Category Archives: Ai

Sherpa, a startup from Bilbao, Spain that was an early mover in building a voice-based digital assistant and predictive search for Spanish-speaking audiences, has raised some more funding to double down on a newer focus for the startup: building out privacy-first AI services for enterprise customers.

The company has closed $8.5 million, funding that Xabi Uribe-Etxebarria, Sherpas founder and CEO, said it will be using to continue building out a privacy-focused machine learning platform based on a federated learning model alongside its existing conversational AI and search services. Early users of the service have included the Spanish public health services, which were using the platform to analyse information about COVID-19 cases to predict demand and capacity in emergency rooms around the country.

The funding is coming fromMarcelo Gigliani, a managing partner at Apax Digital; Alex Cruz, the chairman of British Airways; and Spanish investment firms Mundi Ventures and Ekarpen. The funding is an extension to the $15 million Sherpa has already raised in a Series A. From what I understand, Sherpa is currently also raising a larger Series B.

The turn to building and commercializing federated learning services comes at a time when the conversational AI business found itself stalling.

Sherpa saw some early traction for its Spanish voice assistant, which first emerged at a time when efforts from Apple in the form of Siri, Amazon in the form of Alexa, and others hadnt really made strong advances to address markets outside of those where English is spoken.

The service passed 5 million users as of 2019 customers using its conversational AI and predictive search services include the Spanish media company Prisa, Volkswagen, Porsche and Samsung.

But as Uribe-Etxebarria describes it, while that assistant business is still chugging along, he came up against a difficult truth: the biggest players in English voice assistants eventually did add Spanish, and the conversational AI investments they would make over time would make it impossible for Sherpa to keep up in that market longer-term on its own.

Unless we did a big deal with a company, we wouldnt be able to compete against Amazon, Apple and others, he said.

That led the company to start exploring other ways of applying its AI engine.

It came on to federated privacy, Uribe-Etxebarria said, when it started to look at how it might expand its predictive search services into productivity applications.

A perfect assistant would be able to read emails and know which actions to take, but there are privacy issues around how to make that work, Uribe-Etxebarria said. Someone suggested to him to look at federated learning as one way to teach its assistant to work with email. We thought, if we put 20 people to work, we could build something to read and respond to emails.

The platform that Sherpa built, Uribe-Etxebarria said, worked better than they had anticipated, and so a year later, the team decided that it could use it for more than just triaging email: it could be productized and sold to others as an engine for training machine learning models with more sensitive data in a more privacy-compliant way.

Its not the only company pursuing this approach: TensorFlow from Google also uses federated learning, as does Fate (which includes cloud computing security experts from Tencent contributing to it), and PySyft, a federated learning open-source library.

Sherpa is working with several companies under NDAs in areas like healthcare, and Uribe-Etxebarria said it plans to announce customers in other areas like telecoms, retail and insurance in the near future.

Early Stage is the premier how-to event for startup entrepreneurs and investors. Youll hear firsthand how some of the most successful founders and VCs build their businesses, raise money and manage their portfolios. Well cover every aspect of company building: Fundraising, recruiting, sales, product-market fit, PR, marketing and brand building. Each session also has audience participation built-in theres ample time included for audience questions and discussion. Use code TCARTICLE at checkout to get 20% off tickets right here.

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Sherpa raises $8.5M to expand from conversational AI to B2B privacy-first federated learning services - TechCrunch

The IBM Opportunity

IBM Chairman and CEO Arvind Krishna says that the companys latest investments have IBM poised to continue with capturing cloud share. In a new letter to investors, Krishna outlined areas where IBM has invested in hybrid cloud and artificial intelligence and discussed how the company is driving growth going forward. Ultimately, IBM is positioned to lead as we enter the era of hybrid cloud and AI, Krishna said.

Every company in every industry wants to build a much stronger digital foundation to fundamentally change the way its business works, Krishna said in the letter. There is no going back. In the next two to three years, we expect to see digital transformation at a rate that, before 2020, we thought would take 5 to 10 years.

IBMs cloud-related revenue grew 20 percent to $25.1 billion, excluding the impact of currency and divestitures, and now represents more than a third of the companys total revenue, Krishna said. The urgency for digital transformation continues to fuel momentum for our business.

Hybrid cloud and AI are the two next great shifts in the technology landscape, and IBM is positioning itself to play a key role in this swift and massive transformation, he said. We see the hybrid cloud opportunity at $1 trillion.

What follows are IBM CEO Arvind Krishnas six reasons for how IBM is positioned to lead the industry in hybrid cloud and AI.

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Arvind Krishna: 6 Reasons IBM Is 'Positioned To Lead' Hybrid Cloud, AI - CRN

March10, 20215 min read

Opinions expressed by Entrepreneur contributors are their own.

Artificial Intelligence (AI) is revolutionizing the way we live and work, so today I invite you to learn more about this topic by approaching it from three different and increasingly complex segments.

Broadly speaking, we can refer to AI as the simulation of human intelligence by machines. In other words, a discipline that tries to create systems capable of learning and reasoning like people .

Importantly, Artificial Intelligence is the most debated technology of the 21st century. Today, it is widely used to solve complex problems and facilitate human tasks.

Do you still have doubts about what artificial intelligence is? Join me in the following segments.

Image: Depositphotos.com

Imagine a machine that organizes your closet the way you like itorserves refreshments to your friends during a party. That is, a robot that performs the activities that you do daily. Well, this is precisely what Artificial Intelligence makes possible. Through algorithms and mathematical functions, AI provides machines with intelligence similar to that of a human to perform everyday tasks such as playing soccer with you or even giving you some dance lessons.

Through Artificial Intelligence, machines can learn, reason and solve problems three capabilities that make the robot artificially intelligent.

Image: Depositphotos.com

Artificial intelligence works by combining large amounts of data with fast, iterative processing, as well as intelligent algorithms that allow software to automatically learn and identify patterns. The goal of AI is to create systems that can function intelligently and independently of human beings AND perform tasks in the professional field such asthe construction of buildings or in factories automated by intelligent robots.

Artificial intelligence has subfields, among which the following stand out:

Natural language processing: The ability of computers to analyze, understand, and generate human language, including speech. Some products in this area are Amazon Alexa andGoogle Voice.

Image: Depositphotos.com

To develop this segment, I am going to focus on the fact that Artificial Intelligence allows machines tosolve problems as people do The interesting thing here is to compare them with humans and understand how they interact with them. To do this, the following points must be taken into account:

It is worth mentioning that there are currently 4 types of Artificial Intelligence, according to Professor Arend Hintze:

Purely reactive: Here, machinesdo not have the ability to form memories, therefore they cannot use their experience in decision-making. These are machines that make predictions based on parameters. An example of this type is Deep Blue, the IBM-made supercomputer that defeated chess grandmaster and world champion Garri Kasparov in 1997.

Limited memory: Machines of this type do store memories and use that "past" to make decisions.That is, they use the knowledge of their previous experiences to elaborate a response. However, this information is kept for a certain time, so their vision of the past is very "limited."To put it in another way, they store only a limited number of recent experiences that they use to perform their calculations and act in the environment they do not use that experience to learn it forever, as people would. An example of this type is autonomous cars.

Theory of mind: One of the objectives of Artificial Intelligence is to ensure that machines can emulate the learning process that people have. The machines of this classification aremore advanced, and they are capable of processing and expressing emotions.Theoretical knowledge of these emotions allows them to know how people or elements in their environment think and feel,to predict what we expect them to do, and to later adjust their behavior according to those predictions.

Self-awareness: This isthe highest step in the hierarchy proposed by Professor Hintze. Here,machines are aware of themselves, recognize their internal states and become aware of the place and position they occupy within an environment. In this case, theynot only predict behaviors but also emotions, because they understand what theyentail in all theircomplexity.

Amazing isn't it?

As we can see in the three explanatory segments above, Artificial Intelligence is designed to work with humans and facilitate their daily tasks. We currently find it applied inbanks, online customer service, cybersecurity, virtual assistants, smartphones, cars, social networks, video games, and in many other aspects of daily life.

This panorama allows us to see that, in the near future, we will no longer think about how to use emerging technologies, but we will have to learn to relate to them and live with machines in a natural way.

If you want to learn more about Artificial Intelligence, I invite you to be part of Ai Lab School, the first practical education program in AI programming in Mexico. The course lasts 9 months, where you learn, based on projects, to develop solutions using advanced algorithms with AI. Once the course is finished, you access the next stage: job placement. Here, the Ai Lab School team guides and helps you apply for jobsin Silicon Valley and in technology hubs in the United States, in collaboration with the Mexican talent connection programTalentum Space.

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What Is Artificial Intelligence? Whether You're a Student, Professional, or Scientist, Here's What It Means. - Entrepreneur

Israels retrain.ai, which uses AI and machine learning to read job boards at scale and gain insight into where the job market is going, has closed a $9 million Series A led by Square Peg. Since retrain.ais $4 million seed round last year was unannounced (led by Hetz Ventures, with TechAviv and .406 Ventures participating), that means its raised a total of $13 million. Its competitors include Pymetrics, which has raised $56.6 million, and Eightfold.ai, which has raised $176.8 million.

As well as the funding, the company has secured a first deal with the Israeli Department of Labor to look at the changing nature of the Israeli job market in light of the pandemic.

With technology eating into the traditional labor market, retrain.ai says its platform can look at which jobs are being advertised, which jobs are going down in popularity and see early warning signals as to where new jobs are going to appear. This can help form policy for large organizations and governments.

Retrain.ais CEO is Dr. Shay David, who is best known for co-founding the video enterprise leader Kaltura, which first appeared at TechCrunchs first-ever conference in 2007. Isabelle Bichler-Eliasaf is the companys COO and Avi Simon is retrain.ais CTO.

Dr. Shay David said: What was once the regular tide of change in the workforce has evolved into a tsunami, especially pronounced by COVID-19 and its huge impact on the labor market this has been a wake-up call. Unemployment and underemployment is going to affect a billion people globally in the next few decades. Our vision is to help 10 million workers get the right jobs by 2025 and help organizations navigate efficiently through the wave of change.

Retrain.ai is the first investment by Square Pegs new $450 million fund. The VC previously invested in Canva, Stripe, Fiverr and Airwallex.

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Israels retrain.ai closes $13M to use AI to understand early signals in the changing jobs market - TechCrunch

CHICAGO, March 11, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence In Genomics Market by Offering (Software, Services),Technology (Machine Learning, Computer Vision), Functionality (Genome Sequencing, Gene Editing), Application (Diagnostics), End User (Pharma, Research) - Global Forecasts to 2025", published by MarketsandMarkets, the global AI in Genomics market is projected to reach USD 1,671 million by 2025 from USD 202 million in 2020, at a CAGR of 52.7% between 2020 and 2025

Browse in-depth TOC on "Artificial Intelligence in Genomics Market"141 Tables24 Figures 154 Pages

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The need to control drug development and discovery costs and time, increasing public and private investments in AI in genomics, and the adoption of AI solutions in precision medicine are driving the growth of this market. However, the lack of a skilled AI workforce and ambiguous regulatory guidelines for medical software are expected to restrain the market growth during the forecast period.

Machine learning to dominate the AI in Genomics market in 2019

Based on technology, the Artificial Intelligence in GenomicsMarket is segmented into machine learning and other technologies. The machine learning segment dominated this market in 2019, as pharmaceutical companies, CROs, and biotechnology companies have widely adopted machine learning for drug genomics applications. This is because machine learning can extract insights from data sets, accelerating genomic research.

Diagnostics segment accounted for the largest share of the AI in Genomics market, by end user, in 2019

Based on application, the Artificial Intelligence in GenomicsMarket is segmented into diagnostics, drug discovery & development, precision medicine, agriculture & animal research, and other applications. Diagnostics was the largest application segment in genomics market in 2019. The large share of this segment can be attributed to the increasing research on diseases and the decreasing cost of sequencing.

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North America is the largest regional market for AI in Genomics in 2019

In 2019, North America accounted for the largest share of the AI in Genomics market, followed by Europe. The large share of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US.

Prominent players in the Artificial Intelligence in GenomicsMarket are IBM (US), Microsoft (US), NVIDIA Corporation (US), Deep Genomics (Canada), BenevolentAI (UK), Fabric Genomics Inc. (US), Verge Genomics (US), Freenome Holdings, Inc. (US), MolecularMatch Inc. (US), Cambridge Cancer Genomics (UK), SOPHiA GENETICS (US), Data4Cure Inc. (US), PrecisionLife Ltd (UK),Genoox Ltd. (US), Lifebit (UK), Diploid (Belgium), FDNA Inc. (US), DNAnexus Inc. (US), Empiric Logic (Ireland), Engine Biosciences Pte. Ltd. (US)

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Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024https://www.marketsandmarkets.com/Market-Reports/ai-in-drug-discovery-market-151193446.html

Genomics Market by Product & Service (System & Software, Consumables, Services), Technology (Sequencing, PCR), Application (Drug Discovery & Development, Diagnostic, Agriculture), End User (Hospital & Clinics, Research Centers) Global Forecast to 2025https://www.marketsandmarkets.com/Market-Reports/genomics-market-613.html

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Artificial Intelligence in Genomics Market worth $1,671 million by 2025 - Exclusive Report by MarketsandMarkets - PRNewswire

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In a story last year about nine books I read about AI in 2020, I called Your Computer Is On Fire a book worth watching out for this year. Its released this week, and I was not disappointed. The premise of the book is that techno-utopianism should die because its too dangerous to be allowed to continue. This argument came up recently in the context of Amazon workers in factories with robotics getting hurt more often than workers in factories without robots. But once people throw away unrealistic visions of outcomes, the history of technology looks very different.

The book attempts to interrogate how the legacy of social constructs and media narratives have shaped computing. It invites people to think critically about notions of purity surrounding data, the concealment of the carbon footprint the cloud represents, the whiteness of robots, and the wires and resources involved with making the world wireless. Your computer is on fire in part, authors argue, because of automation that perpetuates racism and sexism, and the growth of resource-intensive datacenters and the cloud at a time when climate change is an existential threat for the planet.

The title of this book is meant to prepare you for a series of 16 provocative essays that consider the history of technology, media, and policy, from Siri disciplines and the cloud as a factory to how the internet will be decolonialized and tech for the Global South. Each essay takes readers on a journey through a topic to consider the ethical and societal implications of technology over the long term, an approach former Ethical AI team lead Margaret Mitchell suggested for Google.

Contributors to the collection of essays include Safiya Noble, author of Algorithms of Oppression, who wrote an essay about race and gender stereotypes that permeate robotics and the role of robotics in policing, prisons, and warfare.

We have to ask what is lost, who is harmed, and what should be forgotten with the embrace of artificial intelligence and robotics in decision-making. We have a significant opportunity to transform the consciousness embedded in artificial intelligence and robotics, since it is in fact a product of our own collective creation, Noble wrote in the book.

Another essay, by Nathan Ensmenger, argues that the cloud is a factory, and it examines the extent to which datacenters demand a lot of energy, water, and the mining of rare mineral resources like cobalt, which has led to accusations that Big Tech companies aided in the death or serious injury of children. That essay also walks through a comparison between Amazon online today and Sears mail-order catalogs a century ago, and compares Amazon transportation and distribution strategy to Standard Oil.

Understanding, for example, that in the past women made up much of computation work treated as menial and feminine for most of its early history helps illuminate ongoing problems of racism and sexism in tech environments that women especially Black women describe as toxic.

I also found something terribly human in an essay arguing that a network is not a network, which looks at the history of large networks built in Chile, Russia, and the United States. Benjamin Peters says that history shows that just because a network works does not mean it works as its designers intended.

[N]etwork projects are twice political for how they, first, surprise and betray their designers, and, second, require actual institution building and collaborative realities far richer than any design, Peters wrote.

Editors of the book include Mar Hicks, a tech historian at the Illinois Institute of Technology in Chicago and an associate editor of the IEEE Annals of History of Computing. They are joined by science and technology historian and University of California, Irvine professor Kavita Philip; Peters, a media historian and University of Tulsa professor; and Stanford University history professor Thomas Mullaney.

The editors take pains to state that the books conclusions arent meant to be an overly dark view of the future or to give people the impression things are hopeless. There is hope, they argue, but recent trends should act as an alarm.

What I also took away from this book is the continuing value of critical analysis. In a recent paper, researchers recommended reporters persist in sharp questioning, declaring, Technology journalism is a keystone of equitable automation and needs to be fostered for AI.

In the final pages of the book, Your Computer Is On Fire also addresses the role of media and the writers of narratives in tech and AI trends.

Tech will deliver on neither its promises nor its curses, and tech observers should avoid both utopian dreamers and dystopian catastrophists. The world truly is on fire, but that is no reason it will either be cleansed or ravaged in the precise day and hour that self-proclaimed prophets of profit and doom predict. The flow of history will continue to surprise, Peters writes.

Even if youre like me and follow trends in artificial intelligence through news, books, and research papers, you may still learn parts about the history of technology in this book that you didnt know, because this book extends across an arch of history. And as editors lay out in the afterword, they hope the messages contained within will be viewed as obvious decades from now.

This lens viewing computing and artificial intelligence across the span of decades and consideration of social and historical context was previously espoused by Ruha Benjamin, who last year argued in the context of deep learning that computational depth without historic or sociological depth is superficial learning. But the collection of impactful tech issues interrogated over the span of decades in this book makes it recommended reading for anyone interested in the impact of tech policy in businesses and governments, as well as people deploying AI or interested in the way people shape technology.

This book presents compelling arguments for essential topics at the center of business and society. By using computational history as a foundation, its able to, as Noble put it, underscore how much is at stake when we fail to think more humanistically about computing.

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Your Computer Is On Fire draws on tech history to critique AI and the cloud - VentureBeat

Leasing consultants report current industry methods for managing leads are not only highly inefficient and frustrating, they're also one of the industry's most significant challenges. By proactively identifying prospective renters who are more likely to lease immediately, both renters and leasing consultants benefit from a far more efficient, personalized experience.

PowerLeads AI features three main components. First, Power Prospects identifies and flags renters who are statistically proven to be up to 2X more likely to lease immediately. Additionally, our system will now surface and provide leasing agents over 50 unique renter characteristics more than 5X more information than the competition that will help leasing agents best meet renter needs.Lastly, PowerLeads AI provides access to up-to-the-minute information allowing leasing teams to see a prospect's interests in real time enabling them to better understand prospective renters' needs.

"Our studies showed that the vast majority of leasing teams want a way to understand which prospects are more likely to lease, so we knew that it was vital to solve this challenge with an industry-first solution," said Zumper's Chief Growth Officer, Tanguy Le Louarn. "In fact, 78% believe having more data on prospects would help them convert more leads. By using cutting edge AI and machine learning to provide predictive insights and data, we'll not only provide higher quality leads, we'll also enable a faster leasing process."

PowerLeads AI will go live on March 11, 2021. For more information about how PowerLeads AI can support advertising needs, please email [emailprotected]

About Zumper: Zumper is the fastest growing and third largest rental platform in North America, serving one in three U.S. adults.Zumper aims to make renting an apartment as easy as booking a hotel. With over 70 million users, Zumper's free online and mobile rental search marketplace has become the largest of any startup in the industry. Headquartered in San Francisco, Zumper has 200 employees across the U.S. and acquired PadMapper in 2016. The company has raised over $140 million in funding from investors including e.ventures, Greycroft, Dawn Capital, Kleiner Perkins, Goodwater Capital, Axel Springer, Stereo Capital, the Blackstone Group, Breyer Capital, Foxhaven Asset Management, Andreessen Horowitz, Greylock, NEA, CrunchFund, xfund, Divco West, MMC Technology Ventures, Scott Cook, and the DeWilde Family Trust. Learn more atZumper.comor email [emailprotected].

Interested in joining the Zumper team? Check out open positionshere.

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Zumper is the first to bring AI to apartment rental leads - PRNewswire

Morgan Lewis partners Mike Pierides and Andrew J. Gray IV and associate Oliver Bell recently presented a webinar, Bias Issues and AI, as part of the Artificial Intelligence Boot Camp series.

Bias in AI can result from assumptions in the machine learning process, or as a result of data that is imbalanced or incomplete and does not reflect a true representation of the relevant population. Examples of such skewed data include datasets that are mislabeled or misrepresentative of reality, systematic errors in the collection of data, or valuable data that is completely excluded, which in turn creates biased outputs.

The implications of bias in AI are widespread, and can affect recruiting processes, credit referencing, and insurance decisions, to name a few. For example, if an employer uses an AI tool for recruiting that uses historical data from the companys past and current employees, most of whom are male, then the AI system may incorrectly learn that the ratio of preferable candidates should match this historical data, therefore resulting in a biased outcome. As the role of AI increases in decision making across industries, the risks of bias also increase due to the large scale of data that can be processed by machines.

The risks associated with bias in AI can result in statutory, contractual, and common law liability. Laws that prohibit discrimination, like the Fair Housing Act in the United States and the Equality Act in the United Kingdom, provide examples of how biased AI could lead to liability for organizations.

There are steps that can be taken, if not to completely remove bias in AI, then at least to mitigate it. Examples include choosing a suitable AI provider, performing regular audits of algorithms, and employing a diverse programming or control team with antibias training and a culture of transparency.

As we look to the future, the detrimental impact and effects of bias in AI will likely increase as we see a continued focus on ensuring diversity and inclusion, which is likely to lead to new or updated legislation which AI bias may fall foul of. There is also the possibility of AI-specific legislation that places obligations on companies regarding their use of AI.

For more details on bias in AI, view the presentation slides or watch the recorded event.

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Recap: Bias Issues and AI | Morgan Lewis - Tech & Sourcing - JDSupra - JD Supra

This article originally appeared in the March 2021 issueofSecurity Businessmagazine. When sharing, dont forget to mention@SecBusinessMagon Twitter andSecurity Business magazineon LinkedIn.

Artificial intelligence (AI) performs services, recognizes patterns, learns object and situation relationships, and makes decisions. There is a paradigm shift provided by new, proven technologies, such as 5G, thermally efficient, low-power AI chipsets and AI itself, carrying the global surveillance and IoT sensor markets forward seemingly without a threshold.

When we ask a voice assistant to search for our favorite law enforcement show, we often get our intended choice first. Vehicle manufacturers are developing advanced driver-assistant systems (ADAS) like Automatic Emergency Braking (AEB) with the goal of creating processes like collision avoidance with both pedestrian and cyclist. The security industry is fortunately tasked with less critical tasks like recognizing objects, a potential threat, and an appropriate deterrence response.

It is also fortunate that our industry is leveraging the availability of more thermally efficient AI chipsets and Edge AI sensors like LiDAR and thermal imaging all of which are decreasing in price. AI chipsets with improved thermal management, such as the Ambarella CV5 introduced at CES 2021, supports four independent 4K video streams, AI capabilities, low power consumption 5nm node. This would be the answer to an underpowered IP camera processing vehicle Automatic Number Plate Recognition (ANPR) in multiple lanes of traffic producing choppy video, as streaming and recognition processes compete for resources.

Deep Neural Networks (DNN) need an enormous amount of data to learn. Security Solutions themselves are not intelligent; they leverage deep learning of a situational awareness report (sitrep) and historical data to deliver the most appropriate action. Data from sensors of multiple formats (visible light, infrared, audio, laser) and complex data from environmental, social media, crime datasets are becoming too massive to process by legacy operating procedures. Fortunately, companies like ESRI offer datasets linking crime, location, and time; in fact, its ArcGIS Insights is now available at no cost through the Disaster Response Program (DRP) to analyze the impact of the COVID-19 pandemic.

Privacy and data retention policies do create challenges for some industry AI solutions to improve for example, a retail loss prevention DNN might need to review days of differential video content, or content in scenes with enough variances in people, crowd movement, products on shelves and endcaps, lighting conditions and types of floor surfaces in order to recognize the basic behavior of a person planning, executing, and leaving the scene of a shoplift.

Corporate security and first responders can ingest, analyze and predict potential outcomes and share data as we move forward as an industry to allow AI to eventually perform learned, basic tasks and give us back the manpower we need to make critical decisions.

Ingest may be a new term to some in the security industry, but it is well-used in markets relying on big data or applied data sciences. For your customers to leverage todays AI solutions especially with video surveillance it will be advantageous to start collecting quality video content within a perimeter for higher assurance of quality alarm processing and response.

While human intelligence continues to lead, Edge AI presents security and safety management with a rebate of time for known processes.

In general, for IP video cameras, common video analytic features like object recognition, zone detection, number plate recognition are now available as AI algorithms embedded within the camera itself. It may not be clear how firmware updates affect AI, or if the algorithm is updated with the latest factory-trained model, or if it retains algorithm training for the specific use-case.

Solutions that have been developed all use updatable algorithms, and white and blacklists, such as basal temperature/fever screening and visual weapons detection by Athena Security; energy waveform signature of ballistics by EAGL; and drone RF signature/behavior detection by WhiteFox and Aero Defense.

Many Edge AI cameras, however, may also require the user to have a Video Management System to update an algorithm. The user should be cautioned to prioritize surveillance investments in Edge AI devices or more specialized weapons/drone detection software, rather than a potentially more isolated VMS that may become a pay-as-you-go service.

Many IP video camera manufacturers have touted AI features. Here are a few of the more recent developments:

Panasonics i-Pro Extreme cameras feature built-in AI for motion detection and analysis for precision object categorization, such as the difference between a human and bicycle at distance. It can detect objects that enter an area restricted by object category such as a pedestrian-only area or region as well as crossline or loitering detection. Privacy guard redacts video specifically faces or bodies. Panasonic also provides an SDK for third-party developers to add advanced features like weapons or fall detection.

Hanwha Techwins Wisenet7 chipset delivers in-camera AI features that include: AI-based object classification for detected objects,The attribute classification function of Wisenet P series AI cameras enables quick and accurate detection of people with or without face masks and immediately sends out an alert.Photo: Hanwha Techwin people, vehicles, license plates and faces; Reduction in false positive alerts, for improved monitoring in operations having many cameras; AI-based object tracking with some PTZ cameras; and an auto-tracking function that tracks vehicles and people an improvement over frame-based tracking.

Milesight delivers edge-based AI over a variety of cameras. Features include: Pre-trained deep learning model and continuously training of algorithms automatically; and three groups of algorithms, including Video Content Analysis (VCA), such as for human and vehicle detection; real-time people counting based on AI algorithms, with statistic reports for analysis; and AI Face Detection.

The use of Edge AI cameras is significant because they can serve as a model for simplified yet effective AI training workflow.

Industrial cameras can capture number plates at high speed and close distance for safety and tracking applications like rail transit and vehicle screening. The IDS NXT camera is an example of improved AI Training and integration with third party systems through web apps (no additional coding needed). The system can create training images and upload them, and users can assign labels (ex. "good" or "bad") so the AI can learn. This, in turn, starts automatic training of the neural network, eventually leading to full deployment.

As reported at CES last year, the use of LiDAR in small 3D cameras can deliver a wireframe image that preserves privacy, yet identifies a face even one partially obscured by a mask or PPE. Furthering on this trend, Intels RealSense ID, introduced at CES 2021, combines active depth with a neural network, a dedicated system-on-chip and embedded secure element to encrypt and process user data. Using deep learning, it adapts to users over time as they change physical features, such as facial hair and glasses or appear in different lighting conditions.

It remains to be seen if complex entry screening tasks will eventually be included in the average security solution; however, there are already AI-based solutions in the security/safety market that perform facial recognition and/or face matching where users are wearing masks, hats and the process is consistent across gender, age, and ethnicity. They can also perform elevated temperature screening, leveraging appropriate basal temperature measurement locations and multi-spectral imaging (usually visible light plus thermal imaging); as well as consistent on person, concealed and non-concealed weapons detection, front, side or rear-worn weapon.

Critical infrastructures can be dangerous places, and Edge AI sensors can play a life-saving role. High voltage wires, combustible chemicals, hazardous waste, and other threats can be detected early with thermal imaging and AI training. LiDAR sensors with an AI process can trigger alerts when personnel enter dangerous areas such as tunnels, railway tracks and bridges.

Steve Surfarois Chairman of the Public Safety Working Group for the Security Industry Association (SIA) and has more than 30 years of security industry experience. He is a subject matter expert in smart cities and buildings, cybersecurity, forensic video, data science, command center design and first responder technologies. Follow him on Twitter, @stevesurf.

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In a new study published in the journal Nature Biomedical Engineering, researchers at IBM say theyve developed an AI model that can assist in the rapid design of antimicrobial peptides the building blocks of proteins. The researchers say that the model outperforms other AI methods at designing such peptides and increases the success rate of identifying a viable candidate by 10%.

Antibiotics have transformed the world of medicine over the past century or so, but theyve also been overused, leading to the emergence of bacteria with powerful resistance. According to the Centers for Disease Control and Prevention (CDC), antibiotic resistance is one of the biggest public health challenges of our time. In fact, in the U.S. alone, nearly 3 million people die annually as a result of antibiotic-resistant infections.

Unfortunately, few new antibiotics are being developed to replace those that no longer work, in part because drug design is an extremely difficult, lengthy, and capital-intensive process. IBMs proposed solution is generative modeling, a subfield of AI that allows researchers to decide upfront what characteristics they want peptides to have versus guessing combinations.

Historically, material design of molecules, proteins, and altogether new peptides has been a complex simulation problem. Even small molecules made of only a few atoms have hundreds of possible combinations. To combat this, IBMs AI model pulls from a large dataset to reverse-engineer a peptides design and produce the desired peptide framework. Effectively, it shortens the time needed to create high-quality peptide candidates from years to potentially days while increasing the likelihood of identifying successful candidates to fight antibiotic drug resistance.

Within 48 days, IBM says its AI-boosted molecular design approach enabled it to identify, synthesize, and experimentally test 20 AI-generated novel candidate antimicrobial peptides. Two of them turned out to be potent against pathogens, very unlikely to trigger drug resistance in E. coli, and had low toxicity when tested both in vitro and in mice.

Beyond antibiotics, IBM says the generative AI system could potentially accelerate the design process of molecules for new drugs and materials. Our proposed approach could potentially lead to faster and more efficient discovery of potent and selective broad-spectrum antimicrobials to keep antibiotic-resistant bacteria at bay for good, IBMs Saska Mojsilovic and Payel Das wrote in a blog post. And we hope that our AI could also be used to help address the worlds other most difficult discovery challenges, such as designing new therapeutics, environmentally friendly and sustainable photoresists, new catalysts for more efficient carbon capture, and so much more.

IBMs latest work builds on an earlier study published in the journal Advanced Science by the companys researchers. It demonstrated a technique that enabled the coauthors to create up to 100 bacteria-fighting polymers in nine minutes, using AI and machine learning.

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IBMs AI may lead to new antimicrobials, drugs, and materials - VentureBeat