ArchIntel

ArchIntel, a leading provider of concise actionable open source market and competitive intelligence (CI) reports to business leaders across the federal sector, has released Competitive Artificial Intelligence: The Crossover Point, the platforms latest white paper discussing the influence that artificial intelligence (AI) is having on the future of the CI landscape.

The full white paper is available for a free download on ArchIntel.com.

The Crossover Point showcases the insights and highlights from a handful of senior executives in the field of competitive intelligence who acted as expert panelists during ArchIntel Events recent Artificial Intelligence in Competitive Intelligence Forum.

ArchIntels first event explored the competitive landscape as emerging technology continues to evolve and influence the federal sector while the panelists explained how businesses can maintain a competitive advantage through the integration of emerging technologies into their organizations.

August Jackson, senior director of Market and Competitive Intelligence with Deltek, served as a speaker and moderator for an expert panel featuring Dr. Fred Hoffman of Mercyhurst University, Arik Johnson of Aurora Worldwide Development and Suki Fuller of Competitive Intelligence Fellows.

We need to find our collective professional voice for us to speak to the developer community to ensure that AI is an enabling tool for competitive intelligence. said Jackson during ArchIntels recent forum. This event is one of the first steps we need to find that voice.

Download your free copy of ArchIntels latest white paper, The Crossover Point to learn the biggest takeaways and best practices of how CI professionals are maintaining a competitive advantage in their field while working to integrate AI and other emerging technologies to push CI into the future.

In case you missed ArchIntels Artificial Intelligence in Competitive Intelligence Forum, you can rewatch the full event by registering on ArchIntel Events.

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ArchIntel Releases 'Competitive Artificial Intelligence: The Crossover Point' White Paper - GovConWire

DUBLIN, Jan. 29, 2021 /PRNewswire/ -- The "Artificial Intelligence in Epidemiology Market by AI Type, Infrastructure, Deployment Model, and Services 2021 - 2026" report has been added to ResearchAndMarkets.com's offering.

This global AI epidemiology and public health market report provides a comprehensive evaluation of the positive impact that AI technology will produce with respect to healthcare informatics, and public healthcare management, and epidemiology analysis and response. The report assesses the macro factors affecting the market and the resulting need for hardware and software technology used in the public healthcare and epidemiology informatics.

The macro factors include the growth drivers and challenges of the market along with the potential application and usage areas in public health industry verticals. The report also provides the anticipated market value of AI in the public health and epidemiology informatics market globally and regionally. This includes core technology and AI-specific technologies. Market forecasts cover the period of 2021 - 2026.

The Center for Disease Control and Prevention sees epidemiology as the study and analysis of the distribution, patterns and determinants of health and disease conditions in defined populations. It is a cornerstone of public health and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for preventive healthcare.

This includes identification of the factors involved with diseases transmitted by food and water, acquired during travel or recreational activities, bloodborne and sexually transmitted diseases, and nosocomial infections such as hospital-acquired illnesses. Epidemiology is also concerned with the identification of trends and predictive capabilities to prevent diseases.

Sources of disease data include medical claims data (commercial claims, Medicare), electronic healthcare records (EHR) including medical treatment facilities and pharmacies, death registries and socioeconomic data. It is important to note that some data is highly structured whereas other data elements are highly unstructured, such as data gathered from social media and Web scraping.

Artificial Intelligence (AI) will increasingly be relied upon to improve the efficiency and effectiveness of transforming data correlation to meaningful insights and information. For example, machine learning has been used to gather Web search and location data as a means of identifying potential unsafe areas, such as restaurants involved in food-borne illnesses.

The combination of data aggregation from multiple sources with machine learning and advanced analytics will greatly improve the efficacy of epidemiology predictive models. For example, machine learning allows epidemiologists to evaluate as many variables as desired without increasing statistical error, a problem that often arises with multiple testing bias, which is a condition that occurs when each additional test run on the data increases the possibility for error against a hypothetical target result.

Another example of AI in epidemiology is the use of natural language processing to capture clinical notes for preservation in EHR databases. As part of data capture and identification of most important information, AI will also be used to validate key terms to identify conditions, diagnoses and exposures that are otherwise difficult to capture/identify through traditional data source mining. This will be used for data discovery and validation as well as knowledge representation.

An extremely important and high growth area for AI in epidemiology is drug discovery, safety, and risk analysis, which we anticipate will be a $699 million global market by 2026. Other high opportunity areas for AI are disease and syndromic surveillance, infection prediction and forecasting, monitoring population and incidence of disease, and use of AI in Immunization Information Systems (IIS). In addition to mapping vaccinations to disease incidence, the IIS will leverage AI to identify the impact of public sentiment analysis and for public safety services such as mass notification.

Select Report Findings:

Report Benefits:

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Defining Public Health Informatics

2.1.1 Epidemiology in PHI

2.1.1.1 Viral Disease Epidemiology

2.1.2 AI in Epidemiology and Public Health Informatics

2.1.3 Medical Informatics vs. Health Informatics

2.2 Social Technical Informatics Technology Stack

2.3 Epidemiology and Public Health Informatics Process

2.3.1 Collection of Data

2.3.2 Defining Study Model

2.3.3 Data Storage

2.3.4 Data Quality Assurance

2.3.5 Data Analysis

2.4 Computational Epidemiology

2.5 Infectious vs. Non-infectious Diseases

2.6 COVID 19 Pandemic and Public Health

2.7 Growth Driver Analysis

2.8 Market Challenge Analysis

2.9 Public Health Policy and Outcomes

2.9.1 Public Health Data Exchange

2.10 Regulatory Analysis

2.10.1 GDPR

2.10.2 HIPAA

2.10.3 ISO Standards

2.10.4 HITECH

2.10.5 ETSI

2.11 Value Chain Analysis

2.11.1 Data Warehouse

2.11.2 AI Companies

2.11.3 Software Development

2.11.4 Semiconductor Providers

2.11.5 Infrastructure and Connectivity Providers

2.11.6 Analytics Providers

2.11.7 Healthcare Service Providers

2.11.8 Regulatory Bodies

3.0 Technology and Application Analysis

3.1 Hardware Technology Analysis

3.1.1 AI Processors and Chipsets

3.1.1.1 Microprocessor Unit (MPU)

3.1.1.2 Tensor Processing Unit (TPU)

3.1.1.3 Graphics Processing Unit (GPU)

3.1.1.4 Field-Programmable Gate Array (FPGA)

3.1.1.5 Application Specific Integrated Circuits (ASIC)

3.1.1.6 Intelligent Processing Unit (IPU)

3.1.2 Memory Chip

3.1.3 Network Adaptor

3.1.4 3D Sensors

3.2 Software Technology Analysis

3.2.1 AI Solution: Cloud vs. On-premise Software

3.2.2 AI Platform Framework and APIs

3.3 AI Technology Analysis

3.3.1 Machine Learning and Deep Learning

3.3.2 Natural Language Processing (NLP)

3.3.3 Computer Vision: Image and Voice Processing

3.3.4 Neural Network Processing

3.3.5 Context Aware Processing

3.4 Enabling Technology Analysis

3.4.1 Electronic Health Records

3.4.2 Social Media Analytics

3.4.3 Traffic Surveillance Systems

3.4.4 Digital Health Passports

3.4.5 Computer-Based Simulation Models

3.4.6 Protective Gear and Equipment

3.4.7 Telemedicine Solutions

3.4.8 Semantics-Based Health Information System

3.4.9 Health Information Technology

3.4.10 Electronic Data Capture

3.4.11 Clinical Data Management Systems

3.4.12 Patient Data Management System

3.4.13 Laboratory Information Management Systems

3.4.14 Internet of Healthcare Technology

3.5 Application Analysis

3.5.1 Disease and Syndromic Surveillance

3.5.2 Infection Prediction and Forecasting

3.5.3 Immunization Information Systems

3.5.4 Public Sentiment Analysis

3.5.5 Environmental Impact Analysis

3.5.6 Drug Discovery, Safety, and Risk Analysis

3.5.7 Monitoring Population and Incidence

3.5.8 Knowledge Representation and Mass Notification

3.6 Industry Use Case Analysis

3.6.1 Government and State Agencies

3.6.2 MassHealth ACOS and MCOS

3.6.3 Research Labs

3.6.4 Pharmaceuticals Company

3.6.5 Hospital, Specialty Clinics, and Healthcare Providers

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Global Artificial Intelligence in Epidemiology Markets, 2021-2026: Vaccine R&D will be a Substantial Beneficiary - Growing Importance in Light of the...

Expectations are high that artificial intelligence will be a game changer for the military and it is, in fact, one of the Defense Department's top priorities.

"We're in the very early days of a very long history of continued very rapid development in the AI field," said William Scherlis, director of the Information Innovation Office at the Defense Advanced Research Projects Agency. He spoke yesterday at a virtual panel discussion at the Defense One Genius Machines 2021 summit.

There are a lot of moving parts to AI that must come together to make it all work for the warfighter, he said.

Components include, machine learning, symbolic reasoning, statistical learning, knowledge representation, search and planning, data, cloud infrastructure, algorithms and computing, he said.

"If you want to do strategy planning, then you're gonna have a mashup of machine learning with, maybe, game theory and a few other elements. So when we talk about AI, sometimes people are referring to just machine-learning algorithms and data and training. But in the systems engineering context, we're really talking about how to build systems that, that have elements of AI capability embedded within them," he said.

Scherlis discussed the history of AI, back to the 1940s and noted that there were three waves of development.

The first wave involved symbolic AI, which has explicit rules, such as if it's raining, then bring an umbrella, he said. Commercial income tax programs operate this way, using rules, logic and reasoning to reach a conclusion.

The second wave involved neural nets, which Scherlis refers to as statistical AI. Neural nets attempt to replicate higher-order human thinking skills, such as problem solving.

All AI relies on having good data. But although data is certainly important, the real game-changer for AI will be the third wave where symbolic is meshed with statistical to get the best of both worlds, Scherlis predicted.

"This is a wide open research area, but there's a lot of good work in this area and I think it's very promising," he said, referring to third wave research.

This third wave will need to focus on how AI systems interact with humans in a productive and symbiotic way, he said.

Warriors will have to understand what it's like to have an AI as a trusted team member, he said.

Currently, AI isn't yet ready for prime time, he said. It's still fragile, opaque, biased and not robust enough, which means it does not yet have trustworthiness.

"At DARPA, we have another number of programs that are, that are addressing these challenges," he added.

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Artificial Intelligence Is a Work in Progress, Official Says - Department of Defense

The field of artificial intelligence is moving at a staggering clip, with breakthroughs emerging in labs across MIT. Through the Undergraduate Research Opportunities Program (UROP), undergraduates get to join in. In two years, the MIT Quest for Intelligence has placed 329 students in research projects aimed at pushing the frontiers of computing and artificial intelligence, and using these tools to revolutionize how we study the brain, diagnose and treat disease, and search for new materials with mind-boggling properties.

Rafael Gomez-Bombarelli, an assistant professor in the MIT Department of Materials Science andEngineering, has enlisted several Quest-funded undergraduates in his mission to discover new molecules and materials with the help of AI. They bring a blue-sky open mind and a lot of energy, he says. Through the Quest, we had the chance to connect with students from other majors who probably wouldnt have thought to reach out.

Some students stay in a lab for just one semester. Others never leave. Nick Bonaker is now in his third year working with Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science, to develop assistive technology tools for people with severe motor impairments.

Nick has continually impressed me and our collaborators by picking up tools and ideas so quickly, she says. I particularly appreciate his focus on engaging so carefully and thoughtfully with the needs of the motor-impaired community. He has very carefully incorporated feedback from motor-impaired users, our charity collaborators, and other academics.

This fall, MIT Quest celebrated two years of sponsoring UROP students. We highlight four of our favorite projects from last semester below.

Squeezing more energy from the sun

The price of solar energy is dropping as technology for converting sunlight into energy steadily improves. Solar cells are now close to hitting 50 percent efficiency in lab experiments, but theres no reason to stop there, says Sean Mann, a sophomore majoring in computer science.

In a UROP project with Giuseppe Romano, a researcher at MITs Institute for Soldier Nanotechnologies, Mann is developing a solar cell simulator that would allow deep learning algorithms to systematically find better solar cell designs. Efficiency gains in the past have been made by evaluating new materials and geometries with hundreds of variables. Traditional ways of exploring new designs is expensive, because simulations only measure the efficiency of that one design, says Mann. It doesnt tell you how to improve it, which means you need either expert knowledge or lots more experiments to improve on it.

The goal of Manns project is to develop a so-called differentiable solar cell simulator that computes the efficiency of a cell and describes how tweaking certain parameters will improve efficiency. Armed with this information, AI can predict which adjustments from among a dizzying array of combinations will boost cell performance the most. Coupling this simulator with a neural network designed to maximize cell efficiency will eventually lead to some really good designs, he says.

Mann is currently building an interface between AI models and traditional simulators. The biggest challenge so far, he says, has been debugging the simulator, which solves differential equations. He pulled several all-nighters double-checking his equations and code until he found the bug: an array of numbers off by one, skewing his results. With that obstacle down, Mann is now looking for algorithms to help the solver converge more quickly, a crucial step toward efficient optimization.

Teaching neural networks physics to identify stress fractures

Sensors deep within the modern jet engine sound an alarm when something goes wrong. But diagnosing the precise failure is often impossible without tinkering with the engine itself. To get a clearer picture faster, engineers are experimenting with physics-informed deep learning algorithms to translate these sensor distress signals.

It would be way easier to find the part that has something wrong with it, rather than take the whole engine apart, says Julia Gaubatz, a senior majoring in aerospace engineering. It could really save people time and money in industry.

Gaubatz spent the fall programming physical constraints into a deep learning model in a UROP project with Raul Radovitzky, a professor in MITs Department of Aeronautics and Astronautics, graduate student Grgoire Chomette, and third-year student Parker Mayhew. Their goal is to analyze the high-frequency signals coming from, say, a jet engine shaft, to pinpoint where a part may be stressed and about to crack. They hope to identify the particular points of failure by training neural networks on numerical simulations of how materials break to understand the underlying physics.

Working from her off-campus apartment in Cambridge, Massachusetts, Gaubatz built a smaller, simplified version of their physics-informed model to make sure their assumptions were correct. Its easier to look at the weights the neural network is coming up with to understand its predictions, she says. Its like a test to check that the model is doing what it should according to theory.

She picked the project to try applying what she had learned in a course on machine learning to solid mechanics, which focuses on how materials deform and break under force. Engineers are just starting to incorporate deep learning into the field, she says, and its exciting to see how a new mathematical concept may change how we do things.

Training an AI to reason its way through visual problems

An artificial intelligence model that can play chess at superhuman levels may be hopeless at Sudoku. Humans, by contrast, pick up new games easily by adapting old knowledge to new environments. To give AI more of this flexibility, researchers created the ARC visual-reasoning dataset to motivate the field to create new techniques for solving problems involving abstraction and reasoning.

If an AI does well on the test, it signals a more human-like intelligence, says first-year student Subhash Kantamneni, who joined a UROP project this fall in the lab of Department of Brain and Cognitive Sciences (BSC) Professor Tomaso Poggio, which is part of the Center for Minds, Brains and Machines.

Poggios lab hopes to crack the ARC challenge by merging deep learning and automated program-writing to train an agent to solve ARCs 400 tasks by writing its own programs. Much of their work takes place in DreamCoder, a tool developed at MIT that learns new concepts while solving specialized tasks. Using DreamCoder, the lab has so far solved 70 ARC tasks, and Kantamneni this fall worked with master of engineering student Simon Alford to tackle the rest.

To try and solve ARCs 20 or so pattern-completion tasks, Kantamneni created a script to generate similar examples to train the deep learning model. He also wrote several mini programs, or primitives, to solve a separate class of tasks that involve performing logical operations on pixels. With the help of these new primitives, he says, DreamCoder learned to combine the old and new programs to solve ARCs 10 or so pixelwise tasks.

The coding and debugging was hard work, he says, but the other lab members made him feel at home and appreciated. I dont think they even knew I was a freshman, he says. They listened to what I had to say and valued my input.

Putting language comprehension under a microscope

Language is more than a system of symbols: It allows us to express concepts and ideas, think and reason, and communicate and coordinate with others. To understand how the brain does it, psychologists have developed methods for tracking how quickly people grasp what they read and hear. Longer reading times can indicate when a word has been improperly used, offering insight into how the brain incrementally finds meaning in a string of words.

In a UROP project this fall in Roger Levys lab in BCS, sophomore Pranali Vani ran a set of sentence-processing experiments online that were developed by an earlier UROP student. In each sentence, one word is placed in such a way that it creates an impression of ambiguity or implausibility. The weirder the sentence, the longer it takes a human subject to decipher its meaning. For example, placing a verb like tripped at the end of a sentence, as in The woman brought the sandwich from the kitchen tripped, tends to throw off native English speakers. Though grammatically correct, the wording implies that bringing rather than tripping is the main action of the sentence, creating confusion for the reader.

In three sets of experiments, Vani found that the biggest slowdowns came when the verb was positioned in a way that sounded ungrammatical. Vani and her advisor, Ethan Wilcox, a PhD student at Harvard University, got similar results when they ran the experiments on a deep learning model.

The model was surprised when the grammatical interpretation is unlikely, says Wilcox. Though the model isnt explicitly trained on English grammar, he says, the results suggest that a neural network trained on reams of text effectively learns the rules anyway.

Vani says she enjoyed learning how to program in R and shell scripts. She also gained an appreciation for the persistence needed to conduct original research. It takes a long time, she says. Theres a lot of thought that goes into each detail and each decision made during the course of an experiment.

Funding for MIT Quest UROP projects this fall was provided, in part, by the MIT-IBM Watson AI Lab.

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An intro to the fast-paced world of artificial intelligence - MIT News

Google Deepmind head Demis Hassabis speaks during a press conference ahead of the Google DeepMind Challenge Match in Seoul on March 8, 2016.

Jung Yeon-Je | AFP |Getty Images | Getty Images

LONDON Artificial intelligence researchers don't like it when you ask them to name the top AI labs in the world, possibly because it's so hard to answer.

There are some obvious contenders when it comes to commercial AI labs. U.S. Big Tech Google, Facebook, Amazon, Apple and Microsoft have all set up dedicated AI labs over the last decade. There's also DeepMind, which is owned by Google parent company Alphabet, and OpenAI, which counts Elon Musk as a founding investor.

"Wow, I hate this question," Mark Riedl, associate professor at the Georgia Tech School of Interactive Computing, told CNBC when asked to pick his standouts.

"Reputationally, there is a good argument to say DeepMind, OpenAI, and FAIR (Facebook AI research]) are the top three," Riedl said.

AI investor Nathan Benaich, a partner at Air Street Capital, agreed. Google Brain and Microsoft could potentially be included in the top ranks, Benaich said, before adding that he believed Amazon and IBM weren't quite in the same league when it comes to AI research output and impact.

Another AI expert, who asked to remain anonymous because he didn't have approval from his company to speak publicly, told CNBC that DeepMind, OpenAI and FAIR were probably the top three pure AI research labs in terms of known funding, while IBM pushes out more patents. "The unknown question is the Baidus and Tencents of the world," he said in reference to the Chinese tech giants.

Alphabet gives DeepMind hundreds of millions of dollars a year to carry out its work, while Microsoft invested $1 billion in OpenAI on top of the $1 billion that the founding investors contributed. FAIR's funding is less clear because Facebook doesn't break it down.

One way to measure the impact of an AI lab is to look at how many academic papers it publishes at the two biggest AI conferences: NeurIPS and ICML.

In 2020, Google had 178 papers accepted and published at NeurIPS, while Microsft had 95, DeepMind had 59, Facebook had 58 and IBM had 38. Amazon had less than 30.

For the same year at ICML, Google had 114 papers accepted and published, while DeepMind had 51, Microsoft had 49, Facebook had 34, IBM had 19, and Amazon had 18.

AI has been hailed as a technology that has the potential to bring about a new industrial revolution and dramatically change the world we live in. But, for now at least, it remains relatively nascent and "narrow" in its abilities an AI that can play chess to a superhuman level doesn't know how to make an omelet, for example.

DeepMind, OpenAI, and FAIR are widely perceived as the top three labs partly due to "strong PR games," Riedl said.

He believes that Microsoft, which carries out much of its AI work through Microsoft Research, could legitimately be included in the top ranks. "For whatever reason they fly below the radar sometimes," Riedl said. "Salesforce, Amazon, IBM all have some really strong pockets of research but, again, fail to make big splashes."

Riedl said he's "not sure that you couldn't swap any group of researchers from any of these companies with any other and make any difference."

Neil Lawrence, the former director of machine learning at Amazon Cambridge, told CNBC that Amazon doesn't have a large, centralized AI research lab because it's more focused on bringing technology to customers.

"I would argue they've done that very successfully," said Lawrence, who is now a professor of machine learning at the University of Cambridge. "But if you look at (academic) publications as a measure then they don't rank."

Lawrence said that Microsoft Research is personally the research lab that he admires the most but "Amazon really ranks up there in deploying AI ... despite not having a (big) research lab."

He added: "DeepMind, OpenAI and FAIR have definitely dominated the headlines. But it's interesting how much of the research they are publishing might traditionally have been done in universities."

Although this ranking didn't focus on university AI labs, experts called out Stanford, MIT, UC Berkley, and Carnegie Mellon as being strong in the U.S., as well as Cambridge, University College London and Imperial College London in the U.K.

Read more:
Artificial intelligence researchers rank the top A.I. labs worldwide - CNBC

It could be argued artificial intelligence (AI) is already the indispensable tool of the 21st century. From helping doctors diagnose and treat patients to rapidly advancing new drug discoveries, its our trusted partner in so many ways.

Now it has found its way into the once exclusively-human domain of love and relationships. With AI-systems as matchmakers, in the coming decades it may become common to date a personalised avatar.

This was explored in the 2014 movie Her, in which a writer living in near-future Los Angeles develops affection for an AI system. The sci-fi film won an Academy Award for depicting what seemed like a highly unconventional love story.

In reality, weve already started down this road.

The online dating industrty is worth more than US$4 billion and there are a growing number of players in this market. Dominating it is the Match Group, which owns OkCupid, Match, Tinder and 45 other dating-related businesses.

Match and its competitors have accumulated a rich trove of personal data, which AI can analyse to predict how we choose partners.

The industry is majorly embracing AI. For instance, Match has an AI-enabled chatbot named Lara who guides people through the process of romance, offering suggestions based on up to 50 personal factors.

Tinder co-founder and CEO Sean Rad outlines his vision of AI being a simplifier: a smart filter that serves up what it knows a person is interested in.

Dating website eHarmony has used AI that analyses peoples chat and sends suggestions about how to make the next move. Happn uses AI to rank profiles and show those it predicts a user might prefer.

Loveflutters AI takes the guesswork out of moving the relationship along, such as by suggesting a restaurant both parties could visit. And Badoo uses facial recognition to suggest a partner that may look like a celebrity crush.

Dating platforms are using AI to analyse all the finer details. From the results, they can identify a greater number of potential matches for a user.

They could also potentially examine a persons public posts on social media websites such as Facebook, Twitter and Instagram to get a sense of their attitudes and interests.

This would circumvent bias in how people represent themselves on matchmaking questionnaires. Research has shown inaccuracies in self-reported attributes are the main reason online dating isnt successful.

While the sheer amount of data on the web is too much for a person to process, its all grist to the mill for a smart matchmaking AI.

Read more: Looking for love on a dating app? You might be falling for a ghost

As more user data is generated on the internet (especially on social media), AI will be able to make increasingly accurate predictions. Big players such as Match.com would be well-placed for this as they already have access to large pools of data.

And where there is AI there will often be its technological sibling, virtual reality (VR). As both evolve simultaneously, well likely see versions of VR in which would-be daters can practice in simulated environments to avoid slipping up on a real date.

This isnt a far stretch considering virtual girlfriends, which are supposed to help people practice dating, have already existed for some years and are maturing as a technology. A growing number of offerings point to a significant degree of interest in them.

With enough user data, future AI could eventually create a fully-customised partner for you in virtual reality one that checks all your boxes. Controversially, the next step would be to experience an avatar as a physical entity.

It could inhabit a life-like android and become a combined interactive companion and sex partner. Such advanced androids dont exist yet, but they could one day.

Read more: Robots with benefits: how sexbots are marketed as companions

Proponents of companion robots argue this technology helps meet a legitimate need for more intimacy across society especially for the elderly, widowed and people with disabilities.

Meanwhile, critics warn of the inherent risks of objectification, racism and dehumanisation particularly of women, but also men.

Another problematic consequence may be rising numbers of socially reclusive people who substitute technology for real human interaction. In Japan, this phenomenon (called hikikomori) is quite prevalent.

At the same time, Japan has also experienced a severe decline in birth rates for decades. The National Institute of Population and Social Security Research predicts the population will fall from 127 million to about 88 million by 2065.

Concerned by the declining birth rate, the Japanese government last month announced it would pour two billion yen (about A$25,000,000) into an AI-based matchmaking system.

The debate on digital and robotic love is highly polarised, much like most major debates in the history of technology. Usually, consensus is reached somewhere in the middle.

But in this debate, it seems the technology is advancing faster than we are approaching a consensus.

Generally, the most constructive relationship a person can have with technology is one in which the person is in control, and the technology helps enhance their experiences. For technology to be in control is dehumanising.

Humans have leveraged new technologies for millenia. Just as we learned how to use fire without burning down cities, so too we will have to learn the risks and rewards accompanying future tech.

Here is the original post:
Love in the time of algorithms: would you let your artificial intelligence choose your partner? - The Conversation AU

Two scientific papers impressed me this week, both in the field of artificial intelligence (AI). The first is by researchers led by Sam Kriegman at the University of Vermont, who present a method for designing biological machines from the ground up. They emphasize the potential good this might do by allowing the creation of living machines that safely deliver drugs inside the human body or assist with cleaning up the environment.

The other paper is a collaboration of the Max Planck Institute in Germany with the Autonomous University of Madrid, Spain. Led by Manuel Alfonseca, the authors claim that based on computability theory, a superintelligent AI cannot be contained, and thus poses a threat to all of us.

Kriegmans team calls their biological machines xenobots, because the cells used to build them derive from the African clawed frog (Xenopus laevis). The xenobots consist of 500 to 1,000 living cells that can move in various directions and, when joined together, can push small objects. The researchers programmed a computer to automatically design simulated biological machines, then built the best designs by combining different biological tissues. The program used an AI evolutionary algorithm to predict which xenobots would likely perform useful tasks. The noble idea behind this method is that reconfigurable biomachines could vastly improve human and environmental healthfor example, by cleaning microplastics from the ocean.

But such biomachines raise many ethical concerns. Although they bear little resemblance to organisms or even individual organs, they are clearly alive. For example, they have the ability to repair themselves. (Am I the only one who finds that a bit creepy?) What if they go rogue and interact with the environment in a different way than intended, possibly in harmful ways? Because they are life forms and not mechanical robots, I think it will be difficult to predict how they will interact with the environment. How would we control them? A kill switch? Theres potential for lots of good, but also for lots of danger.

The second paper considers the important question of whether we can, even in principle, control AI, especially a superintelligent AI. Kriegmans biological machines would not be expected to become superintelligent, but as Alfonseca and colleagues point out, there already are machines that perform advanced tasks independently, without programmers fully understanding how they learned it. Lets go one step further and imagine the AI connecting to the internet and absorbing all the knowledge it contains. How could a human control, let alone stop an entity that is, in the words of philosopher Nick Bostrom, smarter than the best human brains in practically every field.

One way to protect ourselves could be to wall the AI off from the internet. But that defeats the purpose of what the machine is designed to do. Alfonseca considers a different optionusing a containment algorithm to guard against the AI becoming a threat. Unfortunately, they conclude that this would be impossibleno single algorithm could determine whether a superintelligent AI might cause harm to the world.

Other possibilities have been considered. We could give all AIs an ethical and moral underpinninglike Isaac Asimovs famous three laws of robotics. But theres an even worse scenario: What if the superintelligent AI decides that our species is inherently dangerous, and that the best solution is to just stop us from doing more harm? Science fiction writers have long grappled with this problem, as in Jack Williamsons 1947 novelette With Folded Hands, in which AI humanoids relegate our species to sitting around with folded handsso we cant hurt anything.

Needless to say, these are difficult questions. Computer scientists, philosophers, and science fiction authors will have their hands full exploring themand lawmakers need to be ready to reactas AI continues to advance.

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Artificial Intelligence: Cure for What Ails Us, or Looming Threat to the World? - Air & Space Magazine

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Most long-term investors love passive income stocks. Therefore, today we introduce seven Dividend Aristocrats, or businesses that have increased the base dividend every year for the past 25 years. According to metrics from S&P Global (NYSE:SPGI), Since 1926, dividends have contributed to approximately one-third of total return while capital appreciations have contributed two-thirds. Therefore, both sustainable dividend income and capital appreciation potential are important to total return expectations. Over the past year, the S&P 500 Dividend Aristocrats Index has returned over 6%. By comparison, the Dow Jones Industrial Average (DJIA) has increased by 5%.InvestorPlace - Stock Market News, Stock Advice & Trading Tips Solid businesses with wide moats tend to be able to generate stable revenues and strong cash flows in most years, even in volatile times or recessions. In fact, many such firms end up gaining market share at the expense of weaker businesses that might simply fight to stay alive during economically tough times. Meanwhile, companies that consistently grow dividends are in effect saying that they are committed to sharing the success of the business with stockholders. With that information, here are seven Dividend Aristocrats that deserve your attention in 2021: 7 Airline Stocks Being Fueled by Vaccine News AbbVie (NYSE:ABBV) Albemarle (NYSE:ALB) Automatic Data Processing (NASDAQ:ADP) Chubb (NYSE:CB) Emerson Electric (NYSE:EMR) ProShares S&P 500 Dividend Aristocrats ETF (BACS:NOBL) Sysco (NYSE:SYY) Dividend Aristocrats: AbbVie (ABBV) Source: Piotr Swat / Shutterstock.com 52-week range: $62.55 $113.41 1-year price change: Up 23.82% Dividend yield: 4.71% Illinois-based biopharma group AbbVie is our first Dividend Aristocrat. It has numerous research and development (R&D) centers and manufacturing facilities globally. Several of its therapeutic areas include eye care, gastroenterology, immunology, neuroscience, oncology, rheumatology, virology, and womens health. In addition, its Allergan Aesthetics portfolio includes products, such as Botox Cosmetics, fillers, and implants. The last quarterly report showed non-GAAP adjusted net revenues of $12.882 billion, an increase of 4.1% year-over-year (YoY). Net earnings of $2.31 billion meant an increase of 22.5% YoY. Adjusted diluted EPS was $2.83, up 21% YoY. Cash and equivalents stood at $7.89 billion. CEO Richard A. Gonzalez cited, Results from key growth products including Skyrizi, Rinvoq and Ubrelvy continue to track ahead of our expectations, our aesthetics portfolio is demonstrating a strong V-shaped recovery, our hematologic-oncology franchise is delivering double-digit growth and were advancing numerous attractive late-stage pipeline programs. The company has in-demand therapies and products that contribute to revenue growth. AbbVies pipeline also deserves attention. Id regard any drop in price as an opportunity to buy the shares. Albemarle (ALB) Source: IgorGolovniov/Shutterstock.com 52-week range: $48.89 $187.25 1-year price change: Up 124.84% Dividend yield: 0.89% Charlotte, North Carolina-based Albemarle produces specialty chemicals used in a wide range of products manufactured by pharmaceutical companies, agricultural companies, water treatment companies, electronics products manufacturers, refineries, and others. In 2020, Albemarle caught investors attention as it is the industry leader in lithium, used to make electric vehicle (EV) batteries. Consumers love for EVs translated to a jump in the ALB share price. Investors believe the new administration in Washington will continue to provide tailwinds for the renewable energy sector. Q3 results announced in early November showed net sales of $747 million, down by 15% YoY. Net income was $98.3 million and decreased 36.6%. Adjusted diluted EPS of $1.09 showed a decline of 28.8% YoY. CEO Kent Masters said, We now expect to realize approximately $80 million of cost savings this year and to reach an annual savings rate of $120 million or more by the end of 2021. We expect these savings to represent a first wave of ongoing operational improvements that will reap notable benefits for the company. 8 Indian Stocks That Belong on Your International Radar ALB stocks forward P/E and P/S ratios are 48.39x and 6x, respectively. As a result of the recent run-up in price, the valuation metrics are overstretched. Potential investors could consider investing around $170. Automatic Data Processing (ADP) Source: Shutterstock 52-week range: $103.11 $182.32 1-year price change: Down 7.87% Dividend yield: 2.31% Roseland, New Jersey-based Automatic Data Processing provides cloud-based human capital management (HCM) solutions such as human resources (HR) payroll, tax, and benefits administration, as well as business outsourcing services. The company tends to generate steady, recurring revenue. However, 2020 has also meant challenges due to job losses stateside, which has meant revenue loss for the group. According to the most recent quarterly metrics, revenues came at $3.5 billion, down by 1% YoY. Adjusted net earnings of $605 million showed an increase of 4%. Adjusted diluted EPS was $1.41 and increased by 5%. CFO Kathleen Winters commented, Our first quarter results significantly exceeded our expectations across the board While we still expect to face headwinds over the course of the year, we will continue to look for ways to drive strong performance in both the near and long-term. Forward P/E and P/S ratios are 27.9x and 4.81x, respectively. Despite the recent decline in price, I believe the shares are still richly valued for the current environment. A potential decline would improve the margin of safety. Emerson Electric (EMR) Source: Shutterstock 52-week range: $37.75 $84.44 1-year price change: Up 6.29% Dividend yield: 2.44% St Louis, Missouri-based Emerson Electric is a technology and engineering company. The group focuses on Automation Solutions (manufacturing electrical components and providing services and training) and Commercial & Residential Solutions (covering heating, air conditioning, and refrigeration). FY20 Q4 metrics released in early November showed GAAP net sales of $4.6 billion, down 8% YoY. Net earnings were $723 million, up 1% YoY. Adjusted EPS came at $1.10, down 4%. Free cash flow for the quarter was $1.02 billion and increased 2%. CEO David N. Farr commented, Amidst all the challenges, we exceeded our second quarter reset financial forecast in sales, EBITDA, and cash flow We also continued to invest and took bold action to build on our innovation and technology footprint of the future, with three strategic acquisitions: American Governor, Open Systems International Inc. and Progea. 9 Beginner Stocks for First-Time Investors EMR stocks forward P/E and P/S ratios are 25.5x and 2.99x, respectively. Emerson Electrics automation division currently has significant exposure to the traditional energy (i.e., oil and gas) industry. However, it is also growing its alternative energy (i.e., clean fuels and renewables) businesses. Any decline below $80, especially toward $75, would offer a good entry point into the engineering group. Chubb (CB) Source: thodonal88 / Shutterstock.com 52-week range: $87.35 $167.74 1-year price change: Up 1.66% Dividend yield: 2% Chubb is one of the largest publicly traded property and casualty insurance companies worldwide. 2020 has meant challenges for the industry. The pandemic, hurricanes, flooding, flooding, and civil unrest have meant increased insurance claims. However, the companys operations stood the test of times. The most recent quarterly earnings showed revenue of $9.46 billion, up 4.6% YoY. Net income was $1.19 billion, an increase of 9.4%. Diluted EPS was $2.63, up by 10.5%. Operating cash flow was $3.5 billion. CEO Evan G. Greenberg cited, With strong and continuously improving underwriting conditions in most all regions of the world, we grew P&C (property and casualty) net premiums written 6.5% in the quarter in constant dollars, comprised of 10.8% growth in our commercial P&C business and a 3.3% decline in consumer lines we expect to grow our EPS through both revenue growth and improved margins. The fact that Chubb was able to grow its premiums written in 2020 makes it stand out among insurers. I believe the shares could find a place in most long-term portfolios. ProShares S&P 500 Dividend Aristocrats ETF (NOBL) Source: Shutterstock 52-week range: $48.62 $81.96 1-year price change: Up 1.31% Dividend yield: 1.25% Expense ratio: 0.35% Our next choice is an exchange-traded fund (ETF), namely the ProShares S&P 500 Dividend Aristocrats ETF. It focuses on the S&P 500 Dividend Aristocrats Index comprised of businesses that have grown dividends for decades, not just for 25 consecutive years. The fund, which started trading in September 2013, has 65 holdings. Total net assets of the fund are around $6.2 billion. As far as sector allocations are concerned, Industrials leads the ETF with 24.03%, followed by Consumer Staples (18.78%), and Materials (13.19%). The top ten names, with approximately equal weights, make up around 20% of net assets. Albemarle, Exxon Mobil (NYSE:XOM), AbbVie, Walgreens Boots Alliance (NASDAQ:WBA) head the roster. 10 Smart Stocks to Buy With $5,000 NOBL returned 6% in the past 52 weeks. I believe any decline in the price of the fund during this earnings season would make it a good buy for long-term portfolios. Sysco (SYY) Source: JHVEPhoto/Shutterstock.com 52-week range: $26 $84.12 1-year price change: Down 8.58% Dividend yield: 2.35% Houston, Texas-based Sysco sells food products and related equipment to restaurants, health care facilities, hotels, and educational facilities. It has about 57,000 employees in over 300 distribution facilities worldwide. The customer count exceeds 620,000. Needless to say, 2002 was a difficult year as many of those customers had to scale down operations due to the pandemic. Sysco released FY21 Q1 metrics in early November. Sales were $11.8 billion, a decrease of 23.0% YoY. Non-GAAP net earnings were $173.5 million, down by 66.0%. Non-GAAP diluted EPS was 34 cents, a decline of 65.3% CEO Kevin Hourican said, Although our first quarter 2021 results continue to be impacted by the pandemic, we are pleased with our overall expense management and our ability to produce positive free cash flow and a profitable quarter despite a 23% reduction in sales. A potential decline toward $70 would offer better long-term value. In the coming quarters, as economies recover and cities and countries go back to normal, Syscos operations are likely to recover as well. On the date of publication, Tezcan Gecgil did not have (either directly or indirectly) any positions in the securities mentioned in this article. Tezcan Gecgil has worked in investment management for over two decades in the U.S. and U.K. In addition to formal higher education in the field, she has also completed all 3 levels of the Chartered Market Technician (CMT) examination. Her passion is for options trading based on technical analysis of fundamentally strong companies. She especially enjoys setting up weekly covered calls for income generation. More From InvestorPlace Why Everyone Is Investing in 5G All WRONG Top Stock Picker Reveals His Next 1,000% Winner It doesnt matter if you have $500 in savings or $5 million. Do this now. The post 7 Dividend Aristocrats That Will Outlive Us All appeared first on InvestorPlace.

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Unisys to Research Use of Artificial Intelligence and Machine Learning to Detect Deceitful and Persuasive Writing for Australia's Defence and National...

A recent survey reveals an increased upward lift in AI adoption across industries including energy, manufacturing, heavy industry, infrastructure, and transport sectors

Within five years, more than half of leaders in the manufacturing and utilities sector expect artificial intelligence to be controlling high-value assets such as industrial plants, equipment, and machines.

Thats the word from the Next-Gen Industrial AI survey of 515 executives released by Siemens, which. More than half, 54%, believe that within the next five years, AI will autonomously control some of their organizations high-value assets.

See also: Auto Industry Drives The Smart Factories Movement

There are a wide range of use cases, and for starters, the surveys authors point to health and safety. A large set of respondents (44%) believe that, over the course of the next five years, an AI system will autonomously control machines that could potentially cause injury or death. This is important because while AI methodologies are similar across industries, the consequences of failure are not, the surveys authors point out. In many industrial organizations, bad decisions can leave thousands of people without a train to work; millions of dollars can be lost if machinery overheats; slight changes in pressure can lead to an environmental catastrophe, and innumerable scenarios can lead to loss of life.

Executives were asked how AI implementations will play out as they are introduced to factory floors, facilities, and transport fleets. For example, 56% said they would accept the decision of an impressive AI model over an experienced employee (44%), where the decision would have major financial consequences.

The kinds of data that will most likely feed these AI implementations will come from equipment manufacturers (71%), followed by internal data from other divisions, regions, or departments (70%), data from suppliers (70%), and performance data from sold products in use with customers (68%).

A majority of those organizations leading the way in their AI implementations, 55%, report they have already seen positive benefits from the technology, compared to 35% of those lagging in AI. At least 32% say they have been able to automate and improve quality control, and 76% expect to do so within the next two years. Another 32% report they have employed AI to optimize systems, machines, and processes automatically, with 74% expecting to do so within the next two years. Thirty percent represent they have been able to identify risks and issue warnings, with 74% expecting to be able to see this benefit within the next two years.

The surveys authors outline the ways artificial intelligence will impact industrial applications:

Ever-more powerful applications will no doubt raise new challenges, the surveys authors point out. It will require trusting AI with responsibilities that were only ever given to humans. In these cases, AI applications will need to win the confidence of decision-makers, while organizations will need to develop new risk and governance frameworks.

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Artificial Intelligence will Power Most Factories and Facilities - RTInsights

IRVINE, Calif., Jan. 19, 2021 /PRNewswire/ --RCP Construction, Inc. and Everguard.ai today announced a collaboration to bring artificial intelligence (AI) and sensor fusion to commercial construction job sites in an effort to improve safety measures. The agreement will bring Everguard's Sentri360 platform to RCP Construction's White Rock Center Project located in Rancho Cordova, CA. Sentri360 creates a paradigm shift from a reactive to a proactive approach to preventing workplace injuries and accidents.

Initial efforts will focus on using the power of AI and computer vision (CV) to enhance the safety protocols already in place for proper use of personal protective equipment (PPE) and geofencing deep excavation areas on the construction site. The use of heavy machinery, working at height, and the interaction of workers, drivers and machines on a construction site creates significant safety concerns. For example, Everguard's Sentri360 platform will ensure PPE is worn properly and provide proactive alerts to workers via wearables when PPE is not in use. The platform will monitor for proper use of hard hats, safety glasses and reflective vests as well as face mask use to help reduce the occurrence and spread of COVID-19. Another critical safety use case involves geofencing capabilities where the platform alerts an employee who ventures into a virtual safety zone.

In addition, this partnership will allow Everguard and RCP Construction to define additional use cases for ongoing development of Everguard's Sentri360 platform, including teaching the system via new CV algorithms to proactively address other specific hazards found on commercial construction job sites.

"Making sure our employees return home safely at the end of every day is our number one priority," said Phil Fasolo, general superintendent at RCP Construction. "We are excited to collaborate with Everguard.ai and deploy their platform on our construction worksites to help us optimize safety and efficiency."

Everguard.ai is moving environmental health and safety (EHS) from a reactive approach to one of proactive accident avoidance by utilizing AI powered by sensor fusion. Sensor fusion collects inputs from many different technologies, including computer vision (CV), real-time location systems (RTLS) and wearables. Sensor data is fed into edge computer for AI analysis and processing in much the same way that humans process information gathered by their senses. The platform provides near-real-time alerts and analytics to managers and workers, notifying them of safety threats before accidents occur and identifying opportunities for additional employee training.

"We are thrilled to collaborate with the RCP Construction team to reach the ultimate goal: an accident-free construction industry," said Sanjay Pandya, vice president and general manager of construction at Everguard. "RCP Construction's dedication to ensuring every team member makes it home safely each day is unmatched, and we are excited to provide the technology to help them make strides towards that goal."

About Everguard.ai

Everguard's mission is to protect companies' most important assets their people with the first truly proactive solution dedicated to industrial safety. Their Industrial Health and Safety platform utilizes artificial intelligence (AI) and sensor fusion driven by technologies that include edge computing, computer vision (CV), real-time location system (RTLS), wearables and others. Everguard's Sentri360 solution provides proactive interventions to help prevent and avoid industrial accidents and the billions of dollars in fees and lost-time incidents they cause.

About RCP Construction

RCP Construction's mission is to create trusted partnerships with clients and team members to deliver a quality product while maintaining the highest levels of innovation, professionalism, integrity, safety and client satisfaction. For 35 years, they have made the experience of building a better experience through high-quality customer service, teamwork, attention to detail, and follow through.

SOURCE Everguard.ai

https://everguard.ai/

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RCP Construction, Inc., and Everguard.ai Sign Agreement to Utilize Artificial Intelligence (AI) to Improve Job Site Safety - PRNewswire