Intelligent machines have long been the subject of science fiction. However, we now live in an era in which artificial intelligence (Al) is a reality, and it is having very real and deep impacts on our daily lives. From phones to cars to finances and medical care, AI is shifting the way we live.

AI applications can be found in many aspects of our lives, from agriculture to industry, communications, education, finance, government, service, manufacturing, medicine, and transportation. Even public safety and criminal justice are benefiting from AI. For example, traffic safety systems identify violations and enforce the rules of the road, and crime forecasts allow for more efficient allocation of policing resources. AI is also helping to identify the potential for an individual under criminal justice supervision to reoffend.[1]

Research supported by NIJ is helping to lead the way in applying AI to address criminal justice needs, such as identifying individuals and their actions in videos relating to criminal activity or public safety, DNA analysis, gunshot detection, and crime forecasting.

AI is a rapidly advancing field of computer science. In the mid-1950s, John McCarthy, who has been credited as the father of AI, defined it as the science and engineering of making intelligent machines.[2] Conceptually, AI is the ability of a machine to perceive and respond to its environment independently and perform tasks that would typically require human intelligence and decision-making processes, but without direct human intervention.

See A Brief History of Artificial Intelligence

One facet of human intelligence is the ability to learn from experience. Machine learning is an application of AI that mimics this ability and enables machines and their software to learn from experience.[3] Particularly important from the criminal justice perspective is pattern recognition. Humans are efficient at recognizing patterns and, through experience, we learn to differentiate objects, people, complex human emotions, information, and conditions on a daily basis. AI seeks to replicate this human capability in software algorithms and computer hardware. For example, self-learning algorithms use data sets to understand how to identify people based on their images, complete intricate computational and robotics tasks, understand purchasing habits and patterns online, detect medical conditions from complex radiological scans, and make stock market predictions.

AI is being researched as a public safety resource in numerous ways. One particular AI application facial recognition can be found everywhere in both the public and the private sectors.[4] Intelligence analysts, for example, often rely on facial images to help establish an individuals identity and whereabouts. Examining the huge volume of possibly relevant images and videos in an accurate and timely manner is a time-consuming, painstaking task, with the potential for human error due to fatigue and other factors. Unlike humans, machines do not tire. Through initiatives such as the Intelligence Advanced Research Projects Activitys Janus computer-vision project, analysts are performing trials on the use of algorithms that can learn how to distinguish one person from another using facial features in the same manner as a human analyst.[5]

See

The U.S. Department of Transportation is also looking to increase public safety through researching, developing, and testing automatic traffic accident detection based on video to help maintain safe and efficient commuter traffic over various locations and weather, lighting, and traffic conditions.[6] AI algorithms are being used in medicine to interpret radiological images, which could have important implications for the criminal justice and medical examiner communities when establishing cause and manner of death.[7] AI algorithms have also been explored in various disciplines in forensic science, including DNA analysis.[8]

AI is also quickly becoming an important technology in fraud detection.[9] Internet companies like PayPal stay ahead of fraud attempts by using volumes of data to continuously train their fraud detection algorithms to predict and recognize anomalous patterns and to learn to recognize new patterns.[10]

The AI research that NIJ supports falls primarily into four areas: public safety video and image analysis, DNA analysis, gunshot detection, and crime forecasting.

Video and image analysis is used in the criminal justice and law enforcement communities to obtain information regarding people, objects, and actions to support criminal investigations. However, the analysis of video and image information is very labor-intensive, requiring a significant investment in personnel with subject matter expertise. Video and image analysis is also prone to human error due to the sheer volume of information, the fast pace of changing technologies such as smartphones and operating systems, and a limited number of specialized personnel with the knowledge to process such information.

AI technologies provide the capacity to overcome such human errors and to function as experts. Traditional software algorithms that assist humans are limited to predetermined features such as eye shape, eye color, and distance between eyes for facial recognition or demographics information for pattern analysis. AI video and image algorithms not only learn complex tasks but also develop and determine their own independent complex facial recognition features/parameters to accomplish these tasks, beyond what humans may consider. These algorithms have the potential to match faces, identify weapons and other objects, and detect complex events such as accidents and crimes in progress or after the fact.

In response to the needs of the criminal justice and law enforcement communities, NIJ has invested in several areas to improve the speed, quality, and specificity of data collection, imaging, and analysis and to improve contextual information.

For instance, to understand the potential benefits of AI in terms of speed, researchers at the University of Texas at Dallas, with funding from NIJ and in partnership with the FBI and the National Institute of Standards and Technology, are assessing facial identification by humans and examining methods for effectively comparing AI algorithms and expert facial examiners. Preliminary results show that when the researchers limit the recognition time to 30 seconds, AI-based facial-recognition algorithms developed in 2017 perform comparably to human facial examiners.[11] The implications of these findings are that AI-based algorithms can potentially be used as a second pair of eyes to increase the accuracy of expert human facial examiners and to triage data to increase productivity.

In addition, in response to the need for higher quality information and the ability to use lower quality images more effectively, Carnegie Mellon University is using NIJ funding to develop AI algorithms to improve detection, recognition, and identification. One particularly important aspect is the universitys work on images in which an individuals face is captured at different angles or is partially to the side, and when the individual is looking away from the camera, obscured by masks or helmets, or blocked by lamp posts or lighting. The researchers are also working with low-quality facial image construction, including images with poor resolution and low ambient light levels, where the image quality makes facial matching difficult. NIJs test and evaluation center is currently testing and evaluating these algorithms.[12]

Finally, to decipher a license plate (which could help identify a suspect or aid in an investigation) or identify a person in extremely low-quality images or video, researchers at Dartmouth College are using AI algorithms that systematically degrade high-quality images and compare them with low-quality ones to better recognize lower quality images and video. For example, clear images of numbers and letters are slowly degraded to emulate low-quality images. The degraded images are then expressed and catalogued as mathematical representations. These degraded mathematical representations can then be compared with low-quality license plate images to help identify the license plate.[13]

Also being explored is the notion of scene understanding, or the ability to develop text that describes the relationship between objects (people, places, and things) in a series of images to provide context. For example, the text may be Pistol being drawn by a person and discharging into a store window. The goal is to detect objects and activities that will help identify crimes in progress for live observation and intervention as well as to support investigations after the fact.[14] Scene understanding over multiple scenes can indicate potentially important events that law enforcement should view to confirm and follow. One group of researchers at the University of Central Florida, in partnership with the Orlando Police Department, is using NIJ funding to develop algorithms to identify objects in videos, such as people, cars, weapons, and buildings, without human intervention. They are also developing algorithms to identify actions such as traffic accidents and violent crimes.

Another important aspect of AI is the ability to predict behavior. In contrast to the imaging and identification of criminal activity in progress, the University of Houston has used NIJ funding to develop algorithms that provide continuous monitoring to assess activity and predict emergent suspicious and criminal behavior across a network of cameras. This work also concentrates on using clothing, skeletal structure, movement, and direction prediction to identify and re-acquire people of interest across multiple cameras and images.[15]

AI can also benefit the law enforcement community from a scientific and evidence processing standpoint. This is particularly true in forensic DNA testing, which has had an unprecedented impact on the criminal justice system over the past several decades.

Biological material, such as blood, saliva, semen, and skin cells, can be transferred through contact with people and objects during the commission of a crime. As DNA technology has advanced, so has the sensitivity of DNA analysis, allowing forensic scientists to detect and process low-level, degraded, or otherwise unviable DNA evidence that could not have been used previously. For example, decades-old DNA evidence from violent crimes such as sexual assaults and homicide cold cases is now being submitted to laboratories for analysis. As a result of increased sensitivity, smaller amounts of DNA can be detected, which leads to the possibility of detecting DNA from multiple contributors, even at very low levels. These and other developments are presenting new challenges for crime laboratories. For instance, when using highly sensitive methods on items of evidence, it may be possible to detect DNA from multiple perpetrators or from someone not associated with the crime at all thus creating the issue of DNA mixture interpretation and the need to separate and identify (or deconvolute) individual profiles to generate critical investigative leads for law enforcement.

AI may have the potential to address this challenge. DNA analysis produces large amounts of complex data in electronic format; these data contain patterns, some of which may be beyond the range of human analysis but may prove useful as systems increase in sensitivity. To explore this area, researchers at Syracuse University partnered with the Onondaga County Center for Forensic Sciences and the New York City Office of Chief Medical Examiners Department of Forensic Biology to investigate a novel machine learning-based method of mixture deconvolution. With an NIJ research award, the Syracuse University team worked to combine the strengths of approaches involving human analysts with data mining and AI algorithms. The team used this hybrid approach to separate and identify individual DNA profiles to minimize the potential weaknesses inherent in using one approach in isolation. Although ongoing evaluation of the use of AI techniques is needed and there are many factors that can influence the ability to parse out individual DNA donors, research shows that AI technology has the potential to assist in these complicated analyses.[16]

The discovery of pattern signatures in gunshot analysis offers another area in which to use AI algorithms. In one project, NIJ funded Cadre Research Labs, LLC, to analyze gunshot audio files from smartphones and smart devices based on the observation that the content and quality of gunshot recordings are influenced by firearm and ammunition type, the scene geometry, and the recording device used.[17] Using a well-defined mathematical model, the Cadre scientists are working to develop algorithms to detect gunshots, differentiate muzzle blasts from shock waves, determine shot-to-shot timings, determine the number of firearms present, assign specific shots to firearms, and estimate probabilities of class and caliber all of which could help law enforcement in investigations.[18]

Predictive analysis is a complex process that uses large volumes of data to forecast and formulate potential outcomes. In criminal justice, this job rests mainly with police, probation practitioners, and other professionals, who must gain expertise over many years. The work is time-consuming and subject to bias and error.[19]

With AI, volumes of information on law and legal precedence, social information, and media can be used to suggest rulings, identify criminal enterprises, and predict and reveal people at risk from criminal enterprises. NIJ-supported researchers at the University of Pittsburgh are investigating and designing computational approaches to statutory interpretation that could potentially increase the speed and quality of statutory interpretation performed by judges, attorneys, prosecutors, administrative staff, and other professionals. The researchers hypothesize that a computer program can automatically recognize specific types of statements that play the most important roles in statutory interpretation. The goal is to develop a proof-of-concept expert system to support interpretation and perform it automatically for cybercrime.[20]

AI is also capable of analyzing large volumes of criminal justice-related records to predict potential criminal recidivism. Researchers at the Research Triangle Institute, in partnership with the Durham Police Department and the Anne Arundel Sheriffs Department, are working to create an automated warrant service triage tool for the North Carolina Statewide Warrant Repository. The NIJ-supported team is using algorithms to analyze data sets with more than 340,000 warrant records. The algorithms form decision trees and perform survival analysis to determine the time span until the next occurrence of an event of interest and predict the risk of re-offending for absconding offenders (if a warrant goes unserved). This model will help practitioners triage warrant service when backlogs exist. The resulting tool will also be geographically referenced so that practitioners can pursue concentrations of high-risk absconders along with others who have active warrants to optimize resources.[21]

AI can also help determine potential elder victims of physical and financial abuse. NIJ-funded researchers at the University of Texas Health Science Center at Houston used AI algorithms to analyze elder victimization. The algorithms can determine the victim, perpetrator, and environmental factors that distinguish between financial exploitation and other forms of elder abuse. They can also differentiate pure financial exploitation (when the victim of financial exploitation experiences no other abuse) from hybrid financial exploitation (when physical abuse or neglect accompanies financial exploitation). The researchers hope that these data algorithms can be transformed into web-based applications so that practitioners can reliably determine the likelihood that financial exploitation is occurring and quickly intervene.[22]

Finally, AI is being used to predict potential victims of violent crime based on associations and behavior. The Chicago Police Department and the Illinois Institute of Technology used algorithms to collect information and form initial groupings that focus on constructing social networks and performing analysis to determine potential high-risk individuals. This NIJ-supported research has since become a part of the Chicago Police Departments Violence Reduction Strategy.[23]

Every day holds the potential for new AI applications in criminal justice, paving the way for future possibilities to assist in the criminal justice system and ultimately improve public safety.

Video analytics for integrated facial recognition, the detection of individuals in multiple locations via closed-circuit television or across multiple cameras, and object and activity detection could prevent crimes through movement and pattern analysis, recognize crimes in progress, and help investigators identify suspects. With technology such as cameras, video, and social media generating massive volumes of data, AI could detect crimes that would otherwise go undetected and help ensure greater public safety by investigating potential criminal activity, thus increasing community confidence in law enforcement and the criminal justice system. AI also has the potential to assist the nations crime laboratories in areas such as complex DNA mixture analysis.

Pattern analysis of data could be used to disrupt, degrade, and prosecute crimes and criminal enterprises. Algorithms could also help prevent victims and potential offenders from falling into criminal pursuits and assist criminal justice professionals in safeguarding the public in ways never before imagined.

AI technology also has the potential to provide law enforcement with situational awareness and context, thus aiding in police well-being due to better informed responses to possibly dangerous situations. Technology that includes robotics and drones could also perform public safety surveillance, be integrated into overall public safety systems, and provide a safe alternative to putting police and the public in harms way. Robotics and drones could also perform recovery, provide valuable intelligence, and augment criminal justice professionals in ways not yet contrived.

By using AI and predictive policing analytics integrated with computer-aided response and live public safety video enterprises, law enforcement will be better able to respond to incidents, prevent threats, stage interventions, divert resources, and investigate and analyze criminal activity. AI has the potential to be a permanent part of our criminal justice ecosystem, providing investigative assistance and allowing criminal justice professionals to better maintain public safety.

On May 3, 2016, the White House announced a series of actions to spur public dialogue on artificial intelligence (AI), identify challenges and opportunities related to this technology, aid in the use of Al for more effective government, and prepare for the potential benefits and risks of Al. As part of these actions, the White House directed the creation of a national strategy for AI research and development. Following is a summary of the plans areas and intent.[24]

Manufacturing

Logistics

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Transportation

Agriculture

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Communications

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Education

Medicine

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Security and Law Enforcement

Safety and Prediction

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This article was published as part of NIJ Journal issue number 280, December 2018.

This article discusses the following grants:

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Using Artificial Intelligence to Address Criminal Justice ...

The future of decision-making includes an inventive blend of information, analytics, and artificial intelligence (AI), with the perfect scramble of human judgment. The outcome is augmented intelligence, where the analytical force and speed of AI assumes control over most of data processing, controlling human workers to make progressively agile, more intelligent choices and find new discoveries.

The development of analytics has caught the consideration of the heads of significant organizations. However, regardless of progressions throughout the years, few have had the option to stay aware of how to utilize analytics and AI among employees, in processes, and with appropriate oversight. The outcome is a lot of smart thoughts and technologies, however, applications that miss the mark regarding their potential.

Today, you can plug data into AI, and it can make its own models and make predictive recommendations. In any case, these models dont exist in a vacuum. They include data sources and outputs that impact the rest of your business. You need to consider how these models fit in and how to organize the insights from data. Also, you need governance over augmented intelligence to see that the automation is working and individuals know their job in the new man-meets-machine workforce.

If you are putting resources into analytics and AI, at that point think beyond data and technology. You need bilingual ability to bridge the gaps between industry and technology, and build up the correct solutions. You additionally need to consider the change in perspective in how man and machine will cooperate. At exactly that point would you be able to present a winning application and get the most value out of investments.

Augmented intelligence is an elective conceptualization of artificial intelligence that centers around AIs assistive job, accentuating the fact that cognitive technology is intended to improve human intelligence instead of supplanting it. The decision of the word augmented, which signifies to improve, strengthens the job human intelligence plays when utilizing AI and deep learning algorithms to find connections and take care of issues.

Platforms that offer Augmented Intelligence can accumulate a wide range of data (both structured and unstructured) from numerous sources, across different and siloed systems and they present that data such that gives human workers a total 360-degree perspective on every client.

The knowledge extricated from that data and introduced to the client is deeper and more noteworthy than ever before. Thus, workers are better educated on whats going on in their industry, what may influence their clients and the opportunities or threats that may emerge. Joining this abundance of data with the human touch is the thing that makes this innovation so incredible.

Some industry experts believe that the term artificial intelligence is excessively firmly connected to mainstream society and sci-fi, driving the overall population to have unreasonable feelings of trepidation about AI and unlikely assumptions regarding how autonomous robots and other smart frameworks will change the working environment and life in general.

Researchers and marketers trust the term augmented intelligence, which has an increasingly neutral connotation, will assist individuals in understanding that AI programming will just improve products and services, not supplant the people that use them.

Most companies have hills of information yet hardly any insights to advance positive business results. They struggle to accomplish ROI, for example, revenue growth, better customer experiences, and regulatory compliance, as well as to build an analytics capability for the future.

Augmented intelligence joins the strengths of people and machines when prospecting a value from data. To be specific, you can augment human instinct with smart algorithms that give quick, information driven predictive insights. These insights can assist individuals with overhauling functions, detect patterns, find strategic opportunities, and turn data into action.

Planned to extend human cognitive abilities, augmented intelligence is not quite the same as straight automation. Looking at the situation objectively, most procedures later on will be intended for straight-through processing, where there will be no people engaged with the procedure. As of now, that is beyond the realm of imagination on the grounds that in 25-30% of cases, you need people to step in.

Consider an airplane autopilot. In present day aviation, the autopilot can work freely, controlling heading and altitude, or it very well may be combined with a navigation system and fly pre-programmed, when the airplane has effectively gotten airborne. You despite everything need a pilot for departure and landing, for the time being. Ideally, an autopilot framework would consolidate human knowledge, in addition to experience focused through the prism of intuition. The outcome would be human judgment broadened by means of augmented intelligence.

To completely welcome the advantages and capability of augmented intelligence in analytics, it is important to totally re-engineer your mentality. You are not structuring a world that is predominantly manual with 30-40% automation. The objective is to make a completely new procedure, in a world that is predominantly automated and intended for 20% manual exemptions.

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Augmented Intelligence is the New Intelligence - Analytics Insight

State of Artificial Intelligence 2020

300 technology decision makers were surveyed to uncover the ways companies are approaching and implementing emerging technologies like Artificial Intelligence (AI) to grow business and surpass the competition.

SAN FRANCISCO, July 09, 2020 (GLOBE NEWSWIRE) -- Informa Tech media brands, InformationWeek and ITPro Today, today announced findings from their latest research survey the 2020 State of Artificial Intelligence. The team surveyed technology decision makers across North American companies to uncover the ways organizations are approaching and implementing emerging technologies specifically artificial intelligence (AI) and the Internet of Things (IoT) in order to grow and get ahead of the competition.

Key Findings in the 2020 State of Artificial Intelligence

To download a complimentary copy of The 2020 State of Artificial Intelligence, click here.

Media interested in receiving a copy of the report or the State of AI infographic should contact Briana Pontremoli at Briana.Pontremoli@informa.com.

2020 State of Artificial Intelligence Report MethodologyThe survey collected opinions from nearly 300 business professionals at companies engaged with AI-related projects. Nearly 90% of respondents have an IT or technology-related job function, such as application development, security, Internet of Things, networking, cloud, or engineering. Just over half of respondents work in a management capacity, with titles such as C-level executive, director, manager, or vice president. One half are from large companies with 1,000 or more employees, and 20% work at companies with 100 to 999 employees.

About Informa TechInforma Tech is a market leading provider of integrated research, media, training and events to the global Technology community. We're an international business of more than 600 colleagues, operating in more than 20 markets. Our aim is to inspire the Technology community to design, build and run a better digital world through research, media, training and event brands that inform, educate and connect. Over 7,000 professionals subscribe to our research, with 225,000 delegates attending our events and over 18,000 students participating in our training programs each year, and nearly 4 million people visiting our digital communities each month. Learn more about Informa Tech.

Media Contact:Briana PontremoliInforma Tech PRbriana.pontremoli@informa.com

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/416bdfd6-7702-4850-94b0-4838cf3a396f

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MULTIMEDIA UPDATE - New Research Reveals Adoption and Implementation of Artificial Intelligence in the Enterprise - GlobeNewswire

ARLINGTON, Va. U.S. military researchers are reaching out to industry to prevent enemy attempts to corrupt or spoof artificial intelligence (AI) systems by subtly altering or manipulating information the AI system uses to learn, develop, and mature.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) issued a solicitation on Wednesday (DARPA-PA-19-03-09) for the Reverse Engineering of Deceptions (RED) project, which aims at reverse engineering the toolchains of information deception attacks.

A deceptive information attack describes enemy attempts subtly to alters or manipulates information used by a human or machine learning system to alter a computational outcome in the adversarys favor.

Machine learning techniques are susceptible to enemy information warfare attacks at training time and when deployed. Similarly, humans are susceptible to being deceived by falsified images, video, audio, and text. Deception plays an increasingly central role in information warfare attacks.

Related: Research, applications, talent, training, and cooperation frame report on artificial intelligence (AI)

The Reverse Engineering of Deceptions (RED) effort will develop techniques that automatically reverse engineer the toolchains behind attacks such as multimedia falsification, enemy machine learning attacks, or other information deception attacks.

Recovering the tools and processes for such attacks provides information that may help identify an enemy. RED will seek to develop techniques that identify attack toolchains automatically, and develop scalable databases of attack toolchains.

RED Phase 1 will produce trusted-computing algorithms to identify the toolchains behind information deception attacks. The project's second phase will develop technologies for scalable databases of attack toolchains to support attribution and defense.

Related: Air Force researchers ask industry for SWaP-constrained embedded computing for artificial intelligence (AI)

The project also seeks to develop techniques that require little or no a-priori knowledge of specific deception toolchains; automatically cluster attack examples together to discover families of deception toolchains; generalize across several information deception scenarios like enemy machine learning and media manipulation; require just a few attacks to learn unique signatures; and scale to internet volumes of information.

Companies interested should upload 8-page proposals no later than 30 July 2020 to the DARPA BAA Website at https://baa.darpa.mil/. Email questions or concerns to Matt Turek, the DARPA RED program manager, at RED@darpa.mil.

More information is online at https://beta.sam.gov/opp/f108cad02f824285af5ca85e1f7481f4/view.

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trusted computing artificial intelligence (AI) information warfare - Military & Aerospace Electronics

MUMBAI :Artificial intelligence (AI) will not only help to resolve complex global problems but can enable businesses make better decisions on a day-to-day basis if applied well, said panelists at the Mint Pivot or Perish webinar on automation in the new normal post covid pandemic.

AI can be embedded into our day-to-day applications so the human intellect takes better decisions based on the most relevant data to the particular request basis past or similar requests. This is a growth opportunity for businesses now," said Dulles Krishnan, area vice president - Salesforce.

For example, during a service request, offering the employee insights about the customers preferences or similar examples of previous customers can help them efficiently up sell solutions and create more revenue or service opportunities.

On one end of automation is work that is high volume and repetitive. More usage of automation is now moving to low volume but unique work which requires solutions. That is where bots and AI solutions co-exist with humans," said Kamal Singhani, managing partner, IBM India.

Sangeeta Gupta, VP and chief strategy officer, Nasscom, said while AI can augment productivity and innovation, it can only work in well-defined use cases though it needs good quality data to work with.

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Artificial Intelligence will aid in better decision-making capacity - Livemint

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence platforms market and it is poised to grow by USD 12.51 billion during 2020-2024, progressing at a CAGR of over 33% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Latest Free Sample Report on COVID-19 Impact

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alibaba Group Holding Ltd., Alphabet Inc., Amazon Web Services Inc., International Business Machines Corp., Microsoft Corp., Palantir Technologies Inc., Salesforce.com Inc., SAP SE, SAS Institute Inc., and Tata Consultancy Services Ltd. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The rise in demand for AI-based solutions have been instrumental in driving the growth of the market. However, the rise in data privacy issues might hamper market growth.

Artificial Intelligence Platforms Market 2020-2024: Segmentation

Artificial Intelligence Platforms Market is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR44235

Artificial Intelligence Platforms Market 2020-2024: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our artificial intelligence platforms market report covers the following areas:

This study identifies investments in AI start-ups as one of the prime reasons driving the artificial intelligence platforms market growth during the next few years.

Artificial Intelligence Platforms Market 2020-2024: Vendor Analysis

We provide a detailed analysis of around 25 vendors operating in the artificial intelligence platforms market, including some of the vendors such as Alibaba Group Holding Ltd., Alphabet Inc., Amazon Web Services Inc., International Business Machines Corp., Microsoft Corp., Palantir Technologies Inc., Salesforce.com Inc., SAP SE, SAS Institute Inc., and Tata Consultancy Services Ltd. Backed with competitive intelligence and benchmarking, our research reports on the artificial intelligence platforms market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

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Artificial Intelligence Platforms Market 2020-2024: Key Highlights

Table Of Contents:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by Deployment

Customer Landscape

Geographic Landscape

Market Drivers Demand led growth

Market Challenges

Market Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impact & Recovery Analysis - Artificial Intelligence Platforms Market 2020-2024 | Rise in Demand for AI-based Solutions to Boost Growth |...

The second plenary meeting of theAd Hoc Committee on Artificial Intelligence (CAHAI)will be held from 6 to 8 July 2020, bringing together representatives of the 47 Council of Europe member states, observer states (Canada, USA, Holy See, Israel, Japan, Mexico) as well as civil society, academia and the Council of Europe's Internet partners.

The CAHAI observer group is expanding with the participation of Israel for the first time and 12 new stakeholders. Other international organisations (EU, OECD, UNESCO) will also contribute to CAHAIs work.

CAHAI members will make concrete proposals on the feasibility study of a future legal framework on artificial intelligence (AI) based on human rights, democracy and the rule of law. In this connection, they will address issues such as the mapping of legal instruments applicable to AI and the opportunities and risks arising from the design, development and application of AI on human rights, rule of law and democracy, which have already been subject of a preliminary analysis.

Other issues such the scope and main elements of the above-mentioned legal framework will also be discussed.

This will provide the necessary impetus for the preparation of the first draft of the feasibility study, which will be presented at the CAHAI plenary meeting in December 2020.

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Second plenary meeting of the Ad Hoc Committee on Artificial Intelligence (CAHAI) - Council of Europe

The COVID-19 pandemic brings with it increased focus on Artificial Intelligence ("AI") as developers rush to create software, such as contact tracing software, that can help businesses reduce the risks for employees returning to work. Before businesses acquire AI technology, owners and human resources professionals must consider how to balance being proactive with protecting employee privacy. There are numerous provisions they should incorporate into their contracts with their software developers. Some of these provisions include:

Comprehensive Testing

When contracting for AI, the customer should be particularly focused on documenting the level of testing to be provided. Generally, the more robust the description of the testing, the better. At a minimum, this description should include: the number of rounds of testing, the process for testing, what the minimum sample size will be for each round of testing, and who is involved in creating the test environment. In addition, the customer should ask the vendor to contractually commit to describing the remedies if the testing does not result in adequate work product. The parties need to define exactly what constitutes acceptance, and whether ongoing testing is necessary or appropriate, particularly as the AI adapts and learns from itself.

Security

Security is currently one of the fastest evolving areas of information technology law. When contracting for AI, it is important to have standards that can adapt to this ever-changing environment. In order to do this, it is helpful to incorporate a requirement that the vendor comply with industry security standards such as ISO-27001 and OWASP-Top 10 (for web applications). Businesses should also state any specific technical requirements related to security necessary to protect the customers IT environment, as well the whereabouts and other data associated with its employees and the locations of its customers. Finally, requiring adequate cyber-insurance that meets the risk level of the environment is also prudent.

Data Privacy

Customers should be wary that AI may transform data that was once anonymous into data that is decipherable. Also, there is a complex set of data privacy laws in effect in the United States and even more so globally. All vendors should contractually agree to comply with any such applicable laws. Customers should also consider putting limitations on how vendors can use data, particularly outside of providing the services to the contracting customer.

Minimizing Risk

Most vendors require a cap on consequential damages, but in AI contracts this provides additional challenges as much of the risk to the customer lies with items commonly considered to be consequential damages. There are several ways to address this problem. One way is to redefine what constitutes direct damages. A second way is to negotiate exceptions to caps for specified items such as: breaches of privacy/data security, failure to comply with threshold requirements, and allegations of bias due to algorithm data use.

This article is meant to share a few ideas for contracting for AI. As with any contract, you should contact a lawyer understanding the nuances of the subject matter particular to your situation prior to signing it.

For more information, please contact Diana J.P. McKenzie, partner & chair, Information Technology & Outsourcing Practice Group at HunterMaclean, dmckenzie@huntermaclean.com or Nicole Pope, attorney at HunterMaclean, npope@huntermaclean.com.

For other expert advice on taxes, retirement accounts, benefits, and liability insurance, go to savannahnow.com/beacon.

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COVID-19 and Artificial Intelligence: How the pandemic has re-ignited a focus on the software - Savannah Morning News

A Long Island artificial intelligence startup has built software aimed at pinpointing U.S. counties where the COVID-19 outbreak is likely to be most deadly.

In a June report, the data-mining company, Akai Kaeru LLC, forecast spiking COVID-19 mortality with the heaviest concentrations in counties of the Southeast, including Mississippi, Georgia and Louisiana, said co-founder and chief executive Klaus Mueller.

Nationwide, the software found 985 out of all 3,007 U.S. counties are at risk.

"These patterns identify groups of counties that have a steeper increase in the death-rate trajectory," he said.

Closer to home, the software found Nassau and Suffolk counties are likely to be relatively stable, but Westchester and Rockland counties are potential tinderboxes that could tip into crisis, said Mueller, a computer science professorat Stony Brook University.

The factors making Westchester and Rockland more vulnerable to a spike in mortality include areas with more crowding and fewer residents with access to cars, he said.

"They need to be very careful with reopening," Mueller said of the northern suburbs. "It just takes a spark for there to be a second wave."

At the same time, he said, Long Island "is not out of the woods" and abandoning policies like social distancing could lead to a new surge.

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The software analyzes more than 500 attributes related to demographics, economics, infrastructure, race and ethnicity as well as deaths and other health data directly related to COVID-19.

Dr. Isaac Weisfuse, an epidemiologist and adjunct professor at Cornell University's public health program, said that data-mining software is used by public health departments.

If the software provides sufficient warning, he said, preventive measures like screening and mask-wearing policies can be instituted.

"It's more valuable if it's accurate two months before, but it's still valuable two weeks before," he said.

The Centers for Disease Control and Prevention aggregates mortality forecasts from about two dozen software programs and expects 140,000 to 160,000 total reported COVID-19 deaths in the United States by July 25.

While many COVID-19 models provide specific fatality forecasts at the state level, the Akai Kaeru software is one of the few that assesses risk at the county level.

Mueller said that based on the one-month snapshots, the software is finding that counties at the highest risk have a death rate that grew two- to three times more than the United States,overall.

In June, the fatality rate for U.S. COVID-19 related deaths was 24.1 per 100,000 population, he said.

Aside from finding geographies in jeopardy, the software is able to unearth specific and sometimes surprising combinations of factors that appear to be connected to counties with higher death rates.

For instance, counties with low poverty levels, high homeownership rates, but high levels of housing debt were found to be at high risk.

"The more housing debt you have, the more death you have," Mueller said.

Other counties at risk had a combination of residents who were sleep-deprived (according to data from the CDC) and had low levels of education and low rates of health insurance coverage.

Another group of counties had few Asian residents but high overall minority populations, including impoverished Black children.

Rural counties with high poverty rates and an aging population also were deemed at risk.

"One of the defining characteristics is we focus on explainability," said Eric Papenhausen, chief technology officer and co-founder of the company. "You can create a narrative around it," which can lead to changes in public policy.

Akai Kaeru is based at the Center of Excellence in Wireless and Information Technology on the Stony Brook University campus.

The 4-year-old company, whose name is Japanese for red frog, has raised $1 million in funding from the National Science Foundation's Small Business Innovation Research program and about $200,000 through the New York State Strategic Partnership for Industrial Resurgence program and the New York State Center for Advanced Technology.

The COVID-19 software is a demonstration project for the company, whose data-mining software can be applied to a variety of tasks, including assessing mortgage risk, speeding drug discovery and investment analysis.

Another startup, Manhattan-based Dataminr, is seeking to use social media posts as a leading indicator of COVID-19 infections at the county level.

Artificial intelligence refers to the ability of software programs to learn and perform actions previously reserved for humans.

Mueller said his company's "explainable AI" is not a black box and can provide insight into how the software reached its conclusions.

Ken Schachter covers corporate news, including technology and aerospace, and other business topics for Newsday. He has also worked at The Miami Herald and The Jerusalem Post.

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LI startup predicts where COVID-19 will spike - Newsday

This report presents the worldwide Artificial Intelligence in Telecommunication market size (value, production and consumption), splits the breakdown (data status 2018 and forecast to 2025), by manufacturers, region, type and application.

This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porters Five Forces Analysis.

The report presents the market competitive landscape and a corresponding detailed analysis of the major vendor/key players in the market.

Get PDF Sample Copy of this Report to understand the structure of the complete report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.marketresearchhub.com/enquiry.php?type=S&repid=2678594&source=atm

The report provides a valuable source of insightful data for business strategists and competitive analysis of Artificial Intelligence in Telecommunication Market. It provides the Artificial Intelligence in Telecommunication industry overview with growth analysis and futuristic cost, revenue and many other aspects. The research analysts provide an elaborate description of the value chain and its distributor analysis. This Tire Artificial Intelligence in Telecommunication study provides comprehensive data which enhances the understanding, scope and application of this report.

The report firstly introduced the Artificial Intelligence in Telecommunication basics: definitions, classifications, applications and market overview; product specifications; manufacturing processes; cost structures, raw materials and so on. Then it analyzed the worlds main region market conditions, including the product price, profit, capacity, production, supply, demand and market growth rate and forecast etc. In the end, the report introduced new project SWOT analysis, investment feasibility analysis, and investment return analysis.

The major players profiled in this report include:IBM CorporationMicrosoftIntel CorporationGoogleAT&T Intellectual PropertyCisco SystemsNuance CommunicationsEvolv Technology SolutionsInfosys LimitedNVIDIA Corporation

The end users/applications and product categories analysis:On the basis of product, this report displays the sales volume, revenue (Million USD), product price, market share and growth rate of each type, primarily split into-General Type

On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, sales volume, market share and growth rate of Artificial Intelligence in Telecommunication for each application, including-Network SecurityNetwork OptimizationCustomer AnalyticsVirtual Assistance

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Regional Analysis For Artificial Intelligence in Telecommunication Market:

For comprehensive understanding of market dynamics, the global Artificial Intelligence in Telecommunication market is analyzed across key geographies namely: United States, China, Europe, Japan, South-east Asia, India and others. Each of these regions is analyzed on basis of market findings across major countries in these regions for a macro-level understanding of the market.

Influence of the Artificial Intelligence in Telecommunication market report:

-Comprehensive assessment of all opportunities and risk in the Artificial Intelligence in Telecommunication market.

Artificial Intelligence in Telecommunication market recent innovations and major events.

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The report has 150 tables and figures browse the report description and TOC:

Table of Contents of Artificial Intelligence in Telecommunication Market

1 Study Coverage

1.1 Artificial Intelligence in Telecommunication Product

1.2 Key Market Segments in This Study

1.3 Key Manufacturers Covered

1.4 Market by Type

1.4.1 Global Artificial Intelligence in Telecommunication Market Size Growth Rate by Type

1.4.2 Hydraulic Dredges

1.4.3 Hopper Dredges

1.4.4 Mechanical Dredges

1.5 Market by Application

1.5.1 Global Artificial Intelligence in Telecommunication Market Size Growth Rate by Application

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2 Executive Summary

2.1 Global Artificial Intelligence in Telecommunication Market Size

2.1.1 Global Artificial Intelligence in Telecommunication Revenue 2014-2025

2.1.2 Global Artificial Intelligence in Telecommunication Production 2014-2025

2.2 Artificial Intelligence in Telecommunication Growth Rate (CAGR) 2019-2025

2.3 Analysis of Competitive Landscape

2.3.1 Manufacturers Market Concentration Ratio (CR5 and HHI)

2.3.2 Key Artificial Intelligence in Telecommunication Manufacturers

2.3.2.1 Artificial Intelligence in Telecommunication Manufacturing Base Distribution, Headquarters

2.3.2.2 Manufacturers Artificial Intelligence in Telecommunication Product Offered

2.3.2.3 Date of Manufacturers Enter into Artificial Intelligence in Telecommunication Market

2.4 Key Trends for Artificial Intelligence in Telecommunication Markets & Products

3 Market Size by Manufacturers

3.1 Artificial Intelligence in Telecommunication Production by Manufacturers

3.1.1 Artificial Intelligence in Telecommunication Production by Manufacturers

3.1.2 Artificial Intelligence in Telecommunication Production Market Share by Manufacturers

3.2 Artificial Intelligence in Telecommunication Revenue by Manufacturers

3.2.1 Artificial Intelligence in Telecommunication Revenue by Manufacturers (2019-2025)

3.2.2 Artificial Intelligence in Telecommunication Revenue Share by Manufacturers (2019-2025)

3.3 Artificial Intelligence in Telecommunication Price by Manufacturers

3.4 Mergers & Acquisitions, Expansion Plans

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How Innovation is Changing the Artificial Intelligence in Telecommunication Market - 3rd Watch News

Every department in a company has its own challenges.

In the case of Human Resources, recruitment and onboarding processes, employee orientations, process paperwork, and background checks is a handful and many a time painstaking mostly because of the repetitive and manual nature of the work. The most challenging of all is engaging with employees on human grounds to understand their needs.

As leaders today are observing the AI revolution across every process, Human resources is no exception: there has been a visible wave of AI disruption across HR functions. According to an IBMs survey from 2017, among 6000 executives, 66% of CEOs believe that cognitive computing can drive compelling value in HR while half of the HR personnel believe this may affect roles in the HR organization. The study clearly exhibits the apprehension of HR executives caused by the AI disruption in their field.

While one aspect of AI is creating uneasiness: the other is promising convenience. AI aims to empower the HR department with the right knowledge to optimize processes with less manual power and guarantees to mitigate errors.

TheCOVID-19 pandemic has highlighted thepower of AIin real-time< Backlink-https://us.sganalytics.com/blog/ai-can-detect-infections-with-96-percent-accuracy-can-ai-predict-the-next-pandemic/>, including its shortcomings. At the crux of the AI evolution is the minimization of human labored processes. Sophisticated AI algorithms can analyze large amounts of data in no time and self-educate themselves to recognize and map patterns, which can come in handy for HR staffs to plan and operate strategically.

While a human can be biased, get bored and make unintended mistakes provoking inadequacy in productivity and efficiency, AI programs are unbiased and diligent, enabling more productivity and efficiency.

HR executives who perform tasks like applicant tracking, payroll, training, and job postings manually without automation, state that they spend 14 hours a week on an average on these tasks. Leveraging AI to automate these HR processes can be extremely pertinent for meeting the following key business requirements: First, save time and increase efficiency; Second, provide real-time responses and solutions that meet employee expectations.

As per a Mckinseys study AI will drastically change business regardless of the industry. AI could potentially deliver an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year.

Lets dive deeper to understand how AI can help sophisticate HR processes while not necessarily replacing human resource personnel.

1. Improved Employee Experience

Employees are the first customers for any organization. Hence employee experience is as important as customer experience.

As employee experience is becoming the next competitive edge for businesses, the coming days will be focused on providing personalized engagement and improving employee experience for human resources.

According to a Deloitte survey, 80% HR executives rate employee experience as important, while only 22% believe their organization excels at providing a differentiated employee experience.

Additionally, the advent of smart workplace has raised the bars of employees expectations for work-space experience and engagement factors.

Jennifer Stroud, HR Evangelist & Transformation Leader atServiceNow, says,We have seen the need for chatbots, AI and machine learning in the workplace to drive more productivity as well as modern, consumerized employee experiences. These consumer technology solutions are exactly what employees want in the workplace.

Engaging AI can help the HR department provide personalized employee engagement experiences across the entire employee lifecycle, right from recruitment and onboarding to career pathing.

2. Empowering HRs to make Data-Driven decisions

In common, the data-to-decision workflow looks like the below figure for many people.

Source: jobsoffice

Many HR technologies still follow the above workflow and depend on manual methods to glean insights from data. This task grows tedious and creates a bottleneck for end-users (data analysts) to draw insights within the stipulated time leading to decision making on outdated data.

While frontier technologies like data analytics are advancing to provide real-time data to make fast and fact-based decisions, AI can assist Human Resource professionals in harnessing this real-time data and making quick, consistent, and data-driven decisions. After all, the bottom line of HR agility is decision making.

3. Intelligent Automation

Intelligent automation fuses automation with AI. This will enable machines to make human-like decisions by self-educating themselves. Apart from augmenting productivity and efficiency for repetitive manual processes, this can help remove human interventions deployed for automated process completely.

1.More work in less time!

Crafting job descriptions for a particular role, filtering resumes and analyzing skillsets to find the apt talent is not only tiring and tedious, but also tricky for human resource professionals as a simple overlooked aspect can lead to a significant mistake, which may cost the company dearly in the long run. Well, AI can help HR staff overcome such scenarios by crafting bespoken job descriptions automatically and assist them in reading through thousands of resumes within a short time, thus effectively reducing the time and manual hard work put in by recruiters.

2. Identify the right talent without bias

HR personnel are humans and are likely to exhibit bias subconsciously. AI, on the other hand, is immune to human emotions which makes it the perfect fit to process candidate profiles based on required skillset without any disregard for candidates age, race, gender, geographic areas or organizational relationship. An unbiased recruitment is a win-win for both HR staff and organization. Furthermore, AI can be instrumental in increasing retention rates and establishing cultural diversity.

Consider programs like Texito, they help recognize gender bias in ads enabling recruiters to embrace a neutral language.

3. Streamline employee onboarding

The first day of an employee in an organization is like the first day of a transferred student in a new school. Although employees are grown-ups and possess the cognitive intelligence to adapt easily to an environment, deep down they look for a guidance to help them settle down in a new environment. Fortunately, organizations have HR staff to do this job. Employees generally have numerous queries on their first day regarding company policies, leaves, compensations, notice period, insurance claims, etc. As intriguing as the questions may be for an employee, these queries may turn repetitive and exhausting for an HR personnel over the time. Engaging AI chatbots makes it simple to answer such repetitive questions and make more time for the HR staff to concentrate on other essential tasks.

4. Optimize employee engagement to build better relationships

Apart from recruitment and onboarding, AI can be used to streamline processes like scheduling meetings, training employees and other such business processes. AIs capabilities to recognize personas will help Human resources professionals understand the human aspect of every single employee in-depth and enable them to shape a friendly and exciting company culture to provide unique and personalized employee engagement experiences.

5. Manage employee churn

Understanding factors that cause and arrest employee churn is the toughest part of an HRs job. People change jobs for various reasons like financial growth, career growth, shift in profiles, unsatisfied work environment, etc. Leveraging AI capabilities can help the HR department in continuously monitoring and evaluating employees thoughts about the organization, work culture, the degree of satisfaction with their job, etc. Knowing what offends or drives an employee can help in underlining the employee churn factors precisely. AI can help HR executives in performing this task more precisely.

All said and done, even though AIs capabilities would help reduce manual work and boost efficiency and productivity, artificial intelligence doesnt possess the emotional intelligence of humans. AI also cannot compensate for the humane connection that HR personnel form with employees and leverage to drive engagement and responsiveness.

Therefore, to answer the critical question that haunts HR executives Will AI be the reason why I might lose my job? No. Not really. The whole idea of AI in HR is the integration of technology to automate the more monotonous HR related tasks and optimize processes to add value to human work in less time. In the AI era, new jobs will evolve that will have new skills requirements unleashing the evolution of the HR function in an AI-first world.

Author Detail:

Jency is a technology content writer with SG Analytics. She contributes to the companys advancements by writing creative and engaging for their website and blogs. Her hobbies include music, reading, and trekking.

Company designation: Content Writer, SG Analytics

Location: Pune

Links to my blogs: https://us.sganalytics.com/blog/75-percent-consumers-anticipate-financial-impact-effects-of-covid-on-consumer-behaviour/, https://us.sganalytics.com/blog/social-media-analytics-is-truly-a-game-changer-heres-why/

Social media profile: LinkedIn https://www.linkedin.com/in/jency-durairaj-21225aa9

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AI In Human Resources To AI or not to AI? - Analytics Insight

KRAKW, Poland and AUCKLAND, New Zealand, July 9, 2020 /PRNewswire/ --Ardigenand CVC announced that they entered a research collaboration aimed at the development of SARS-CoV-2 vaccine.

Ardigen's neoantigen prediction platform called "ArdImmune Vax" employs state of the art bioinformatics and Artificial Intelligence to identify an optimal set of neoantigens as targets for cancer vaccines or adoptive cell therapies. This technology is also very well suited for the design of vaccines for infectious diseases. The core of the platform is a proprietary algorithm capable of predicting neoantigens' probability to elicit an immune response.

This joint research enables CVC to benefit from Ardigen's vaccine design technology by selecting which viral epitopes are the most suitable to boost cellular immune response. Complementing humoral and cellular response in the vaccine design is expected to result in 2 strong lines of defense against the coronavirus. The approach is likely to be more effective than vaccines designed to create antibodies alone.

"We are thrilled to help global efforts to mitigate COVID-19 applying our breakthrough technology powered by Artificial Intelligence, reducing the vaccine design phase to a few weeks," comments Janusz Homa, CEO of Ardigen.

Robert Feldman, CEO and Co-founder of CVC, adds: "CVC is excited to be working with Ardigen who are at the forefront of T-cell epitope design. The collaboration gives us the best chance of our product inducing an effective T-cell response against SARS-Cov-2."

About Ardigen

Ardigenis harnessing advanced Artificial Intelligence methods for novel precision medicine. The company accelerates therapy development by decoding microbiome, designing immunity and providing digital drug discovery services. Ardigen's team is rooted in biology and holds deep expertise in bioinformatics, machine learning, and software engineering. The company's in-house datasets together with platforms for immunology, biomarker, and microbiome research can empower effective pharmaceuticals development.

About CVC

New Zealand based CVC (COVID-19 Vaccine Corporation) is focused on creating an effective vaccine for the SARS-CoV-2 virus which has caused the recent pandemic. The highly scalable biobead technology that will be used in the vaccine production was developed by Polybatics, a company spun off from Massey University in 2009 in New Zealand. The CVC founders bring to the table 30 years of experience in the biotechnology industry and in pharmaceutical manufacturing. Read more at cvc.nz.

Logo - https://mma.prnewswire.com/media/969153/Ardigen_SA_Logo.jpg

Contact:Barbara Wyroba| Ardigen S.A.+48-539-730-118[emailprotected]

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Ardigen Enters Into Research Collaboration With CVC to Apply Artificial Intelligence for Identification of T-cell Targets - PRNewswire

HYDERABAD, India, July 10, 2020 /PRNewswire/ -- Today, Founder & Chief Data Scientist of AiBridge ML, Mr. Prajnajit Mohanty, announced addition of Handwriting & Image Recognition capabilities to their Financial Document Automation tool, AIMunshi. It is notable that AIMunshi is a 'Make in India' Deep Learning based Intelligent Financial Documents Automation tool from AiBridge ML.

"Addition of deep learning based Handwriting & Image recognition capabilities to AiMunshi will enable us to offer augmented features to diversified industries. It will help them to operate in contactless manner and automate their routine work during current COVID-19 pandemic. Industries like education, healthcare, retail, manufacturing etc. will be benefitted immensely and we are committed to help Indian industries to use AI & Machine learning," said Mr. Mohanty.

Many USAand Australian healthcare, pharma and retail companies have already realized considerable financialand operational benefits using AiMunshi and yielding real, tangible ROI faster.

AiMunshi processes ordersand invoices automatically, reducing accounts payable costs while improving both the accuracy and the speed of data extraction from various sources or emails directly. It is capable of automatically interpreting the relevant information and fields within a PDF or image-based invoicesand order, or in emails in real-time.

Intelligent features of AiMunshi:

About AiBridge Ml Pvt Ltd

Founded in Feb, 2019 by Senior Technology Leaders with combined experience of 84 years in IT, AiBridge ML Pvt Ltd develops innovative Enterprise Solutions in Artificial Intelligence, Machine Learning, Augment Reality and Robotic Process Automation. Aibridge ML released AI powered deep learning based tool for Financial Document Processing Automation, AiMunshi in Sep 2019. They currently have 30+ Senior Data Scientists with combined experience of more than 70 years. Currently, Aibridge ML is offering their solutions in USA, Australia, Canada & India.

Company website: http://www.aibridgeml.ai AiMunshi Product website: http://www.aimunshi.ai

Media Contact: Ajay Ray[emailprotected]+91-9849743823Director, Aibridge ML Pvt Ltd

SOURCE AiBridge ML Pvt Ltd

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'Make in India': Artificial Intelligence Company, AiBridge ML, Adds Handwriting and Image Recognition Capabilities to AiMunshi, the Popular Financial...

SALT LAKE CITY, July 10, 2020 (GLOBE NEWSWIRE) In a release issued under the same headline on June 1, 2020 by OMNIQ, Inc. (OTCQB:OMQS), please be advised that the second paragraph as originally issued contained certain inaccuracies, not related to financial results or projections, which have been corrected below.

OMNIQ, Inc. (OTCQB:OMQS) (OMNIQ or the Company), announces that it has been selected to deploy its Quest Shield campus safety solution at the Talmudical Academy of Baltimore in Maryland.

The Quest Shield security package uses the Companys AI-based SeeCube technology platform, a ground-breaking cloud-based/on-premise security solution for Safe Campus/School applications. The platform provides unique AI-based computer vision technology and software to gather real-time vehicle data, enabling the Quest Shield to identify and record images of approaching vehicles including color, make and license plate information. The license plate is then compared against the schools internal watch list to provide immediate notifications of unauthorized vehicles to security and administrative personnel. In addition to providing a vehicle identification and recognition solution to the Talmudical Academy, the Quest Shield comprehensive security platform addresses other security concerns including controlling access to the buildings and visitor management as well as the ability to pre-register guests for school activities.

Additionally, as part of COVID-19 mitigation, parents in Maryland will be asked to take and record their childs temperature each day before they leave for school. Quest Shield will automate this process, by providing parents an online form where they may record the temperature. All Talmud Academy students will be equipped with an ID tag that will have a QR code that can be read with a barcode scanner. As students enter campus, faculty equipped with Quest handheld scanners will read the barcode to confirm that the students temperature has been taken that day; if the form has not been filled in, faculty will check temperatures before allowing students inside.

Shai Lustgarten, CEO of OMNIQ, commented: It is our privilege to work with the Talmudical Academy to provide our solution to enhance safety at their Baltimore campus. Quest Shield is an extension of the homeland security solution we designed for the Israeli authorities to fight terrorism and save lives.

Rabbi Yaacov Cohen, Executive Director, Talmudical Academy of Baltimore, commented:Concern about campus safety and the safety of our students and faculty drove the Talmudical Academy to seek ways to implement new strategies aimed at preventing crimes and violence that may be committed on the school grounds. The unfortunate reality today is that situations we could never imagine just a few years ago are happening now with increasing regularity. Most security systems that are currently being deployed on other campuses are good at recording events subsequent to crimes being committed. With Quest Shield, we have an opportunity to alert personnel and Law Enforcement ahead of any sign of violence.

Mr. Lustgarten added: The Quest Shield has been tailored to provide a proactive solution to improve security and safety in schools and on campuses as well as community centers and places of worship in the U.S. that have unfortunately become a target for ruthless attacks. Were pleased to work with a forward-thinking organization like the Talmudical Academy, it is gratifying that the Academy selected the Quest Shield platform to strengthen its security precautions.

Additionally, many schools and communities are expressing concern around children returning to school in the fall due to COVID-19. With that in mind, Talmudical Academy will also employ the Quest Shield to provide an automated screening process to confirm that students have had their temperatures checked, per Maryland regulation, upon their arrival on campus and prior to them entering the school facilities.

Mr. Lustgarten concluded, We are proud to be able to improve student safety in the U.S., as well as in other vulnerable communities. Quest Shield has previously been implemented by a pre-K through Grade 12 school in Florida and at a Jewish Community Center in Salt Lake City. We look forward to working closely with the Academy and other institutions to promote the health and safety of students, faculty and support personnel.

About OMNIQ, Corp.OMNIQ Corp. (OMQS) provides computerized and machine vision image processing solutions that use patented and proprietary AI technology to deliver data collection, real time surveillance and monitoring for supply chain management, homeland security, public safety, traffic & parking management and access control applications. The technology and services provided by the Company help clients move people, assets and data safely and securely through airports, warehouses, schools, national borders, and many other applications and environments.

OMNIQs customers include government agencies and leading Fortune 500 companies from several sectors, including manufacturing, retail, distribution, food and beverage, transportation and logistics, healthcare, and oil, gas, and chemicals. Since 2014, annual revenues have grown to more than $50 million from clients in the USA and abroad.

The Company currently addresses several billion-dollar markets, including the Global Safe City market, forecast to grow to $29 billion by 2022, and the Ticketless Safe Parking market, forecast to grow to $5.2 billion by 2023.

Information about Forward-Looking StatementsSafe Harbor Statement under the Private Securities Litigation Reform Act of 1995. Statements in this press release relating to plans, strategies, economic performance and trends, projections of results of specific activities or investments, and other statements that are not descriptions of historical facts may be forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934.

This release contains forward-looking statements that include information relating to future events and future financial and operating performance. The words anticipate, may, would, will, expect, estimate, can, believe, potential and similar expressions and variations thereof are intended to identify forward-looking statements. Forward-looking statements should not be read as a guarantee of future performance or results, and will not necessarily be accurate indications of the times at, or by, which that performance or those results will be achieved. Forward-looking statements are based on information available at the time they are made and/or managements good faith belief as of that time with respect to future events, and are subject to risks and uncertainties that could cause actual performance or results to differ materially from those expressed in or suggested by the forward-looking statements. Important factors that could cause these differences include, but are not limited to: fluctuations in demand for the Companys products particularly during the current health crisis, the introduction of new products, the Companys ability to maintain customer and strategic business relationships, the impact of competitive products and pricing, growth in targeted markets, the adequacy of the Companys liquidity and financial strength to support its growth, the Companys ability to manage credit and debt structures from vendors, debt holders and secured lenders, the Companys ability to successfully integrate its acquisitions, and other information that may be detailed from time-to-time in OMNIQ Corp.s filings with the United States Securities and Exchange Commission. Examples of such forward looking statements in this release include, among others, statements regarding revenue growth, driving sales, operational and financial initiatives, cost reduction and profitability, and simplification of operations. For a more detailed description of the risk factors and uncertainties affecting OMNIQ Corp., please refer to the Companys recent Securities and Exchange Commission filings, which are available at http://www.sec.gov. OMNIQ Corp. undertakes no obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, unless otherwise required by law.

Investor Contact: John Nesbett/Jen BelodeauIMS Investor Relations203.972.9200jnesbett@institutionalms.com

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CORRECTION - OMNIQs Artificial Intelligence-Based Quest Shield Solution Selected by the Talmudical Academy of Baltimore - Financialbuzz.com

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The decision to invest in a company can rely on a lot of guesswork, but Kim Polese, co-founder and chairman of CrowdSmart, is using artificial intelligence to turn qualitative information into quantitative dataand reduce bias along the way.

When were talking about using collective intelligence, augmented collective intelligence, what were really talking about is using a combination of human and machine intelligence to improve the way that diligence is done, Polese said this past Wednesday at a BarronsInvesting in Tech panel. The founder of an artificial-intelligence platform designed to predict a companys potential for success, Polese detailed how the CrowdSmart platform works, and how it could help remove bias from the diligence process.

The system draws on the insights of a group of 25 or more people, selected for their different levels of expertise, to evaluate prospective investments, explained Polese, who said her career in Silicon Valley began 30 years ago at the first artificial-intelligence company to go public.

Those people are able to access all of the full diligence materials, so that might be videos, live Q&As with the teams, all of the financials, and, ultimately, a brainstorming process is kicked off, Polese said. Participants are given prompts, like do you find this a compelling investment opportunity? and what are your top concerns? to assist in evaluating the companies.

By ranking the anonymous responses that come in, investors can start to drill down into those specific elements within this investment opportunity, Polese said.

Using natural language processing, the insights gathered are transformed into a quantitative score, which can determine the investment risk or opportunity.

While the platforms primary goal was to accurately predict investment success, one side effect has been the reduction of bias, she said. Traditionally, venture-capital funding has been very much a relationship-driven, network-driven business that can leave behind underrepresented founders without connections in the industry, Polese said.

When Polese first used the platform to pick investments about four years ago, she said 42% of the highest-scoring companies were founded or led by women. That result was not something we set out to achieve as a goal, [but] a side effect of reducing ingrained bias, which is an important element of this approach, she said.

The diligence process takes place over the course of a couple of weeks and is designed from the ground up to be virtual, remote, said Polese. It can be applied to companies in different stages, from start-ups to public offerings.

By scaling diligence this way, you dont have this tiny little funnel that only a few deals can get through, Polese said. Youd have a much wider funnel that then you can evaluate with more predictive accuracy.

Email: editors@barrons.com

Original post:

How Artificial Intelligence Could Lead to Better Investment Decisions - Barron's

The European Parliament announced the MEPs who will sit on its new special committees on artificial intelligence (AI), cancer and foreign interference in democratic processes.

The new committee on beating cancer will identify areas for action, legislation and other measures to help prevent and fight cancer, and look into the best ways to support research.

It has been set up following European Commission president Ursula von der Leyens announcement in April of a nine-month consultation among member states for her cancer plan.

The wide-ranging push focuses mainly on prevention, but it will also promote better data sharing, and there will be a research component, in the form of the cancer mission set to begin next year as part of Horizon Europe.

Joining the new 33 member cancer committee is Petra de Sutter, a former senator in Belgiums parliament, and a physician who ran the department for reproductive medicine at Ghent University. She currently chairs the parliament committee on the internal market and consumer protection.

The committee on AI meanwhile will study the impact and challenges of rolling out the technology, and propose a roadmap detailing what the objectives of the legislation should be.

The commission is pushing to be one of the first places in the world to regulate AI, aiming to devise rules and limits that parallel those set out in the general data protection regulation, promoting adoption of the technology, whilst also providing a proportionate regulatory framework. As of now, the EU executive has only released a white paper, spelling out preferred options for laws.

The committees ranks will include Romanias Dan Nica, Portugals Maria da Graa Carvalho and Spains Pilar del Castillo Vera, three members who also sit on ITRE, the committee on industry, research and energy.

Also joining the AI committee is Andrus Ansip, former European Commission vice president, and former holder of the digital single market portfolio.

The third new committee, focused on foreign interference, will review alleged breaches of democratic processes in the EU, including misinformation, and will identify areas where greater control over social media platforms may be required.

Each of the special committees is set up for one year only, with each having 33 members. The chairs and vice-chairs will be decided in September.

Parties agree on the allocation of committees between them, to make sure the chairs are a fair representation of the make-up of parliament. Each political group is allocated a number of seats on the new committees based on the number of MEPs they have in the chamber.

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European Parliament names MEPs on artificial intelligence, cancer plan and foreign interference committees - Science Business

Yasmina Darveniza, an investor at leading PropTech VC Round Hill Ventures, says AI can have a major impact in real estateThe amount of venture capital money flowing into UK artificial intelligence start-ups hit a record-breaking $3.2 billion in 2019, making it one of the hottest sectors to be in.

This financial boost, along with bolder algorithms, Big Data and better infrastructure, is bringing founders andfunders to the AI equation. Yet according to a recent report, 40 per cent of European firms classified as AI start-ups do not actually use artificial intelligence.

Is AI then just a fad or is it worth the hype?

AI makes it possible for human capabilities to be undertaken by technology at scale. While rules-based programs have existed since the 1950s, AI nowadays usually relates to machine learning providing systems withthe ability to automatically learn from data and improve from experience without being explicitly programmed.

This can be applied to a wide variety of prediction and optimisation challenges, from predicting when patients will get sick to teaching self-driving cars to understand their surroundings.

To utilise this technology, start-up founders need access to talent around applied AI, access to large and proprietary data training sets, and domain knowledge to provide deep insights into the opportunities within an industry. Founders need to identify a sizeable target market and understand the problem theyre trying to solve.

I see no better target market for AI applications than real estate. Not only is it the worlds largest and most important asset class, but also one of the last industries to adopt technological change.

A great example is Israeli start-upSkyline AI,which takes the guesswork out of investmentdecisions by training its technology on the mostcomprehensive data set for US multi-family assets.

Mining data from over 130 sources and analysing10,000-plus data points on each property forthe last 50 years, its tech estimates asset value,predicts future performance and discoversinvestment opportunities.

AI can also optimise both property developmenttime and cost. Nordic start-upSpacemakerAIisa development tool used to maximise the potentialof building sites. Property professionals canuse it to generate and assess billions of possiblesolutions to multi-building developments inhours analysing designs for a range of differentparameters such as sun exposure, noise pollutionand apartment size.

The company has partneredwith leading developers in Europe includingSkanska, OBOS, AF Gruppen and Bouygues tohelp them reduce critical planning time whileincreasing sellable space by up to double digits.

Using Big Data and machine learning algorithms,Iberian start-upCASAFARIenables a higherlevel of efficiency and transparency in assetmanagement. The software provides users withdownloadable historical and descriptive datasets for all property cases and is working tobuild the cleanest, most complete database in itsgeographies. Asset managers can use it to setdatadrivenrental prices and identify the best time tosell assets.

AI has almost unlimited potential across multipleindustries and especially real estate. Not everysolution requires it, but knowing how, when andwhere to effectively use the technology can be akey lever for start-ups and businesses alike.

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Artificial Intelligence: worth the hype? - BusinessCloud

The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature.

While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence.

Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks.

The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI). This proposition was surmised by the progenitors of AI in the 1950s, though it has been generally side-lined over the course of AIs history. However, developments in both neuroscience and more conventional AI make it fascinating to consider the issue anew.

The history of neuroscience has shown both tendencies from its inception, not least in terms of the alternative functions performed by the characteristic technologies of the AI field.

Understanding the complete impacts of this distinction needs eluding from the reductionist problematic that perpetuates to haunt philosophical discussions of neurosciences aspirations as a mode of inquiry

The early prospect, which will help to build machines possessing intelligence of humans, found inspiritment in three main directions.

Firstly, proof that the functioning of the human brain and nervous system, while astonishingly perplexed from a biological perspective, is predicated on elementary all-or-nothing procedures of the type that can facilely be copied by digital electronic circuits.

Secondly, the growth of symbolic logic and formal languages that are able to communicate immense components of higher mathematics, recommending that all human reasoning might be ultimately abbreviated to similar manipulating strings of symbols according to sets of rules. Such formal operations can probably easily be imitated by a digital computer.

Thirdly, the outlook of creating faster electronic calculating devices. With regard to this, developments since the 1950s have rarely been saddening. The density of switching elements of todays microchips surpasses that of neurons in the brain.

Artificial intelligence makes industrial machines and equipment precise, credible and self-healing, making strides calibrated performance imitating human action. AI incorporates robotic controls, vision-based sensing, and geospatial systems in order to automate advanced frameworks. It improves disease detection and prevention along with its treatment, amplifies engineering systems and handles self-organizing supply chains.

We, humans, are dependent on machines for decision-making for various processes like underwriting, recruitment, fraud detection, maintenance, etc. Real Core Energy deploys machine learning that assesses production as well as performance factors to better conduct oil drilling operations and investment decisions.

Though artificial intelligence has become indispensable in almost all fields today, the presiding approaches to artificial intelligence are based in false conceptions about the nature of the mind and of the brain as a biological organ.

Sadly, the superficial models of the brain and mind, which were the initial Kickstarter of artificial intelligence, have now become the paradigm for everything called cognitive science, as well as a huge part of neurobiology. It has become a standard protocol to levy methods, concepts, models and vocabulary from the domain of artificial intelligence, computer science onto the research of the brain and the mind. It is difficult to discover a scientific paper on these subjects which does not contain terms like computing, processing, circuits, storage and retrieval of information, encoding decoding etc.

Computational neuroscience connects human intelligence and artificial intelligence by developing theoretical models of the human brain for multiple studies on its functions, including vision, motion, sensory control, and learning.

Studies in human cognition are uncovering a deeper comprehension of our nervous system and its compound processing abilities. Models that provide high-level insights into memory, data processing, and speech/object recognition are simultaneously reshaping AI.

The integration of human intelligence with artificial intelligence will evolve computers into superhumans or humanoids that go far beyond human abilities. However, it needs computing models that combine visual and natural language processing, just how the brain functions, for comprehensive communication.

Neuroscience has made significant contributions to strengthen AI research and gain its increasingly important relevance. In planning for the future amalgamation of the two fields, it is essential to value that the past contributions of neuroscience to AI have hardly consisted of a simple shift of complete solutions which can be simply re-implemented in machines. Rather, neuroscience has often been useful in a precise way, facilitating algorithmic-level questions about qualities of animal learning and intelligence of interest to AI researchers and offering initial drives toward applicable mechanisms.

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The Amalgamation of Human Brain and Artificial Intelligence - Analytics Insight

To appraise the trends of Artificial Intelligence (AI) 2020, we have to recall that 2018 and 2019 saw a large number of platforms, applications, and devices that depend on artificial intelligence and machine learning.

Such technology patterns laid huge implications on programming and the Internet business. Moreover, its impacts on fields like healthcare services, assembling, manufacturing, agriculture, and automobile are valuable.

The advancement of ML and AI-related advancements will have a long journey in 2020, or considerably further.

As the hardware and skill expected to deploy AI become less expensive and progressively accessible, we will begin to see it utilized in an increasing number of tools, gadgets, and devices. In 2019 were already used to running applications that give us AI-fueled predictions on our PCs, phones and watches.

As the following decade draws near and the expense of hardware and software keeps on falling, AI devices will progressively be embedded into our vehicles, household appliances, and workplace tools. Augmented by innovation, for example, augmented reality displays, and paradigms like the cloud and Internet of Things, this year we will see an ever increasing number of devices of each shape and size beginning to think and learn for themselves.

Artificial intelligence for digital marketing takes into account uncommon change via social media. It forecasts all day, every day chatbots, analyzes data and patterns, oversees custom feeds to produce content, looks for content points, makes custom based personalized content and makes recommendations when required.

This trend is driven by the success of web giants like Amazon, Alibaba, and Google, and their capacity to provide personalized experiences and recommendations. Artificial intelligence permits suppliers of products and enterprises to rapidly and precisely project a 360-degree view on clients in real-time as they cooperate through online portals and mobile applications, rapidly figuring out how their predictions can accommodate our needs and wants with ever-increasing accuracy.

Similarly, as pizza delivery companies like Dominos will realize when we are well on the way to want pizza, and ensure the Order Now button is before us at the right time, each other industry will turn out solutions planned for offering personalized customer experiences at scale.

The AI-based Deep Learning innovation detects signs of the perplexing five finger movements in real-time. The sensor fix is joined to the clients wrist. This single stranded electronic skin sensor tracks human development from a distance in real-time with a virtual 3D hand that reflects the original movement.

Maybe considerably more unsettlingly, the rollout of facial recognition technology is just prone to escalate as we move into the next decade. Not simply in China (where the government is taking a look at methods of making facial recognition obligatory for accessing services like communication networks and public transport) yet around the globe. Enterprises and governments are progressively putting resources into these techniques for telling what our identity is and deciphering our movement and behaviour.

Theres some pushback against this this year, San Francisco turned into the first significant city to boycott the utilization of facial recognition technology by the police and civil organizations, and others are probably going to follow in 2020. However, the topic of whether individuals will at last start to acknowledge this interruption into their lives, in return for the increased security and convenience it will bring, is probably going to be a hotly discussed subject of this year.

As the AI system is surmising, it can intensify the carbon impression. A variant range of data sets can be utilized from cell phone location information to estimate electrical load. This engineering can consider information from the geographical area and beat conventional forecasting methods by more than 2 times.

A few things, even in 2020, are likely best left to people. Any individual who has seen the present state-of-the-art in AI-generated music, poetry or storytelling is probably going to concur that the most refined machines despite everything have some best approach until their output will be as charming to us as the best that humans can produce. Notwithstanding, the impact of AI on entertainment media is probably going to increase. This year we saw Robert De Niro de-aged before our eyes with the help of AI, in Martin Scorseses epic The Irishman, and the utilization of AI in making brand new visual effects and trickery is probably going to turn out to be progressively normal.

In video games, AI will keep on being utilized to create challenging, human-like opponents for players to compete against, as well as to powerfully alter gameplay and difficulty with the goal that games can keep on offering a convincing challenge for gamers of all expertise levels. And keeping in mind that totally AI-produced music may not be for everyone, where AI exceeds expectations is in making dynamic soundscapes, consider brilliant playlists on services like Spotify or Google Music that match tunes and tempo to the temperament and pace of our everyday lives.

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Latest Trends in the Field of Artificial Intelligence - Analytics Insight

Artificial intelligence, seen as the cure-all for a plethora of enterprise shortfalls, from chatbots to better understanding customers to automating the flow of supply chains. However, it is delivering the most impressive results to information technology departments themselves, enhancing the performance of systems and making help desks more helpful. At the same time, there's a recognition that AI efforts -- and involvement -- need to expand beyond the walls of IT across all parts of the enterprise.

This is one of the takeaways of a recentsurveyof 154 IT and business professionals at companies with at least one AI-related project in general production, conducted and published by ITPro Today, InformationWeek and Interop. Among those survey respondents with at least one AI application in general production, those with "excellent" and "very good" results comprise 64% of the group -- excellent results account for 23% of respondents and 41% report very good results.

Looking at the characteristics of the successful AI leaders, top use operational cases include predictive maintenance (54%), Inventory and supply chain optimization (50%) and manufacturing analytics (50%). At the same time, many respondents see the greatest benefits going right to the IT organization itself -- 63% say they hope to achieve greater efficiencies within IT operations. Another 45% aim for improved product support and customer experience. Another 29% seek improved cybersecurity systems.

The top IT use case is security analytics and predictive intelligence, cited by 71% of AI leaders. Another 56% say AI is helping with the help desk, while 54% have seen a positive impact on the productivity of their departments. "While critics say that the hype around AI-driven cybersecurity is overblown, clearly, IT departments are desperate to solve their cybersecurity problems, and, judging by this question in our survey, many of them are hoping AI will fill that need," relates Sue Troy, author of the survey report. "On the help desk, meanwhile, AI tools are using predictive analytics to improve decision-making around incident management and demand planning. And AI is being used for help desk chatbots and intelligent search recommendations."

There is a significant need for AI expertise and skills. More than two in three successful AI implementers, 67%, say they are seeing shortages of machine learning and data modeling skills, while 51% seek greater data engineering expertise. Another 42% say compute infrastructure skills are in short supply.

Security ranks as the top concern among successful AI implementers, with 44% citing this as their leading issue. Model transparency or the degree to which the inner workings of AI algorithms are visible to users of the technology was the second-leading concern, as cited by 36%, "Model transparency is an especially thorny issue," Troy relates. "A high level of transparency can help mitigate bias and promote trust of the system, but it carries concerns that model explanations can be hacked, making the tech more vulnerable to attack." Built-in bias follows among 33%, as well as concerns about unexpected or unusable outcomes with 33%.

When asked about specific AI technologies they expected to incorporate into their workplaces in the next six to 24 months, machine learning tops the list among successful AI sites, cited by 55%. Deep learning follows at 53%, and intelligent robotic process automation (RPA) rounds out the top three at 52%.

Successful AI projects take time to roll out. The typical AI project took six months to a year to complete, close to half of successful AI implementers (47%) indicate. Close to one-third, 32%, report taking more than year. Only 21% were able to wrap up AI initiatives in less than six months. The costs of these projects were kept in line -- 45% said the project cost about as much as planned, while 25% said the costs ran over budget. By contrast, 40% of those with less-successful AI initiatives report cost overruns. "The more experienced IT practitioners are with AI, the better able they are to project costs and avoid going over budget," Troy says.

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The main beneficiaries of artificial intelligence success are IT departments themselves - ZDNet