2026. The prominent market value projection of artificial intelligence in cybersecurity paints a great picture. Yet artificial intelligence is like a double edge sword where hackers trying to gain access to your intelligent AI devices could cause more damage to your organization than the benefits you would have thought of getting from AI. Let us find out in this blog whether Artificial intelligence is threat proof from hackers or not.

Artificial intelligence is the branch of study which focuses on the question if a machine can think? Artificial intelligence tries to develop the capability of a machine to perform and think like a human. Machine are trained to make decisions like a human. This is done to shift the workload from human to machine, thus making peoples lives easier. The low prices of chipset and increase in computing speed of computer have made it possible for a man to have to talk back machine chatbox to self-driving car etc. in the world for an affordable price of the general public. According to finacesonline.com report, around 54% of organizations and leaders think AI tools play a significant role in boosting productivity in the organization. Artificial intelligence is changing industries into the smart industry.

Today every industry is adopting artificial intelligence at a very high rate. Artificial Intelligence is bringing the workload of workers and helping organizations increase productivity with the machine. Some of the applications of AI in the market are:

The sophistication of Artificial intelligence makes it the best technology helping organizations further their goals and ambitions. Yet, suppose the Artificial Intelligence technology installed in an organization is hacked. In that case, this could be hazardous and detrimental to the company and incur loss rather than profit. It is effortless to hack an AI machine by altering the dataset and confusing the machine to predict the wrong object instead of the real object. This might not sound so dangerous, but it is enough to cause disruptive incidents and cause loss of property and lives.

A self-autonomous car dataset being hacked could result in the car unable to identify the green signal from red; it could cause the vehicle to move into the oncoming traffic and cause accidents. The autonomous missiles misidentifying the friendly target for the real target could be disastrous. The security camera fed with the wrong database could mislead face detection to allow access to unauthorized persons in critical places like airports, border checks, etc. The severity of artificial intelligence being hacked create a dilemma in the use of the system. Companies need to invest in robust Artificial intelligence security at a similar pace as the speed of adoption of AI technology. A safe and secure environment for artificial intelligence is actively looked into by governments and organizations.

Its not only cybersecurity specialists using Artificial Intelligence to defend against ethical hacking, but hackers are also using artificial intelligence to conduct dangerous attacks. The robustness in artificial intelligence security is not of primary focus for organizations, as is the adoption of AI. This is a mistake, and there needs to be more focus on the secure and safe adoption of Artificial Intelligence to safeguard ourselves against hackers. Organizations and governments need to focus more on the security aspect of artificial intelligence technology and have a dedicated team of ethical hackers to strengthen their autonomous system and protect it from hackers malicious intent. Ethical hackers are trained cybersecurity experts well-versed in protecting AI technology, computer system, and networks against hackers.

For security enthusiasts reading the blog, to become an Ethical Hacker, EC-Councils Certified Ethical Hacker (CEH) certification is one the best certificates identified by industry leaders and governments worldwide. It is developed by security experts and designed to impart knowledge of becoming an Ethical hacker.

Become a Certified Ethical Hacker Today!

Do hackers use artificial intelligence?

Read more: Artificial Intelligence as Security Solution and Weaponization by Hackers

Will Artificial Intelligence stop hackers?

Read more: The Role of Artificial Intelligence in Ethical Hacking

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Is Artificial Intelligence (AI) prone to threats by which it can be hacked? - EC-Council Blog

Dr. Zsolt Garami, medical director of the vascular ultrasound lab at Houston Methodist Hospital, puts it very straightforward: Transcranial Doppler (TCD) is one of the least understood, rarely utilizedand potentially most valuable tools available for monitoring brain circulation.

THE PROBLEM

TCD was introduced by Aaslid in 1982. Thanks to the addition of power M-mode Doppler (PMD) in 2000, TCD can detect not only the presence of blood flow, but also its depth, direction and resistance (DDR), he explained.

"TCD provides great sensitivity in detecting foreign particles, known as emboli, as small as 40 microns in diameter present in the blood cell stream," he said. "As a noninvasive, safe and cost-effective method for evaluating cerebrovascular circulation, TCD is the 'stethoscope for the brain.'"

Emboli detected on TCD are referred to as HITS (high-intensity transient signals) and the TCD machine provides both a visual and auditory signal of their presence. Emboli traveling up the carotid system pass from deep in the brain, generating unique sound and images.

"Even with all its benefits, TCD is underutilized as it requires a trained sonographer to perform an exam," Garami noted. "In addition, interpretation of the captured signal is not taught in school, leaving only experienced physicians able to understand the value of the data provided by the detailed waveform images."

PROPOSAL

NovaSignal is a medical technology and data company that offers NovaGuide, a cerebrovascular monitoring system.

The company worked to attack TCD's primary shortcomings. The fully automated, robotic NovaGuide minimizes the need for a trained sonographer, and its AI algorithms assist with the reading of the captured waveforms.

Dr. Zsolt Garami, Houston Methodist Hospital

"The robotic probe pods automatically identify the acoustic window in the temple that allows for the pulsed Doppler ultrasound to view blood flow in the brain," Garami explained. "This acoustic window varies from individual to individual, leading to variability in manual exams, even when performed by well-trained sonographers.

"NovaGuide eases that burden by automatically identifying the signal," he continued. "Furthermore, once the signal is acquired, NovaSignal has introduced novel AI algorithms to further aid in the interpretation of the cerebral blood flow velocity waveforms."

MEETING THE CHALLENGE

NovaGuide opens the opportunity for "green" medical staff, nurses without any TCD training or experience, to learn and use the robotic TCD system in just a few hours, Garami reported.

"With four billable clinical codes assigned to TCD, NovaGuide is economically suitable for big academic hospitals as well as small practices," he noted. "Specifically, for those centers with TCD experience, NovaGuide provides an automated solution to ease the clinical burden of longer exams. Additionally, if the site does not have the expertise required, NovaGuide provides access with just a few hours of training.

"TCD's sensitivity in detecting emboli presents an advantage for PFO tests where agitated air is deliberately injected into a systemic vein and shown passing into the cerebral circulation via the hole in the heart," he added.

"Without the hole, the air would be filtered out by the lungs. We are currently conducting a research trial with NovaGuide to prove that this indirect diagnostic mode is the most sensitive test for PFO and not as uncomfortable as swallowing a tube in sedation for the cardiac ultrasound."

At Houston Methodist Hospital, NovaGuide exports clinical reports to the PACS system for easy viewing and interpretation of the final reports.

RESULTS

There have been several clinical scenarios at the hospital where NovaGuide has provided concrete clinical evidence to help support the management of patients. A few specific examples revolve around the use within the operating theater for cardiac procedures.

"It has been well established that embolization occurs during a variety of cardiac procedures and the use of TCD can inform on how to change clinical practice to reduce these perioperative emboli and reduce stroke risk," Garami explained.

"Multiple protection filters were developed to clean the blood flow from these materials. TCD helps to test these and, even early in development, to decide which could be more effective to use.

"I believe this technology has the ability to improve outcomes of those procedures, as it is the only tool able to provide real-time information about embolization during manipulation of the procedure," he continued.

"In addition to emboli monitoring, we are currently using the system to assess and compare pre- and post-procedure cerebral hemodynamics to ensure the operation has accomplished the necessary clinical impact by restoring improved cerebral blood flow."

This can be done in real time at the bedside, before the patient is removed from the operating room.

"In addition to the operating room, the use of the NovaGuide has applications in the recovery room, intensive care unit and on the floor, as it provides bedside hemodynamic monitoring, a distinct advantage when compared to 'static' radiological images like CT, MRI, DSA, etc.," Garami said.

"Within these environments, TCD has many well-accepted clinical uses: vasospasm detection after intracranial bleeding, detecting large vessel occlusion in stroke/TIAs, detection of intracranial stenoses, PFO bubble test ... The list can be endless to utilize this technology."

ADVICE FOR OTHERS

TCD provides unique access to monitoring of cerebral hemodynamics in the large arteries of the brain and the ability to monitor emboli events. The technology is a noninvasive, safe and cost-effective method for evaluating cerebrovascular circulation, and complements existing "static" imaging, Garami advised.

"Unfortunately, the technology has been limited due to the difficulty in signal acquisition leading to some negative opinions by nonusers," he noted:"'I do not want to know about this emboli;it has no clinical manifestations,' or, 'The results are user-dependent and cannot be trusted.'

"The obvious retort is, 'Explain to me what kind of emboli do good when going up into the brain? Do you know what is going on in your brain?'

"We have long waited for changes to come to TCD, and we are excited about the opportunity to see the technology continue to expand," he concluded.

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

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Houston Methodist uses AI brain ultrasound to reduce open heart surgery complications - Healthcare IT News

The startup investing market is crowded, expensive and rapid-fire today as venture capitalists work to preempt one another, hoping to deploy funds into hot companies before their competitors. The AI startup market may be even hotter than the average technology niche.

This should not surprise.

In the wake of the Microsoft-Nuance deal, The Exchange reported that it would be reasonable to anticipate an even more active and competitive market for AI-powered startups. Our thesis was that after Redmond dropped nearly $20 billion for the AI company, investors would have a fresh incentive to invest in upstarts with an AI focus or strong AI component; exits, especially large transactions, have a way of spurring investor interest in related companies.

That expectation is coming true. Investors The Exchange reached out to in recent days reported a fierce market for AI startups.

The Exchange explores startups, markets and money.

Read it every morning on Extra Crunch or get The Exchange newsletter every Saturday.

But dont presume that investors are simply falling over one another to fund companies betting on a future that may or may not arrive. Per a Signal AI survey of 1,000 C-level executives, nearly 92% thought that companies should lean on AI to improve their decision-making processes. And 79% of respondents said that companies are already doing so.

The gap between the two numbers implies that there is space in the market for more corporations to learn to lean on AI-powered software solutions, while the first metric belies a huge total addressable market for startups constructing software built on a foundation of artificial intelligence.

Now deep in the second quarter, were diving back into the AI startup market this morning, leaning on notes from Blumberg Capitals David Blumberg, Glasswing Ventures Rudina Seseri, Atomicos Ben Blumeand Jocelyn Goldfein of Zetta Venture Partners. Well start by looking at recent venture capital data regarding AI startups and dig into what VCs are seeing in both the U.S. and European markets before chatting about applied AI versus core AI and in which context VCs might still care about the latter.

The exit market for AI startups is more than just the big Microsoft-Nuance deal. CB Insights reports that four of the largest five American tech companies have bought a dozen or more AI-focused startups to date, with Apple leading the pack with 29 such transactions.

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AI startup investment is on pace for a record year - TechCrunch

MOUNTAIN VIEW, Calif.--(BUSINESS WIRE)--The AI Hardware Summit, September 13-16, 2021 returns to the Computer History Museum, Mountain View, for its fourth iteration, having sold out the venue each previous year. As the first hybrid event in its industry, attendees can benefit from tickets to attend in-person; or virtual tickets to tune in from across the globe.

The 2021 AI Hardware Summit has evolved. As machine learning models continue to grow in size and complexity and enter production in enterprises and institutions worldwide, the approach to accelerating these workloads is shifting. With support from Headline Partner, Synopsys, and Platinum Partners; Cadence, Graphcore, Habana, Intel and SambaNova Systems, this years summit has shifted from focusing solely on hardware to taking a holistic view on AI acceleration at the systems level.

Presentations feature multidisciplinary perspectives on building and deploying fast, efficient, and affordable AI systems, from 50+ speakers drawn from the AI hardware user and technology vendor ecosystems. Topics include:

The AI Hardware Summit hosts more than 600 C-suite and engineering specialists across the machine learning technology stack.

Previous event highlights include AI hardware start up, Habana Labs, (acq. by Intel, 2019) emerging from stealth at the event in 2018, and Chairman of Alphabet Inc. & former President of Stanford University, John Hennessy, giving a luminary keynote in 2019.

This year keynote speakers include Aart de Geus, Chairman & Co-CEO, Synopsys and Lip-Bu Tan, CEO, Cadence.

The AI Hardware Summit is a great place where lots of people interested in AI Hardware come together and exchange ideas, and together we make the technology better. Theres a synergistic effect at these summits which is really amazing and powers the entire industry. John Hennessy, Chairman, Alphabet Inc.

Expect limitations on the availability of live tickets, dependent on Covid-19 US government guidance and regulations. Registration to attend AI Hardware Summit starts from only $999 for an in-person ticket, and $499 for virtual tickets.

To find out more, visit http://www.aihardwaresummit.com

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AI Hardware Summit: Must-Attend AI Event Releases Agenda Ahead of its Return to Face-to-Face in Silicon Valley - Business Wire

The innovation is a collaboration between AP-HP Greater Paris University Hospitals and Owkin, a start-up pioneering federated learning and Artificial Intelligence (AI) technologies for medical research and clinical development. The tool, which is a trained and validated AI model, can be used in clinical practice worldwide and opens the possibility of patient molecular stratification in routine care and for clinical trials.

The results of the companies ongoing strategic collaboration was presented at ASCO 2021. The abstract and poster, entitled Identification of pancreatic adenocarcinoma molecular subtypes on histology slides using deep learning models, demonstrates the first AI-based tool for predicting genomic subtypes of pancreatic cancer (PDAC) developed from machine learning applied to histology slides.

Gilles Wainrib, Chief Scientific Officer and Co-Founder of Owkin, said: Our research shows AI can help connect information at the genomic, cellular and tissue levels, and how doing so can bring immediate value to make precision medicine a reality for patients. This study further underscores the value of using machine learning for identifying histo-genomic signals for cancer research and clinical development.

Pancreatic adenocarcinoma is a complex and heterogeneous disease. Heterogeneity and tumour plasticity are likely major factors in the failure of many clinical trials. Multiomics studies have revealed two main tumour transcriptomic subtypes, Basal-like and Classical, that have been proposed to be predictive of patient response to first-line chemotherapy. The determination of these subtypes has been possible so far by RNA sequencing, a costly and complex technique that is not yet feasible in a clinical routine setting. Taken together, these factors make it compelling to use advanced AI methods with common histological slides, trained alongside crucial context from expert researchers, to address the unmet needs of patients.

Professor Jrme Cros, Pathologist at Beaujon Hospital, Universit de Paris, said: This tool was developed using the unique histological and molecular resources from four APHP hospitals through a unique collaboration between pathologists from APHP, bioinformaticians from the group Carte dIdentit des Tumeurs de la Ligue Contre le Cancer, and data scientists from Owkin. It can remotely subtype tumour in minutes, paving the way for many applications from basic science to clinical practice.

This research is part of a successful and ongoing collaboration between Owkins multidisciplinary teams and those of the AP-HP Greater Paris University Hospitals. Since 2019, the two have collaborated in the service of shared objectives: to improve patient care and facilitate the development of new drugs in three main areas (oncology, immunology, cardiology), and to democratise access to AI for researchers to promote innovation and medical advances.

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AI-based tool could improve pancreatic cancer research and treatment - Health Europa