Category Archives: Ai

Out at sea, those working in the maritime industry are faced with increasingly congested waterways, dangerous weather and challenging visibility conditions, which present navigation challenges.

According to the European Maritime Safety Agency, there were 3,173 maritime casualties and incidents reported in 2019 alone, with the last five years averaging around 3,239. One way to mitigate these incidents is via the use of improved navigation technology.

Orca AI, a company whose goal is to provide the industry with intelligent navigation solutions to prevent collisions and save lives, offers technology to reduce human-caused navigation errors through intelligent AI.

This technology allows the captain and crew access to real-time environment views which assists with decision making. We spoke with Dor Raviv, co-founder & CTO, Orca AI, to find out more about the solution.

Dor Raviv (DR): The main aim of Orca AIs technology is to enhance the safety of the shipping industry through a data-driven approach at fleet level. Utilising onboard navigation sensors and high-resolution cameras with proprietary AI algorithms, the technology is able to provide valuable insight such as alerting the crew on dangerous targets, prioritize risk in real time and sort out complex navigation situations.

Additionally, the technology collects data, stored in the cloud; a vital tool for data analysis. Valuable and actionable takeaways can then be extracted and analysed to generate safety insights. Fleet managers can learn about how ships behave in terms of their safety parameters, understand specific areas in the world known to be more prone to risk, how many manoeuvres they perform and why, all with the view to make informative decisions moving forward.

GlobalData's TMT Themes 2021 Report tells you everything you need to know about disruptive tech themes and which companies are best placed to help you digitally transform your business.

DR: We acknowledge that AI may not be the solution to everything. However, in this instance and with our mission to enhance safety within the shipping industry, we deem it essential. This technology provides the solution to analyse masses amounts of raw data, process it and uncover unseen targets, all while removing the need for hand-crafted statistical methods which requires time and often brings with it inaccuracy.

With Orca AI, artificial intelligence is used to detect targets at sea, estimating navigation parameters and analysing huge amounts of data at fleet level to generate insights.

The solution also provides an interesting twist. In the shipping industry, one of the biggest hurdles faced is the lack of experienced seafarers. Orca AI aims to provide tools for education within the industry. Its technology can allow for teams to demand a record of footage, accompanied by sensors and analysis, to investigate and reflect on how they have been performing to improve future work. In this respect, AI provides a realistic approach to empower and better the industry.

DR: The shipping industry is traditional. Traditional methods of ship auditing, interviewing the crew and occasionally monitoring the ships, is neither a scalable nor cost-efficient system. It brings with it expense and the impossible task of monitoring whether the crew are adhering to safety standards when they are not being monitored. Therefore, it completely misses the aim of providing the consistent monitoring that bodies such as insurance companies may require.

This is where the attractive alternative of digitalisation comes in. Connectivity and use of data are becoming increasingly important in understanding the industry. Ships are more connected than before, and all major shipping companies are looking for digital solutions.

It can reduce emissions, optimise voyages, provide real-time analytics, and give a level of transparency that creates a consensus on events which in turn can be used to better handle claims. The data is fuel for interesting use cases in the industry such as asset management, fuel, and safety optimisation and therefore is becoming a much more viable option adopted throughout.

DR: A key benefit of Orca AIs technology is its ability to understand the maritime environment as well as any captain and to process raw data to generate actionable insights. It enables crews to reflect on specific events and understand why they happened.

The main use cases and benefits are the guarantee that it will monitor and prevent risk, it will reduce workload, provide the ability to safeguard teams, supply the tools to record events for insurance purposes and will give unprecedented transparency between ship and shore and effective training.

DR: AI brings with it a whole host of possibilities and really has no restrictions. Since Orca AIs technology is designed specifically for the maritime domain, this technology fits into any offshore operations. There are many additional use cases for this technology, such as pirate detection and offshore constructions monitoring.

DR: We see a future whereby ships are intelligent and able to make decisions autonomously based on data, getting from A-B on their own, all while communicating with others. These intelligent ships will also make decisions based on the type of operation they are trying to achieve. For example, being efficient does not always necessarily mean fast.

DR: Orca AIs key aim is to enhance safety whether that be through reducing claims and collisions or reducing environmental damage and emissions. The use of AI technology is an effective platform to achieve this.

Maritime Software Solutions

28 Aug 2020

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Navigating the oceans with help from AI: Q&A with Orca AI - Ship Technology

By Jaro Uljanovs, Lead AI Developer & Data Scientist at Sharper Shape

If you are in charge of a powerline transmission network, the chances are that errant vegetation is one of the major banes of your life.

Utilities are forced to spend huge sums annually keeping lines clear of encroaching vegetation that could damage or bring down powerlines in bad weather, risking outages, expense and even fire. Like painting the Forth Bridge, it is an intensely manual task that is already overdue by the time its finished.

However, with the application of cutting-edge artificial intelligence (AI) techniques, combined with state-of-the-art data collection, it is now possible to aggressively streamline the process, saving time and money while lowering risk.

Digitising vegetation management

Vegetation management feels very analogue. It brings to mind images of trees and forests, a far cry from the artificial imagery we associate with technology. And indeed, the traditional approach to it is very analogue. Workers walking the line, looking for vegetation that looks like its getting close to powerlines, then making a decision on what needs to be done depending on how fast-growing the species in question is.It may be all fresh air and forest, but in reality this is a data problem. First, the worker must identify instances where, say, a tree branch is getting too close to a powerline. Maybe too close is defined in a fuzzy, human way, but thats a datapoint. Second, that worker must go through their mental database of candidate tree species and combine that initial location data with what they know about that tree. Is it fast-growing? Is it prone to breakage in high winds? Is it deciduous or evergreen?

Finally, they must combine and weigh that data to come to a decision: cut it back or leave it be.

Broken down that way, does it seem so far-fetched a computer could do this?

Good results require good data

Garbage in, garbage out the old tech adage is as true as ever. Just as we wouldnt trust the judgement of a line walker who had left their glasses in a truck, if we are to digitize vegetation management, we need high-quality data as an input.

For vegetation management, we can specify three datasets necessary to do a good job.

First, we have LiDAR or light distance and ranging data. LiDAR sensors create a geospatial point cloud, essentially compiling a 3D map of objects in a surrounding area. LiDAR wont tell us much about what is there, but at any given point it will tell us whether there is an object present or not.

We can therefore tell that a tree-shaped object is x feet away from our powerline, as well as its dimensions.

Second, add RGB (red, green, blue) imaging data to add human-eye visible wavelength color and a level of detail to the point cloud, before adding the third layer to the picture: hyperspectral imagery.

Hyperspectral sensors measure parts of the electromagnetic spectrum invisible to the human eye, and combined with RGB data this gives a richer, more informative view of a given object than a human observer will get, resulting in higher fidelity data. Crucially, different species of vegetation reflect the suns electromagnetic spectrum with different amplitudes at different wavelengths of the spectrum, meaning that hyperspectral imagery combined with an accurate reference database can even identify species from the foliage. Tests have shown this to be at least as accurate as species identification by an informed human, at scales impractical for human labour applications.

So, by combining these three data sets, we have a geospatial understanding of where vegetation is in relation to powerlines, plus an understanding of what that particular piece of vegetation is and, therefore, how it might behave. All the inputs needed for our decision.

Making decisions

As we know though, the intelligent part of artificial intelligence isnt collecting data its knowing what to do with it. In other words: making the decision.

Getting to a point where a machine can make that decision is not simple, quick, or easy. However, hard work and technological advancement have now allowed us to get there.

First, we can start with some simpler AI tasks. Returning to the breakdown above, we can for example develop specific Neural Network algorithms to use the LiDAR point cloud data to recognize the shapes of certain objects, e.g. a transformer, a pole, a tree. Separately, we could develop algorithms to classify different trees and subspecies of tree using hyperspectral data to prioritize the most and least dangerous.

This is already a highly sophisticated approach to vegetation management, capable of saving hundreds of man-hours in the field. Right now, we first analyse the LiDAR pointcloud data to create a canopy height model, discarding vegetation under a certain height as non-threatening (as long as it is not directly under the powerlines). On the remaining pixels in the point cloud, we overlay the hyperspectral data for species classification to make a risk assessment.

However, recent advances in graph neural networks (GNNs) have made headway on what is possible for machine learning and AI, even with sparse pointclouds. Using a combination of open-source and proprietary technology, it will not be too long before our AI can combine the datasets into a single input automatically, and rapidly classify vegetation according to species, health and risk, before making an automated recommendation on how to proceed.

Imagine that: instant, automatic insight into vegetation danger-spots on the network. Resources could be more intelligently and economically deployed to manage vegetation, and risk of damage, outages and wildfires would be drastically lower all based on data that itself could be harvested automatically by drone in the not-too-distant-future. Its a future where AI has upended the challenge of vegetation management and its tantalizingly close.

No silver bullets

Of course, big promises have been made for all sorts of technologies, and we must be careful not to overhype AI-based vegetation management. There is significant work to do upfront in such a project to collect, label and annotate high-quality data for the machine learning algorithms to learn from.

This can be difficult for utilities with tight budgets hostile to upfront costs, however, thanks to our previous projects we have a wealth of LiDAR data, labelling key infrastructure components to work from, and dedicated experts who can sift through terabytes of data. This results in clean, error-free datasets to train algorithms on. In other words, there is a significant upfront investment required in expert human time to pre-process, label and annotate the data to then train the algorithms.

However, once that stage is completed, ongoing operational costs can be extremely low. The vegetation management program could be repeated ad infinitum so long as data is kept up to date, and planners could even use it to run what-if scenarios for events such as major storms. Whats more, unlike many transmission utility investments, there are no buried costs. If you install a new insulator, for example, it will sit there for years and upgrading it probably means replacing it and extensive labor so you want to be 100 per cent sure before going ahead with it. With software, upgrades are continual and non-disruptive, meaning an investment that improves with time, not one superseded by better options.

Based on the clear business need and the rapid improvement of technology in the space, I am confident that AI-based vegetation management will become the gold standard for advanced transmission utilities in a short span of years.

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Powerline vegetation management: Using AI to see the wood for the trees - sUAS News

Has enterprise artificial intelligence (AI) lived up to the hype generated at a decades worth of industry conferences? Or is it coming up short? Maybe putting the word enterprise in front of AI just adds up to a marketing spin. It depends on how individual businesses deploy AI.

When companies adopt AI wisely, they do more than shift repeatable tasks and processes from humans to more efficient computers. They bring humans and machines together to build more intelligent workflow transformational workflows.

The private equity firm Graham Allen has been leaning on AI to revitalize and grow midwestern industrial and mid-sized businesses with a pragmatic approach thats gaining attention.

The enterprise AI-focused operating company SymphonyAI has been earning headlines for its strategy. Its portfolio companies have been making inroads in the industry verticals they each address, including Symphony IndustrialAI. With the recent acquisition of Savigent, Symphony AyasdiAI in banking, and Symphony MediaAI in the business of subscription and media distribution revenue, including gaming.

In data ops for private capital, Harmonate has been leading a quiet revolution in how private equity and funds-of-funds middle and back offices operate with machine learning.

Humans and machines together can achieve more, in a more repeatable and reliable fashion, and with better insight. But apart from some funds and companies, is that actually happening throughout the economy?

No, and yes. Money is being poured into AI, and its making a difference. Its just that the difference being made is not necessarily visible. This lack of visibility fuels skeptics. And the progress is not fast, given that the availability of huge amounts of data is both a blessing and a curse. Copious data delivers the raw material AI needs. But AI is still learning how to cope with the complexity and needs help from human domain experts.

The smart companies are the ones that are not tinkering and failing to make big moves. And the smart companies also arent trying to leap too far ahead with moonshots that skip steps.

What the smart companies are doing is putting together point solutions into products that solve real business enterprise solutions. They are developing the right loop between domain experts and machines. The result is real AI product suites that capture the knowledge capital of enterprises and can transform industries.

We all know AI investments have been increasing in recent years. Skeptics would say the trend derives from big promises and false expectations. But Im compelled to think many companies are deploying AI more wisely than we understand. They are discovering value and growing the potential of AI.

Its just happening in quieter corners of business enterprises. Its happening in places where domain experts and the right technologists are solving small problems, then connecting those breakthroughs to others, until theres an inflection point. Theres a germination period underway right now.

We are moving from a diffuse cloud of point solutions to product suites in industry verticals powered by business leaders whove embraced the new reality of their markets.

AI skeptics, however, persist in believing that artificial intelligence advances are like flying cars a sci-fi fantasy that has failed to materialize despite years of hopes and promises. Its true that optimistic predictions have sometimes outstripped the reality of AI.

By one estimate, AI has been through seven false starts since the 1950s. Impressive multimillion-dollar AI efforts have faltered. Some ostensible AI startups arent even really using AI but rather are selling automation with elements of machine learning. This poor performance and confusion fuels skepticism, inhibits innovation, wastes money and reduces returns.

Most investor enthusiasm for AI is based on sound logic, however. AI tools have evolved from defeating humans at chess. Machines are good at recognizing patterns, a powerful and important cognitive function.

And, in fact, processing patterns are humanitysintellectual edge over other species. It also accounts for many daily business tasks that AI-driven machines can now frequently do better than humans across a range of sectors. The results are driving enhanced AI chips that reduce costs and dramatically improve performance.

But those chips are also being driven by the fact that repeatable tasks can be deceiving. When multiple choices of what to do lead to many more multiples of options. Even AI can start to lose track of where its going. Experience with humans, and more chip power can bridge that gap.

There is a lot more data to process today, too, which means more potential value. Thanks to the internet, social media, connected devices and the Internet of Things, total extant data exceeds 40 zetabytes, a ten-fold increase since 2013.

There are now 40 times more bytes than there are stars in the observable universe, according to the World Economic Forum. Cloud computing has facilitated elastic consumption of storage and network demands to handle that data. Digital transformations have resulted.

A growing number of companies are recognizing the benefits. AI adoption tripled in the 12 months leading up to March 2019, perhaps the fastest paradigm shift in technology history according to a major study. PWC forecasts that AI could add $15.7 trillion to the global economy by 2030.

AI is not a fad. It is a key differentiator. Like the internet, it has the potential to completely transform the economy. Companies that deploy it effectively will make changes.

Of course, companies can possess all the ingredients necessary to conduct top-performing AI analysis but still fail to achieve results, particularly if they lack a robust understanding of their industrys business processes. Human perspective and insight are more art than science. Inspiring the former while developing the latter is the challenge we all face in the new AI age were now in the middle of.

Companies sometimes tinker, improving obsolete systems rather than rethinking and reinventing their operations to capitalize on enterprise AI.

Tinkering is good. But tinkering too long leads to a flawed approach that may help a company reduce its costs or streamline processes in the short run. But such gains are unlikely to justify the investment needed to gain significant market share.

Worse yet, the company will have missed an opportunity to achieve a transformational advantage, one that competitors may be exploiting.

Adding to the problems with tinkering are startups seeking to harness AI for individual point solutions. Their value proposition is harder to figure out. The potential for differentiation is typically diminished, and their survivability is less certain. A task and a point solution are not a business enterprise.

Companies dont face a choice of incremental change or narrow focus, however. Instead, established and new ventures need to harness enterprise AIs capacity to capture and profit from the knowledge capital in their given sectors.

In 1998, Paul Strassmann argued that the proper function of the software is to serve as the businesss prefrontal cortex, storing and exploiting the working knowledge that has traditionally remained stuck in employees heads. When applied correctly, enterprise AI is the ideal technology for this work.

The goal of enterprise AI is not only to empower humans but also to program and institutionalize stronger, smarter, more efficient organizations.

Enterprise AI can expedite those changes because, unlike traditional software, which follows the static instructions of a programmer, AI can evolve to capture a wider variety of tasks and learns through practice.

Furthermore, enterprise AI is undaunted by the many terabytes of data that companies gather. It quickly observes complex and obscure patterns that humans miss.

Thats why forward-looking companies are using it to build next-generation platforms systems of actionable intelligence that capture siloed data from existing systems of record. The enterprise AI solution makes this data available in a holistic way, through a set of AI models, applications and solutions.

These platforms also acquire and integrate data from external sources, providing intelligence for further revenue growth.

Businesses will need a vision for AI-ification if they want to rethink their operations, transform their technology stacks, overhaul existing solutions and win in the future. And were fast approaching the point where its not a question of wanting to rethink, but needing to rethink.

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Chris is the founder of enterprise technology advisory and communications firm Gale Strategies. He's an integrated communications marketer helping growing businesses and multinationals manage critical issues and tell their story to investors, customers, and consumers.

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What Makes Enterprise AI Different From Any Other AI? - ReadWrite

BERKELEY, Calif., April 30, 2021 /PRNewswire/ --Arize AI, the leading Machine Learning (ML) Observability company, has been named to the Forbes AI 50, a list of the top private companies using artificial intelligence to transform industries.

The Forbes AI 50 list, in its third year, includes a list of private North American companies using artificial intelligence in ways that are fundamental to their operations, such as machine learning, natural language processing, and computer vision.

Today, companies spend millions of dollars developing and implementing ML models, only to see a myriad of unexpected performance degradation issues arise. Models that don't perform after the code is shipped are painful to troubleshoot and negatively impact business operations and results.

"Arize AI is squarely focused on the last mile of AI: models that are in production and making decisions that can cost businesses millions of dollars a day," said Jason Lopatecki, co-founder and CEO of Arize. "We are excited that the AI 50 panel recognizes the importance of software that can watch, troubleshoot, explain and provide guardrails on AI, as it is deployed into the real world, and views Arize AI as a leader in this category."

In partnership with Sequoia Capital and Meritech Capital, Forbes evaluated hundreds of submissions from the U.S. and Canada. A panel of expert AI judges then reviewed the finalists to hand-pick the 50 most compelling companies.

About Arize AI Arize AI was founded by leaders in the Machine Learning (ML) Infrastructure and analytics space to bring better visibility and performance management over AI. Arize AI built the first ML Observability platform to help make machine learning models work in production. As models move from research to the real world, we provide a real-time platform to monitor, explain and troubleshoot model/data issues.

Media Contact: Krystal Kirkland [emailprotected]

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Arize AI Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies of 2021 - PRNewswire

NEW YORK, May 4, 2021 /PRNewswire/ --The winners of Fast Company's 2021 World Changing Ideas Awards were announced today, honoring the businesses, policies, projects, and concepts that are actively engaged and deeply committed to pursuing innovation when it comes to solving health and climate crises, social injustice, or economic inequality.

Health at Scale, a machine intelligence company that uses proprietary advances in artificial intelligence and machine learning to match individuals to the next best action in real time, was recognizedin the AI and Data Category for its Precision Navigation platform. Founded by machine learning and clinical faculty from MIT, Harvard, Stanford, and the University of Michigan, the company's mission is to bring precision delivery to health care, using tens of thousands of health variables from over a hundred million lives to generate personalized and precise guidance for individual patients seeking care providers. Health at Scale's Precision Navigation looks at providers in a hyper-personalized, outcomes-based way, providing each individual with a personalized rating of providers in the geography where they want to receive care.

"We're honored to be included in this year's World Changing Ideas showcase," said Health at Scale CEO and Founder Zeeshan Syed. "Health care today is imprecise and impersonal, which makes care inefficient, less effective, and more costly. Our Precision Navigation technology looks to change this, using machine intelligence to accurately match patients to physicians, facilities and care settings likely to produce optimal outcomes for them individually. We're thankful for Fast Company highlighting our work so we can continue to change health care, focusing on personalization, not generalization."

Now in its fifth year, the World Changing Ideas Awards honors inspirational innovation for the good of society with Health and Wellness, AI & Data among the most popular categories. A panel of eminent Fast Company editors and reporters selected winners and finalists from a pool of more than 4,000 entries across transportation, education, food, politics, technology, and more. The 2021 awards feature entries from across the globe, from Brazil to Denmark to Vietnam.

Showcasing some of the world's most inventive entrepreneurs and companies tackling exigent global challenges, Fast Company's Summer 2021 issue (on newsstands May 10) highlights, among others, a lifesaving bassinet; the world's largest carbon sink, thanks to carbon-eating concrete; 3D-printed schools; an at-home COVID-19 testing kit; a mobile voting app; and the world's cleanest milk.

"There is no question our society and planet are facing deeply troubling times. So, it's important to recognize organizations that are using their ingenuity, impact, design, scalability, and passion to solve these problems," says Stephanie Mehta, editor-in-chief of Fast Company. "Our journalists, under the leadership of senior editor Morgan Clendaniel, have discovered some of the most groundbreaking projects that have launched since the start of 2020."

About Health at Scale: Health at Scale is a health care machine intelligence company that uses proprietary advances in artificial intelligence and machine learning to match individuals to the next best action in real-time and when needed most: whether it's the ideal choice of treatment, an early intervention, or the right provider. Founded by machine learning and clinical faculty from MIT, Harvard, Stanford, and the University of Michigan, the company's mission is to bring precision delivery to healthcare, using tens of thousands of health variables from over a hundred million lives to generate personalized and precise recommendations for individual patients. Health at Scale's machine intelligence is deployed at scale in real operational settings--including with some of the largest payers in the country, driving better health outcomes and affordability for its customers. The company's software solutions service a broad range of use cases: provider navigation and network design, early targeted prediction and prevention of adverse outcomes, optimized treatment planning; and fraud, waste and abuse prevention. For more information, please visit healthatscale.com.

About the World Changing Ideas Awards: World Changing Ideas is one of Fast Company's major annual awards programs and is focused on social good, seeking to elevate finished products and brave concepts that make the world better. A panel of judges from across sectors choose winners, finalists, and honorable mentions based on feasibility and the potential for impact. With the goals of awarding ingenuity and fostering innovation, Fast Company draws attention to ideas with great potential and helps them expand their reach to inspire more people to start working on solving the problems that affect us all.

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Health At Scale Recognized In "AI And Data" Category Of Fast Company's 2021 World Changing Ideas Awards - PRNewswire

Artificial intelligence and machine learning have gained popularity in the medical device industry in recent years, with some top players in the space developing systems or buying their way into the competition.

Medtronic, GE and Philips have all invested in AI and machine learning, with claims that the technologies can better diagnose and treat patients. The wave of AI adoption and usage has brought new challenges to the healthcare industry, leading the FDA to consider adopting new regulatory review processes specifically for AI and machine learning technologies.

Rich Whitney, CEO of Radiology Partners, a U.S-based radiology practice, said that while interest in AI has been growing recently, barriers in the healthcare system like fragmentation and high amounts of regulation have still prevented widespread adoption.

One barrier is low adoption among physicians. However,Whitney said that Radiology Partner's recent partnership with AI medical imaging company Aidoc is intended to help address the problem.

"You need to make sure that physicians are on board and are properly trained and are really champions of this technology.Otherwise, it's not going to work. It's not going to be utilized;it's not going to get you the results you'd expect,"Whitney said.

Nina Kottler, associate CMO for clinical AI with Radiology Partners, contends a primary benefit of using AI systems in radiology is improving patient care by identifying health concerns more quickly. Kottler said one of the crucial features of Aidoc's algorithms is a triage system, which can flag critical exam results for radiologists to prioritize.

"When your patient has an intracranial hemorrhage or a pulmonary embolist, these are findings that if you're not detecting them ... they can have catastrophic outcomes,"Kottler said. "The earlier you get to those things, the better it is for patient outcomes."

The algorithms are also used for oncology exams, where they help identify if diseases are getting better or worse, according to Kottler.

Watchdogs, though, worry the pendulum could swing too quickly in the direction of AI.ECRI, for example, warns the technologies may be unreliable and misrepresent some patient populations, which could lead to misdiagnoses and inappropriate care decisions.

In a conversation with MedTech Dive, Whitney, Kottler and Aidoc CEO Elad Walach discuss how interest in AI has grown, handling potential bias and regulatory changes at the FDA.

This interview has been edited for clarity and brevity.

MEDTECH DIVE: How have you seen AI technology change over the last several years as this space has received more attention?

RICH WHITNEY: The technology is moving very, very rapidly. But we haven't yet crossed into that part of the evolution here where there is a significant amount of use and actual impact. The partnership with Aidocreally creates the prospect for much more widespread use of AI and really moving us into the future that we all envision,which is radiologists being enabled by AI and being able to add significantly more value to the health system.

NINA KOTTLER: There's been a lot of improvement in the technology, and a lot more options in terms of what kind of algorithms are available. But the technology on its own is insufficient. And I think what has been missing has been that connection with the radiologists. The technology is meant to be deployed in a clinical environment, and because there hasn't been a lot of deployment, there hasnt been a lot of lessons in how to do that right.

AI systems have to be deployed with the direct assistance of radiologists to make sure that they understand how these clinical systems work. We need to make sure it's integrated into their workflow, and then we need to figure out how to monitor these systems over time to make sure that both the AI and the clinician are working together to improve patient care. And that's not simple.

Do you see providers prioritizing and investing more in AI systems today than two or three years ago?

ELAD WALACH: I can definitely say yes. By the way, COVID even though it's difficult really impacted the trend of healthcare executives being able to see value and return on investment from software-based solutions. They know that there is value to be captured by utilizing the right technical infrastructure and software. So I think that in terms of prioritization, absolutely. But I think there is a lot of momentum building up by physicians and radiologists using the technology, understanding that there is value and analyzing what that value is.

The FDA is considering how best to regulate AI. For example,whether to allow algorithms to be updated without review or remain "locked." How will a change in this review process impact the industry?

KOTTLER: Instead of just locking an algorithm in at one point in time, and then waiting for that algorithm to improve and redoing that evaluation, the FDA is looking at evaluating the vendor and their practices to see if the way that the vendor updates things themselves is good enough. And if the vendors processes are good enough, then the output should be good enough. So, the agency wont actually have to check the output, they can check the vendor processes. They're just in the very beginning of it. I think they're beta testing it with a few big groups right now, so it's going to take a little while. But I think it's quite fascinating.

WALACH: It is a difficult problem the FDA is facing, and a lot of it is the flood with the number of products that are coming to market. The question that the agency is tasked with is, How do we maintain safety and efficacy while making sure that we can bring innovation to the market? The agency has been moving quite quickly in terms of creating new processes, new pathways and being very communicative with the companies. So, you've asked, Are you waiting for the FDA to do something? In some sense, yes, but it's a very active process. Its an active engagement with the agency.I do think that there are some exciting regulatory changes ahead.

One issue continually brought up with AI is bias built into algorithms. How do you work to prevent this from happening and then fix the problem if it is recognized?

WALACH: You want to make sure that on the one hand, you trained the data on a very robust, diverse set that isn't biased towards a certain population. On the other hand, we want to make sure that even after we release a product to market, we may encounter bias that was unexpected initially. We want to make sure that we keep monitoring performance over time.For me, it's battling biases with data. That data is the protector against bias in all stages of the product lifecycle.

KOTTLER: Eventually, we may end up going in the opposite direction. Right now, we're trying to have data that's as generalizable as possible so you can apply the same algorithm everywhere. But, ultimately, that means that the specificity and value for a certain patient will have to decrease, even if it's just a little bit.

As the FDA evolves, and as these AI algorithms evolve, we will be able to have an AI algorithm that's suited for a specific population, and that means it's going to be much more accurate for that population.

Where do you see AI use heading?

KOTTLER:The next area that we're getting into is predictive medicine. While medicine has always been about the treatment of disease, we really need to move more into the prevention of disease. AI can help us with the prevention of disease because it's detecting things that we may not be able to detect as humans.If we combine that information with the other information that we have as humans, we can start to predict which patients are more at risk.

For example, for breast cancer, maybe certain patients should be getting mammograms or their imaging studies much more frequently than others. Maybe we can identify if certain patients are at risk for developing a bone fracture because we can look at the quality of their bones and see which ones are the most at risk for developing osteoporosis. These are all preventative measures that I think we're going to get much more involved in.

We're going to combine that with information from the patient systems that are getting more prevalent, like wearables, to provide a more holistic view of the patient.

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Radiology Partners, Aidoc talk AI adoption, handling bias, FDA actions - MedTech Dive

The company launched diverse training data sets for natural language processing initiatives.

Image: iStock/metamorworks

Training data provider Appen data just launched recently developed diverse training data sets for natural language processing initiatives in an effort to ensure end-users will receive the same experience, regardless of language variety, dialect, ethnolect, accent, race or gender.

Appen said it realized that AI projects that are based on biased or incomplete data don't work for everyone. It is enabling organizations to launch, update and operate unbiased AI models through a variety of projects and partnerships focused on the diversity of languages and dialects, the company announced on its website.

In March, Proceedings of the National Academy of Sciences found that popular automated speech-recognition systems used for virtual assistants, closed captioning, hands-free computing and more, "exhibit significant racial disparities in performance."

SEE:Juggling remote work with kids' education is a mammoth task. Here's how employers can help (free PDF)(TechRepublic)

The report concludes "that more diverse training datasets are needed to reduce these performance differences and ensure speech recognition technology is inclusive. Language interpretation and natural language processing systems suffer from the same challenge and require the same solution."

"The quality and diversity of training data directly impacts the performance and bias present in AI models," said Mark Brayan, CEO at Appen, in a press release. "As a data partner, we can supply complete training data for many use cases to ensure AI models work for everyone. It's critical that we engage a diverse group of individuals to produce, label, and validate the data to ensure the model being trained is not only equitable, but also built responsibly."

With a goal to create AI for everyone, Appen developed a variety of projects and partnerships which focus on the diversity of languages and dialects.

As an example, the Appen website explained:

Without setting out to do so, biased AI data can set off a wave of information that is not only not valuable toward research, but can actually be detrimental.

"Biased AI data leads to projects that can fail to deliver the expected business results and harm individuals they are supposed to benefit," said Dr. Judith Bishop, senior director of AI specialists at Appen. "The scale and complexity of AI projects makes it impossible for most companies to acquire sufficient unbiased high-quality data without partnering with an AI data expert." She added, "Developing the most diverse and expert crowd of data annotators provides the industry with a clearly differentiated resource for building fair and ethical AI projects."

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LOS ANGELES, May 3, 2021 /PRNewswire/ --MeetKai [www.meetkai.com]- the AI Personalized Conversational Search company - today launches the first voice-operated AI assistant that uses conversation, personalization, and curation to assist its users. The MeetKai app is available now for free in the US.

Unlike other AI-assistants, MeetKai's sophisticated voice recognition and AI integration remembers its users preferences and context to respond within seconds. This enables Kai to respond to questions as specific as: "Hey Kai, I wanna watch a Joaquin Phoenix movie that's not The Joker".

MeetKai's unique features include:

James Kaplan, MeetKai Co-Founder & CEO, comments: "As a user and enthusiast of known AI assistants myself, I started finding ways to make them better and took on the challenge to build a true virtual assistant in the form of the MeetKai app".

Kaplan founded MeetKai two years ago, pioneering a revolutionary new approach to search called AI Personalized Conversational Search. He comments: "We're very excited to release our AI technology for the first time, through a fun AI Virtual Assistant, accessible to everyone. The MeetKai app is a great first step for our technology capabilities, and we can't wait to share more".

Weili Dai, MeetKai Co Founder & Chairwoman, adds: "People are ready for a leapfrog beyond the basic and limited search that currently dominates the market. That's why we redefined AI assistance by giving each user a completely unique experience...and we made it free. Limits blur when there's true passion and commitment and MeetKai will be at the forefront of AI, conversation, and innovation".

MeetKai is now available in the iOS, Google Play, and AppGallery App Stores at no cost. The app is currently available in the US, Mexico, Europe, and Asia, and will be available in more countries and launching more industry technology in the near future.

About MeetKai Inc.MeetKai Inc. is a pioneering company in language recognition and search technologies. Its unmatched portfolio includes a true multi-turn search recognition system. MeetKai's technology is deployed globally through iOS, Google Play, MeetKai website, and the AppGallery. Visit http://www.meetkai.com for more info & latest MeetKai news.

Cindy Fischer 818-720-9241 [emailprotected]

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MeetKai - The Next Gen AI Assistant - Launches Today in the US - PRNewswire

European Union (EU) officials have proposed new rules that could restrict and even ban some uses of artificial intelligence (AI) within its borders. That could include some technology developed by U.S. and Chinese-based tech giants. The rules would be the most significant international effort to regulate the use of AI to date.

The Coordinated Plan on Artificial Intelligence 2021 Review, put forth by the 27-nation bloc, could set a new standard for technology regulation.

If passed, rules could impact how facial recognition, autonomous vehicles, and even algorithms that are employed in online advertising are used across the EU. It could also limit the use of AI and machine learning as it applies to automated hiring, school applications and credit scores. It would ban AI outright in situations deemed risky, including government social scoring systems where individuals are judged on their behavior.

This could be the first-ever legal framework on AI, and the EU has said the new Coordinated Plan with Member States would guarantee the safety and fundamental rights of people and business, while also strengthening AI uptake, investment and innovation across the EU.

With these landmark rules, the EU is spearheading the development of new global norms to make sure AI can be trusted, Margrethe Vestager, the European Commissions executive vice president for the digital age, said in a statement. By setting the standards, we can pave the way for to ethical technology worldwide and ensure that the EU remains competitive along the way.

The EU has maintained that the new AI regulations could ensure that the Europeans can trust what AI has to offer, and would create flexible rules that could address the specific risks posed by AI-based systems. AI systems considered a clear threat to the safety, livelihoods and rights of people will be banned.

High-risk use of AI would include critical infrastructure, including transport, which could put the lives and health of citizens at risk; educational and vocational training, such as the scoring of exams; and law enforcement where it could interfere with peoples fundamental rights. In those cases, the high-risk AI systems would be subject to strict obligations before they could be put on the market, and would require logging of activity to ensure traceability of results, high quality of datasets, high level of security and accuracy, and appropriate human oversight measures to minimize risk.

AI is a means, not an end, explained EU Commissioner for Internal Market Thierry Breton.

It has been around for decades but has reached new capacities fueled by computing power, added Breton. This offers immense potential in areas as diverse as health, transport, energy, agriculture, tourism or cyber security. It also presents a number of risks. Todays proposals aim to strengthen Europes position as a global hub of excellence in AI from the lab to the market, ensure that AI in Europe respects our values and rules, and harness the potential of AI for industrial use.

This isnt the first time the EU has attempted to create regulation around new technology that surpasses anything else in the world. This has mainly been focused on privacy, including search results, but also in how personal information can be used by tech firms.

Now it addresses the developing technologies of AI and machine learning, but the question is whether such a hard line could limit the efforts by the tech giants. Or is this the best course of action to ensure privacy, security and to maintain a fair and level playing field for all involved?

First and foremost, allowing technologies to be developed unilaterally without any oversight is an effective vote for market dominating behavior, technology industry analyst Charles King of Pund-IT, told ClearanceJobs.

Weve seen it happen among tech industry behemoths, including Facebook and Google, and in countries, like China and government agencies in the U.S. and elsewhere, King added.

The EU hold all the cards right now, and the tech firms will have to play by their rules or ignore the European market. The latter really isnt an option.

Along with trying to place some restraints on potentially damaging behavior, the EU is also acting from a position of historic strength, explained King. The organization has aggressivelypursued businesses that it believes are acting against the interests of consumers and markets, and has passed regulations including GDPR that have successfully influenced global businessesand markets.

One of the biggest concerns would be whether such strict regulations simply make it too hard for businesses to play by the EUs rules. AI could be one of those areas where this could seem like stifling but in the end could ensure that control over the technology is, in fact maintained accordingly.

The writer and futurist Arthur Clarke famously asserted, Any sufficiently advanced technology is indistinguishable from magic,' said Jim Purtilo, associate professor of computer science at the University of Maryland. If thats the definition, then todays decision systems based on artificial intelligence technologies surely qualify as magic. They are subtle, tremendously complex methods which defy full explanation as to how a given result was computed.

Part of the issue is in understanding exactly what is entailed by AI. While it is easy to think of the science-fiction version of a thinking computer or android, in reality it is just ever-more complicated algorithms.

There has always been some aura of mystery to it. In some sense, AI is the area that stops being AI once we understand it, added Purtilo.

Many fairly ordinary forms of computing for example logic systems and computer memory once fell under a heading of AI research, he noted. What differs today is the scale of decisions that people will leave to machines. It used to be that at least programmers had an understanding of how their programs computed a result, but with AI the programmers generally cant work back to justify how an outcome was reached. That programs accuracy not a big deal when all it is doing is tagging photos of your friends in an album on your phone, but it becomes a very big deal for individuals who fall under suspicion of police based on wide deployment of facial recognition technology.

Given the understanding of what is, and to some extent what is not, AI, the question then becomes whether the EU is taking its regulation of AI too far?

Im not particularly afraid of computer programs, but Im terrified of the bureaucrats who use them thinking they escape responsibility by pretending to be mere servants of science which, by virtue of AI methods, is somehow settled even if not explained, warned Purtilo.

I thus see the EUs move as being less about AI than it is about policy, Purtilo told ClearanceJobs. Government practices should offer transparency and accountability, but as AI methods offer neither, these proposed regulations represent a first attempt to push back at opaque technologies that cloak the basis for impactful decisions.

That could stifle AIs development, or perhaps simply allow it to be better controlled and managed.

Whether or not this latest effort will succeed is anyones guess, said Pund-ITs King. But the EU is taking action because it believes it should and because it can.

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The EU's Ambitious AI Regulations: Increasing Trust or Stifling Progress? - ClearanceJobs

SAN FRANCISCO, May 4, 2021 /PRNewswire/ --The 2021 SIG Procurement Technology Summit runs May 4-6, 2021 online this year due to the pandemic and sourcing professionals are more hungry than ever for advanced technology to predict and win faster savings.

Supply disruptions from the so-called "Big Freeze" in Texas to the ship blocking the Suez Canal have overwhelmed sourcing teams -- not to mention the global microchip shortage. As supply complexity increases due to the acceleration of new product launches, sourcing teams at SIG's Summit will be looking for technology solutions that can help them do more with less.

"The value of procurement within companies has fundamentally changed," says former Chief Procurement Officer Matt Ziskie, who also serves on the Bid Ops Board of Directors. "Category managers want to spend their time focused on the highest value activity that supports their key stakeholders. They shouldn't have to spend time calling up five friends to find out if they are getting a good price or not. Bid Ops shows procurement teams what they should be paying and gets the whole process out of email-and-spreadsheets, which lets buyers focus on more important aspects of their job."

That's one explanation for why Bid Ops has seen a surge of interest from manufacturing firms, growing over 300% in the past year. With a roster of customers including Bel Brands, Autotruck, Dover Chemical and Kurita Water, Bid Ops predictive sourcing driven by AI is turning heads in industries that have long embraced an Excel-based approach to sourcing.

Jean-Michel Dos Remedios, a sourcing leader at Bel Brands, said of Bid Ops capabilities: "In the procurement world, time is of the essence. Bid Ops has really helped to kickstart our digital transformation journey by giving us our time back through leveraging data and AI. These innovations make us faster, and the faster we get, the more we can accomplish, and the more successful we can be."

Bid Ops predictive sourcing platform offers a complete RFP, RFQ, RFI, Reverse Auction, and Spot Buy capability complete with KPI dashboards for tracking a team's savings pipeline alongside automated reporting on supplier diversity and sustainability. The platform includes a messaging app and document storage to get all supplier communication out of email.

"It's the only platform where you can run a fully autonomous RFQ with only a few clicks," says Bid Ops CEO Edmund Zagorin. "Sourcing teams are famously overworked. Many sourcing teams put in heroic efforts during the pandemic to secure enough PPE while at the same time re-negotiating office leases and corporate travel agreements. Now that businesses are re-opening, it's clear that sourcing teams need additional resources to continue delivering peak performance. That's why customers are turning to Bid Ops."

SIG's Procurement Technology Summit coincides with Bid Ops welcoming new team members, including long-time collaborator Eric Buras as Head of Machine Learning and Data Science. "Our engineering team is really excited by the results our customers are seeing," says Eric. "When you can see the price paid variation for the exact same item, it becomes clear that the savings opportunities are quite compelling."

About Bid OpsBid Ops is the only predictive sourcing software built to keep your business ahead of the market. Your procurement team can leverage Bid Ops predictive AI to drive 2-5x more savings by getting better quotes faster. Learn more at http://www.bidops.com

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Sourcing teams explore Bid Ops predictive sourcing AI at SIG Procurement Technology Summit - PRNewswire