The visionary Slaughterhouse-Five author predicted the rise of artificial intelligence back in the 1950s and became convinced computers would replace the human workforce.

The science fiction legend also tells how large US corporations such as General Electric felt guilty about the tech revolution.

The biographical film reveals that he took a job at GE writing press releases when he could not make enough money from his short stories.

He said: "GE showed me a milling machine that was being run by punch cards. They could do it better than a man could but they were ashamed.

"They felt guilty about what they'd done. The whole emphasis now is throwing people out of work."

It would inspire his first novel Player Piano, in which humans were replaced by machines. It was published in 1952 for an advance of $2,500, but achieved little success.

For his entire writing career the chain-smoking author used a typewriter and never sent an email.

He said: "We're here on earth to fart around. And, of course, the computers will do us out of that. What the computer people don't realise, or don't care [about], is we're dancing animals. We love to move around."

Vonnegut, who died in 2007 aged 84, lectured on the value of the "extended family". He said: "We need more people in our lives. What we need is numbers."

After his sister and her husband died a month apart from each other, he took in their four teenage boys to add to his own three children.

Fame as an author arrived in 1969 with the publication of his modern classic, Slaughterhouse-Five, about his wartime experiences.

Vonnegut fought at the Battle of the Bulge in the Ardennes region of Belgium. He was captured by the Germans and taken to Dresden, where he was kept in a "meat locker" while the Allies bombed the city.

When released he saw the devastation first-hand and helped to bury the dead. When asked if he would rather not have seen what he did, he replied, with his trademark wit, "I wouldn't have missed it for anything!"

He continued to rail against computers throughout his life.

"All the new technology seems redundant to me. I was quite happy with the United States mail service - I don't even have an answering machine."

His parting shot to mankind was: "Life is no way to treat an animal."

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Kurt Vonnegut, the visionary writer who predicted the rise of artificial intelligence - Express

As part of the UK data protection authoritys new three-year strategy (ICO25), launched on 14 July, UK Information Commissioner John Edwards announced an investigation into the use of AI systems in recruitment. The investigation will have a particular focus on the potential for bias and discrimination stemming from the algorithms and training data underpinning AI systems used to sift recruitment applications. A key concern is that training data could be negatively impacting the employment opportunities of those from diverse backgrounds.

Bias is a particular risk in AI or machine learning systems designed not to solve a problem by following a set of rules, but instead to learn from examples of what the solution looks like. If the data sets used to provide those examples have bias built in, then an AI system is likely to replicate and amplify that bias. For example, if successful candidates reflected in the training data share certain characteristics (such as gender, demographic profile or educational profile) then there is a risk of excluding candidates whose profiles do not match those criteria.

The ICO also plans to issue refreshed guidance for AI developers on ensuring that algorithms treat people and their information fairly. However, even where algorithms and training data reflect ethical guidance, it will remain best practice to retain meaningful human involvement in decision-making. In effect, AI systems should produce recommendations for human review, rather than decisions. Under EU and UK GDPR Article 22, decisions based solely on automated processing, including profiling, which produce legal effects concerning him or her or similarly significantly affects the data subject are restricted unless they are:

The making or withholding of employment offers would clearly constitute legal or similarly significant effects.

Where special category personal data is involved, decisions based solely on automated processing are permissible only:

In addition, because decisions based solely on automated processing are considered to be high risk, UK GDPR requires a Data Protection Impact Assessment (DPIA), showing that risks have been identified and assessed, and how they are addressed. From there, compliance obligations include:

The ICOs indication that investigating AI in the context of recruitment will be one of its priorities over the next three years is significant. AI and machine learning tools are an increasingly valuable resource, but they come with compliance obligations that are likely to come under intense scrutiny as an area of particular interest to the ICO as the UKs data protection authority. To learn more, or to discuss the practicalities of compliance, please contact the authors.

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Artificial Intelligence (AI) and the Risk of Bias in Recruitment Decisions - Lexology

WILMINGTON, Del., July 25, 2022 (GLOBE NEWSWIRE) -- InterDigital, Inc. (NASDAQ:IDCC), a mobile and video technology research and development company, applauded the appointment of Xiaofei Wang to serve as Chair of the Topic Interest Group for Artificial Intelligence and Machine Learning (AIML) in IEEE 802.11, the IEEE working group dedicated to standards for wireless local area networks (WLAN).

The IEEE 802.11 AIML TIG is responsible for identifying and exploring potential use cases for AIML for Wi-Fi and will specifically describe AIML use cases applicable in 802.11 Wi-Fi systems. In addition, the topic interest group will investigate the technical feasibility of features that enable support for AIML.

We congratulate Xiaofei for this important leadership appointment, as it is a significant recognition of his research career and dedicated participation throughout 802.11 standards development, said Rajesh Pankaj, Chief Technology Officer, InterDigital. This position will greatly increase InterDigitals contribution to the operation of IEEE 802.11 working group and standardization process for new technologies, such as AIML for WLAN.

In addition to his leadership role in the AIML TIG, Xiaofei also serves as secretary for the IEEE 802.11bc Task Group.

The AIML TIG commenced its research activities in July and is expected to conclude in March 2023.

About InterDigital

InterDigital develops mobile and video technologies that are at the core of devices, networks, and services worldwide. We solve many of the industrys most critical and complex technical challenges, inventing solutions for more efficient broadband networks, better video delivery, and richer multimedia experiences years ahead of market deployment. InterDigital has licenses and strategic relationships with many of the worlds leading technology companies. Founded in 1972, InterDigital is listed on NASDAQ.

InterDigital is a registered trademark of InterDigital, Inc.

For more information, visit:www.interdigital.com.

InterDigital Contact:Roya StephensEmail: roya.stephens@interdigital.com +1 (202) 349-1714

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InterDigital's Xiaofei Wang Appointed Chair of the IEEE - GlobeNewswire

Professor Gemma Figtree

"We have long known that smoking, high cholesterol, diabetes and hypertension are risk factors for heart disease, but many people develop a silent build-up of plaque in their arteries and suffer subsequent heart attack without any of these risk factors," Professor Figtree said.

As part of the project, Professor Figtree and her team will apply complex algorithms to clinical and state-of-the-art 'omic' data to unravel novel biomarkers of heart disease in its early phases.

"As part of our study, we will analyse blood samples of individuals who have advanced imaging of their coronary arteries and characterisation of their coronary plaque burden. We will use advanced technology platforms to measure hundreds of thousands of small molecules in the blood, including, RNA, protein and metabolites, as well as genomic variations. With the help of machine learning, we will then be able to train our systems to discover novel signatures of coronary plaque."

"This will allow us to develop new methods for early diagnosis. Our vision is for a simple blood test that your GP could order on a regular basis to detect the earliest phases of coronary heart disease, many years before a heart attack. If positive, your GP could prescribe life-saving drugs that stabilise the plaque, and prevent plaque progression and heart attack."

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Artificial intelligence pioneers fund next generation of researchers - University of Sydney

NEW YORK--(BUSINESS WIRE)--Hyfe, Inc., the global leader in AI-powered cough detection, tracking and classification, has entered into an agreement with Merck, known as MSD outside the United States and Canada, to integrate its artificial intelligence-powered (AI) cough tracker technology into Mercks consumer disease educational efforts..

Hyfes AI-powered technology passively and remotely monitors the number of times a person coughs and the sound of the cough through any smart device, such as a mobile phone. This data collected over time provides the user with helpful information by revealing patterns and potential triggers like seasonal allergies that may be causing the cough that would otherwise go unnoticed or unaddressed.

The overwhelming majority of people cannot even come close to accurately estimating the number of times they cough per day, said Peter Small, M.D., chief medical officer of Hyfe, Inc. In order to help diagnose and treat patients, cough frequency and sound should be monitored and measured with the same accuracy as blood pressure, temperature, heartbeat and other biomarkers that are essential for assessing health and medical conditions.

Users of the app will be able to determine when and how long to track their coughing. The AI-powered technology records cough-related sounds and allows the user to compare the frequency and quality of their coughs against their own benchmark. This personalized approach provides users with more detailed information and confidential data about their individual situations to share with their health care providers.

Hyfe, Inc. is the global leader in AI-powered cough detection and classification that provides insight into cough patterns and correlations that can greatly improve treatment and prevention. With more than 280 million samples, Hyfe maintains the largest cough dataset in the world enabling the building of powerful models to track, manage and diagnose respiratory illnesses. Hyfe provides platforms and data for pharmaceutical companies, medical researchers, government agencies, health care providers and patients and has partnerships with leading academic institutions including Johns Hopkins University and the University of California at San Francisco. The company was founded in 2020 and is headquartered in Wilmington, Delaware. More information is available at Hyfe.ai, on social media @hyfeapp and LinkedIn at /hyfe.

Note to media: Digital assets for this story are available at hyfe.PressRep.net.

1 EO Meltzer, RS Zeiger, P Dicpinigaitis, et al. Prevalence and burden of chronic cough in the United States J Allergy Clin Immunol Pract, 9 (2021), pp. 4037-4044e2

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Hyfe Joins Forces with Merck to Deploy Its Artificial Intelligence-Powered Cough Detection and Counting Technology - Business Wire

Metaverse In Healthcare Market Scope

A new report titled Global Metaverse In Healthcare Market Professional Report 2022-2028 has been added to our repository of research reports. The report analyzes and estimates the Metaverse In Healthcare market on global, regional, and country levels. The report offers data from previous years and an in-depth analysis based on the revenue.

The Global Metaverse in Healthcare Market is valued at USD 5056.4 Million in 2021 and is expected to reach USD 7453.6 Million by 2028 with a CAGR of 33.7% over the forecast period.

While studying the global market for Metaverse In Healthcare, the report also thoroughly analyzes the driving factors, development trends, restraints, challenges, and lucrative challenges to showcase the current and future market scenario. We provided a comprehensive report that consists of the key market strategies based on the latest technologies, applications, and different geographies around the world. The market is expected to show significant growth over the forecast period due to the increasing demand for Metaverse In Healthcare Industry.

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Competitive Analysis-

Some of the key players for global Metaverse in Healthcare are Intutive Surgical Inc., CableLabs, AccuVein, Microsoft, Immersive Touch Inc., Koninklijke Philips NV, Google LLC, Medical Realities Ltd., Meta Platforms Inc., MindMaze SA, Chill Inc., Global Healthcare Academy, Aimedis B.V. & Aimedis Information Technology LLC and others.

These key players have adopted organic and inorganic growth strategies, including new product launches and developments, joint ventures, partnerships, research and development (R&D) activities, agreements, contracts, collaborations, and mergers & acquisitions (M&A) to strengthen their position in the market.

Metaverse In Healthcare Market Segmentation-

By Devices

By Component

By Technology

By End-User

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This report forecasts revenue growth at the global, regional, and local levels. It analyzes the most recent industry trends from 2022 to 2028 in each of the segments and sub-segments. Some of the major geographies included in the market are given below:

Table of Contents: Global Metaverse In Healthcare Market Research Report

Chapter 1: Global Metaverse In Healthcare Market Overview

Chapter 2: Economic Impact of Global Metaverse In Healthcare Market

Chapter 3: Global Market Size Competition by Industrial Producers

Chapter 4: Global Production, Revenue (Value) by Regions

Chapter 5: Global Supply (Production), Consumption, Exports, Imports, Geographic

Chapter 6: Global Production, Revenue (Value), Price Trend, Product Type

Chapter 7: Global Market Analysis Based on Application

Chapter 8: Metaverse In Healthcare Market Industry Value Chain

Chapter 9: Type A Metaverse In Healthcare Market Chain, Sourcing Strategies and Downstream Buyers

Chapter 10: Strategies and Key Policies by Distributor/Supplier/Trader

Chapter 11: Key Economic Indicators by Market Vendor

Chapter 12: Market Effect Factor Analysis

Chapter 13: Global Metaverse In Healthcare Market Forecast Period

Chapter 14: The Future of Markets

Chapter 15: Appendix

Study Objectives of Global Metaverse In Healthcare Market:

Reasons for Buy Metaverse In Healthcare Market Report:

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We publish market research reports & business insights produced by highly qualified and experienced industry analysts. Our research reports are available in a wide range of industry verticals including aviation, food & beverage, healthcare, ICT, Construction, Chemicals and lot more. Brand Essence Market Research report will be best fit for senior executives, business development managers, marketing managers, consultants, CEOs, CIOs, COOs, and Directors, governments, agencies, organizations and Ph.D. Students.

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US Metaverse In Healthcare Market Size By Technology (Artificial Intelligence, Virtual Reality, Augmented Reality, Mixed Reality) Forecast by 2028 ...

It's a rare cybersecurity product these days that doesn't claim to have some form of AI capability. But exactly what benefits does AI deliver? And is there a risk of an arms race as threat actors also turn to the technology?

We spoke to Corey Nachreiner, CSO at WatchGuard Technologies, to find out more about the role of AI in cybersecurity.

BN: What role does AI play in cybersecurity? What are some key use cases?

CN: Artificial intelligence plays an increasingly important role in cybersecurity. A recentPulse Survey shows that 68 percent of senior executives say they are using cybersecurity tools that use AI technologies, and among those who are not yet using AI, 67 percent are willing to consider it. The survey also discusses the main areas of cybersecurity that benefit from AI. These include network security, identity and access management, behavioral analytics (accidental/malicious internal threat detection), automated response and endpoint detection and response. For example, leveraging AI can help reduce zero-day malware by automating the discovery of threats without the need to wait for human driven signatures. Additionally, security teams can rely on machine learning to review vast quantities of data in order to detect malicious behavior more quickly.

BN What benefits do security teams get from using AI?

CN: AI offers security teams many benefits. These include increased threat detection speed, predictive capabilities, error reduction, behavioral analytics and more. AI enables a system to process and interpret information more quickly and accurately and, in turn, use and adapt that knowledge. It has substantially improved information management processes and allowed companies to gain time -- a critical component of the threat detection and remediation process. Additionally, today's ML/AI is good at automating basic procedural security tasks. For instance, AI can process noisy security alerts, removing the obvious false positives, or events that may not be serious, and leave only the important things that humans need to validate. Put a different way, it helps separate the wheat from the chaff in the deluge of security alerts, so human analyst can focus on whats important.

BN: How are threat actors utilizing AI to step up attacks and evade detection?

CN: Threat actors are using AI in many ways. For example, attackers use it to automate the discovery and learning about targets. When ML is applied to social networks, it can help identify the most prolific users with the most reach, etc., and it can then help automate learning what those individual users care about. This type of automated investigation of public profiles can allow attackers to use AI to craft messages that will more likely appeal to that target. In short, AI can automate the research into human targets that was traditionally done manually, enabling hackers to quickly collect enough information about the targets to deliver very specific phishing messages.

In fact, recent research on this subject presented at Black Hat demonstrated that a typical, widespread phishing attempt will see about a five percent success rate. Layer on machine learning that uses knowledge about the targets to make the phishing attempts more accurate and believable, and hackers will see about a 30 percent success rate. This is nearly as much as they see in a highly specified, targeted spear-phishing attempt.

BN: What does the AI tug-of-war between attackers and defenders look like?

CN: With AI/ML being used more and more by both the good guys and bad guys, its become a true cat and mouse game. As quickly as a defender finds a flaw, an attacker exploits it. And with ML, this happens at line speed. But there is work being done to address this. For example, at DEFCON 24 DARPA created the Cyber Grand Challenge, which pitted machine versus machine in order to develop automatic defense systems that can discover, prove, and correct software flaws in real-time. But this tug-of-war will likely continue as both attackers and defenders become more and more sophisticated and able to leverage the power of AI, and it will become a machine-to-machine battle with humans just contributing to help form the best models.

BN: What can organizations do to stay one step ahead of attackers?

CN: Normal users can't really help to minimize AI/ML used in an attack, they can only try to avoid the attacks themselves, which may still use social lures. The first place to start for companies is security awareness training. Teach employees how to recognize phishing and spear-phishing attempts. Understanding the problem is a big step in addressing it. Additionally, employ threat intelligence that sinkholes bad links, so they get quarantined and don't cause harm, even if they are clicked on. While this tug-of-war will likely go on indefinitely, we can continue to take steps to help the good side gain a little more muscle.

BN: What will the future of AI look like in the cybersecurity landscape?

CN: I would refer to the Cyber Grand Challenge for that future outlook. In that challenge, the attacks and defense were entirely machine to machine. Humans were not involved in the step-by-step tactics. The human security experts only played a role in helping design the models and strategies the AIs used to stay ahead once the fight started. In the future, I see machines with abilities to adjust their defense on the fly to fend of new attacks, but cybersecurity will involve a bigger data science component for defenders to help build AI/ML models so that their AIs stay one step ahead of those of the attackers.

Photo credit: jijomathaidesigners/Shutterstock

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The artificial intelligence tug-of-war in the world of cybersecurity [Q&A] - BetaNews

Researchers in the Oregon State University College of Engineering have harnessed the power of artificial intelligence to help protect bees from pesticides.

Cory Simon, Assistant Professor of chemical engineering, and Xiaoli Fern, Associate Professor of computer science, led the project, which involved training a machine learning model to predict whether any proposed new herbicide, fungicide, or insecticide would be toxic to honey bees based on the compounds molecular structure.

The findings, featured on the cover of The Journal of Chemical Physics in a special issue, Chemical Design by Artificial Intelligence, are important because many fruit, nut, vegetable, and seed crops rely on bee pollination.

Without bees to transfer the pollen needed for reproduction, almost 100 commercial crops in the U.S. would vanish. Bees global economic impact is annually estimated to exceed $100 billion.

Pesticides are widely used in agriculture, which increase crop yield and provide food security, but pesticides can harm off-target species like bees, Simon says. And since insects, weeds, etc. eventually evolve resistance, new pesticides must continually be developed, ones that dont harm bees.

Graduate students Ping Yang and Adrian Henle used honey bee toxicity data from pesticide exposure experiments, involving nearly 400 different pesticide molecules, to train an algorithm to predict if a new pesticide molecule would be toxic to honey bees.

The model represents pesticide molecules by the set of random walks on their molecular graphs, Yang says.

For more, continue reading at ScienceDaily.com.

ScienceDaily features breaking news about the latest discoveries in science, health, the environment, technology, and more, from leading universities, scientific journals, and research organizations. See all author stories here.

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Artificial Intelligence Tasked To Help Protect Bees From Certain Pesticides - Growing Produce

OTTAWA, Ontario & PARIS--(BUSINESS WIRE)--Winner of the Best A.I. Product at the recent TA Awards, TC Market Buzz helps modern investors & traders tackle infobesity while improving brokerage platforms' return on news investment.. The cutting-edge technology employs proprietary artificial intelligence (AI) in particular natural language processing (NLP) trained by market analysts to crunch incredibly large amounts of content into simple actionable insights. Its disruptive iconic interface, designed for the mobile consumer, leverages beautifully simple visualizations to convey whats happening in the markets.

Key specs:

TC Market Buzz declutters the digital news experience making it easier to identify and act on trade opportunities for the large number of retail investors accessing their platform once a week or less. TC Market Buzz help investors Read less, know more thanks to concise analytics such as:

The recent addition of French and Chinese reading skills to Trading Centrals AI and NLP engine adds tens of thousands of articles from leading content sources to crunch in order to derive ever more powerful analytics. Market Buzz users are seamlessly provided with deeper insights into global stocks buzz score, sentiment score and trending topics.

"At Webull, we believe in delivering reliable, actionable research to our investors within an interface they enjoy using", says CEO Anthony Denier. "Trading Central's news and sentiment APIs provided the flexibility we needed to integrate layered insights and education throughout our platform."

Providing traders with actionable insight is incredibly important to us," says Olly Stevens, Product Director at StoneX Retail. Our clients want to know about the hottest conversations in the market and fact-check those stories from the most reputable sources. Combining Refinitiv news products with Trading Central news & sentiment analytics provide a unique vantage point into the performance of an instrument."

"We're passionate about providing high-quality, actionable insights across the full spectrum of investors, from those just getting started to the active trader," says Vincent Sangiovanni, Chief Executive Officer at Money.Net. "That's why we integrated Market Buzz, alongside Technical Insight and Strategy Builder within our Scout platform. The combination of Trading Central's award-winning research, robust AI and NLP capabilities, provides our investors a holistic view of a security, while their flexible, intuitive interfaces helped us deliver insight in a seamless fashion."

Trading Central has been supporting investment decisions through the world's most admired brokerage and wealth tech brands since 1999. Learn about our award-winning, embeddable research solutions: http://www.tradingcentral.com

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Actionable news insights surfaced by Trading Central Artificial Intelligence - Business Wire

The new AI developed an "intuitive physics", just as human babies do. Image: Olga Belyaevskaya/Shutterstock.com

From driverless vehicles to weapons systems, artificial intelligence (AI) models are being trusted with an awful lot of responsibility these days, so youd like to think the technology has some idea of whats going on. Fortunately, we can all now rest assured thanks to the whizzes at DeepMind, who have created the first ever AI with a grasp of physics comparable to that of a human baby.

Writing in the journal Nature Human Behaviour, the researchers explain how we develop intuitive physics within the first months of life, quickly coming to understand certain fundamental laws governing the material world. For example, infants tend to comprehend the concepts of permanence whereby objects dont simply vanish plus solidity and continuity, referring to the inability of objects to pass through one another or to suddenly alter their trajectory through time and space.

However, the authors go on to state that current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to even very young children. To help the bots catch up, the team turned to the field of developmental psychology to develop an AI that is capable of learning in the same way as a baby.

For instance, by the age of about three months, human infants are capable of showing surprise when an object disobeys one of the three pillars of our intuitive physics. This ability is known as the violation-of-expectation (VoE) paradigm, and provides the inspiration behind the new AI.

Called PLATO standing for Physics Learning through Auto-encoding and Tracking Objects the deep-learning system was trained on a series of videos of balls moving through space and interacting with one another. The video dataset was specifically designed to represent the concepts of permanence, solidity and continuity, as well as two extra concepts known as unchangeableness and inertia. These relate to the fact that objects do not suddenly alter their basic characteristics or disobey the laws that govern speed and direction.

When PLATO was later shown videos of scenarios that contravened any of these five tenets, it successfully reacted with a VoE signal. After training PLATO on videos of simple physical interactions, we found that PLATO passed the tests in our Physical Concepts dataset, explained study author Luis Piloto in a statement.

By varying the amount of training data used by PLATO, we found that PLATO could learn our physical concepts with as little as 28 hours of visual experience.

Impressively, the AI could even identify transgressions of the laws of physics when looking at a separate video dataset featuring objects that it had never seen before. PLATO passed, without any re-training, despite being tested on entirely new stimuli, says Piloto.

This breakthrough certainly bodes well for the future of AI, since, As Piloto points out, if were to deploy safe and helpful systems in the real world, we want these models to share our intuitive sense of physics.

Obviously theres still a way to go, but baby smarts aint a bad start.

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Artificial Intelligence Has A Baby's Understanding Of Physics (Which Is Impressive) - IFLScience