Arnold Schwarzeneggers Terminator character is a top example of artificial intelligence, according to nearly one-in-five confused Britons.

Some 19% believed Arnie's cyborg assassin from the 1984 blockbuster film was a prime illustration of the technology.

The revelation stands in stark contrast to Prime Minister Boris Johnson's claim in a speech last week that Britain could lead the world in AI.

A survey carried out into people's understanding of artificial intelligence lays bare how much work remains to be done.

In the hit I'll be back science-fiction movie, the T-800 Terminator is sent back in time from 2029 to 1984 to kill Sarah Connor, played by Linda Hamilton.

Her son will one day become a saviour against machines in a post-apocalyptic future and needs to be destroyed.

AI pioneer Yoshua Bengio told the BBC in October 2019 he was not a fan of the Terminator films.

"They paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future," he said.

"We are very far from super-intelligent AI systems and there may even be fundamental obstacles to get much beyond human intelligence."

But for 19% Britons, the film is a chilling demonstration of what AI can offer.

The reality is more useful predictive texting on mobile phones uses AI, as do apps like Uber and Google Maps.

However, just 41% of people questioned believed they had encountered AI in the past three months.

Researchers uncovered big gender gaps, with 69% of women saying they did not know when they last encountered AI if they ever had.

Some 51% of men thought they had used it in the past 12 weeks.

The online Populus study of 1,093 adults was carried out for communications agency Zinc Network.

Executive director Louis Brooke said: The Government has laid out an ambitious agenda for AI, seeking to turn the UK into a world leader in this area.

AI will play a vital role in helping the UK exit lockdown and overhauling health, education, travel and the workplace.

"Yet this data shows public understanding of AI is chronically low, particularly amongst women.

"For the public to buy into new uses for AI technologies, it will be vital to ensure that innovations are well understood, and benefit those who may be the most sceptical.

Some of those quizzed readily understood the technology, saying they thought it included any sort of robot that can react to its surroundings and doesn't need programming and chat bots used by companies to deal with customer service queries.

But others were more fearful of AI's potential to oust humans from the workplace.

One described it as work done by machines replacing humans and another as creepy Japanese humanoids.

Others totally missed the point, according to researchers, with responses including artificial insemination, as with cows and other animals for breeding and aliens.

See original here:
Artificial intelligence isn't all about the Terminator, tech sceptics are warned - Mirror Online

Content Intelligence Market

The sudden challenges created by the ongoing COVID-19 are captured effectively to exhibit the long term growth projections in the MRFR report on Content Intelligence Market. The growth sectors of the Content Intelligence Market are identified with precision for a better growth perspective.

According to MRFR analysis, the global content intelligence market was valued at USD 280.8 million in 2018 and is expected to reach USD 2,653.2 million by 2025 growing at a CAGR of 33.2% during the forecast period. Factors driving the growth of the market include surge in the adoption of enterprise content management (ECM) systems and increasing demand for analytical solutions to identify the target audience and increase the conversion rate of marketers. Additionally, growing demand for personalized content services to improve the customer experience is one of the revenue-generating pockets in the content intelligence market during the forecast period.

FREE [emailprotected] https://www.marketresearchfuture.com/sample_request/8382

The global content intelligence market has been segmented based on component, deployment mode, organization size, end user, and region.

By component, the global content intelligence market has been bifurcated into solution and service. Furthermore, based on service, the content intelligence market has been subdivided into professional services and managed services. The professional services segment has been categorized into content engineering services, content strategy services, and other support services. The solution segment accounted for the larger market share in 2018. The market growth is attributed to increasing demand for analytical solutions to improve customer experience. On the other hand, the services segment is anticipated to exhibit the higher CAGR during the forecast period.

By deployment mode, the market has been segmented into cloud, on-premise, and hybrid. The cloud segment is presumed to be the largest segment in the market owing to the availability of seamless cloud-based solutions that can be easily integrated with systems and mobile devices. However, the hybrid segment is expected to register then highest CAGR during the forecast period; the market growth is attributed to a secured mode of content operations.

Based on end user, the global content intelligence market has been categorized into BFSI, education, healthcare, government, manufacturing, IT and telecommunication, media and entertainment, and others. The BFSI segment accounted for the largest market share in 2018 owing to the rising adoption of automated content-centric processes in financial services. However, the media and entertainment segment is presumed to register the highest CAGR during the forecast period.

Regional Analysis

By region, the global content intelligence market has been segmented into North America, Europe, Asia-Pacific, the Middle East and Africa, and South America. North America accounted for the largest market in 2018 owing to the presence of a number of vendors and early adoption of AI and ML-based content solutions.

Europe gained the second spot in the content intelligence market due to the increasing number of startups offering content analytics solution in Western and Central Europe and adoption of robotic process automation solution that enables the enterprises to determine the target customers and improve the conversion rate for business growth.

Key Players

The key players in the global content intelligence market are OpenText (Canada), Socialbakers (Prague), Atomic Reach (Canada), M-Files (Finland), Content Insights (US), Knotch (US), OneSpot (US), Abbyy (Russia), Curata, Inc (US), BuzzSumo (UK), Concured (UK), Acrolinx GmbH, (Germany), Adobe (US), TrackMaven, Inc. (US), Conductor (US), Vennli (US), Persado (US), Ceralytics (US), Idio Ltd (UK), and Smartlogic (US).

More [emailprotected]

About Market Research Future:

At Market Research Future (MRFR), we enable our customers to unravel the complexity of various industries through our Cooked Research Reports (CRR), Half-Cooked Research Reports (HCRR), Raw Research Reports (3R), Continuous-Feed Research (CFR), and Market Research and Consulting Services.

Contact:

Market Research Future

+1 646 845 9312

Email:[emailprotected]

Read more:
Adoption of Artificial Intelligence and Machine Learning solutions is likely to Drive the Growth of the Content Intelligence Market (SARS-CoV-2,...

The Global Artificial Intelligence Applications for Smart Cities Market report presents an in depth analysis about the major segments covering all the applications, top products, top companies and key geographies. Also report on Global Artificial Intelligence Applications for Smart Cities Market solutions market size is expected to grow in billions from base year 2019 to 2025 at Compound Annual Growth Rate in terms of revenue during the forecast period. Report also covers some major driving factors for the market which are the growing initiatives for the promotion of the Global Artificial Intelligence Applications for Smart Cities Market. Furthermore, technological trends, new innovations, governing an industry are some factors impacting development of the market.

This study covers following key players:Amazon (Rekognition)BonsaiIBM Corp.One ConcernHuaweiC3.aiSogetiCortiBeyond Limits

Request a sample of this report @ https://www.orbisresearch.com/contacts/request-sample/4578983?utm_source=Biren

All these developments would take the industry in the long term growth. In addition, report provides upcoming industry solutions for the Global Artificial Intelligence Applications for Smart Cities Market. Report also presents driving factors which are influencing the growth of the market. However, there can be some challenges and risk to face for the participants which may hamper the growth of the market during the forecast period. In addition, report also covers the vendors with complete overview of their company profile, market size, and sales analysis on the basis of regions that would offer high growth for the vendors in the market. Key players and Market leaders are competencies and capacities of these companies in terms of production as well as sustainability and prospects of the market.

Global Artificial Intelligence Applications for Smart Cities Market is highly split on the basis of key segments such as product type, application, end users, key companies and key regions. And report explains various strategies used by major players such as acquisitions, partnerships, joint ventures, agreements, expansions, new product launches and others to increase their footprints in the Global Artificial Intelligence Applications for Smart Cities Market. The report includes market shares of global market for global regions such as Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Access Complete Report @ https://www.orbisresearch.com/reports/index/global-artificial-intelligence-applications-for-smart-cities-market-growth-status-and-outlook-2020-2025?utm_source=Biren

Market segment by Type, the product can be split intoMobile Application Based Taxi ServicesTaxi Services

Market segment by Application, split intoBig CityMedium and Small Cities

Some essential tools have studied such as SWOT analysis, PESTEL analysis and Value chain analysis for the quantitative study of the market to help the participants to explain an overview of the Global Artificial Intelligence Applications for Smart Cities Market. This report is suitable for any stakeholders investing in the market. Thus report provides strategic analysis to the vendor to expand their business at large scale across the globe. Also report covers all the challenges so that users will be aware of the situations while investing in the market. Moreover, report covers all the quantitative and qualitative study of the market on the basis past and current data.

For Enquiry before buying report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/4578983?utm_source=Biren

About Us:

Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: USA: +1 (972)-362-8199 | IND: +91 895 659 5155

Continue reading here:
Global Artificial Intelligence Applications for Smart Cities Market Expected To Reach Highest CAGR By 2025: Amazon (Rekognition), Bonsai, IBM Corp.,...

Artificial intelligence - or AI for short - is technology that enables a computer to think or act in a more 'human' way. It does this by taking in information from its surroundings, and deciding its response based on what it learns or senses.

It affects the the way we live, work and have fun in our spare time - and sometimes without us even realising.

AI is becoming a bigger part of our lives, as the technology behind it becomes more and more advanced. Machines are improving their ability to 'learn' from mistakes and change how they approach a task the next time they try it.

Some researchers are even trying to teach robots about feelings and emotions.

You might not realise some of the devices and daily activities which rely on AI technology - phones, video games and going shopping, for example.

Some people think that the technology is a really good idea, while others aren't so sure.

Just this month, it was announced that the NHS in England is setting up a special AI laboratory to boost the role of AI within the health service.

Announcing that the government will spend 250 million on this, Health Secretary Matt Hancock said the technology had "enormous power" to improve care, save lives and ensure doctors had more time to spend with patients.

Read on to find out more about AI and let us know what you think about it in the comments below.

What does AI do?

AI can be used for many different tasks and activities.

Personal electronic devices or accounts (like our phones or social media) use AI to learn more about us and the things that we like. One example of this is entertainment services like Netflix which use the technology to understand what we like to watch and recommend other shows based on what they learn.

It can make video games more challenging by studying how a player behaves, while home assistants like Alexa and Siri also rely on it.

It has been announced that NHS England will spend millions on AI in order to improve patient care and research

AI can be used in healthcare, not only for research purposes, but also to take better care of patients through improved diagnosis and monitoring.

It also has uses within transport too. For example, driverless cars are an example of AI tech in action, while it is used extensively in the aviation industry (for example, in flight simulators).

Farmers can use AI to monitor crops and conditions, and to make predictions, which will help them to be more efficient.

You only have to look at what some of these AI robots can do to see just how advanced the technology is and imagine many other jobs for which it could be used.

Where did AI come from?

The term 'artificial intelligence' was first used in 1956.

In the 1960s, scientists were teaching computers how to mimic - or copy - human decision-making.

This developed into research around 'machine learning', in which robots were taught to learn for themselves and remember their mistakes, instead of simply copying. Algorithms play a big part in machine learning as they help computers and robots to know what to do.

An algorithm is basically a set of rules or instructions which a computer can use to help solve a problem or come to a decision about what to do next.

From here, the research has continued to develop, with scientists now exploring 'machine perception'. This involves giving machines and robots special sensors to help them to see, hear, feel and taste things like human do - and adjust how they behave as a result of what they sense.

The idea is that the more this technology develops, the more robots will be able to 'understand' and read situations, and determine their response as a result of the information that they pick up.

Why are people worried about AI?

Many people have concerns about AI technology and teaching robots too much.

Famous scientist Sir Stephen Hawking spoke out about it in the past. He said that although the AI we've made so far has been very useful and helpful, he worried that if we teach robots too much, they could become smarter than humans and potentially cause problems.

Sir Stephen Hawking spoke out about AI and said that he had concerns that the technology could cause problems in the future

People have expressed concerns about privacy too. For example, critics think that it could become a problem if AI learns too much about what we like to look at online and encourages us to spend too much time on electronic devices.

Another concern about AI is that if robots and computers become very intelligent, they could learn to do jobs which people would usually have to do, which could leave some people unemployed.

Other people disagree, saying that the technology will never be as advanced as human thoughts and actions, so there is not a danger of robots 'taking over' in the way that some critics have described.

What do you think about AI? Do you think that it is a good thing or a bad thing? Let us know in the comments below.

Read more:
What is AI? What does artificial intelligence do? - CBBC ...

Artificial Intelligence in Medicine publishes original articles from a wide variety of interdisciplinary perspectives concerning the theory and practice of artificial intelligence (AI) in medicine, medically-oriented human biology, and health care.

Artificial intelligence in medicine may be characterized as the scientific discipline pertaining to research studies, projects, and applications that aim at supporting decision-based medical tasks through knowledge- and/or data-intensive computer-based solutions that ultimately support and improve the performance of a human care provider.

Artificial Intelligence in Medicine considers for publication manuscripts that have both:

Potential high impact in some medical or healthcare domain; Strong novelty of method and theory related to AI and computer science techniques.

Artificial Intelligence in Medicine papers must refer to real-world medical domains, considered and discussed at the proper depth, from both the technical and the medical points of view. The inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly recommended.

Artificial Intelligence in Medicine is looking for novelty in the methodological and/or theoretical content of submitted papers. Such kind of novelty has to be mainly acknowledged in the area of AI and Computer Science. Methodological papers deal with the proposal of some strategy and related methods to solve some scientific issues in specific domains. They must show, usually through an experimental evaluation, how the proposed methodology can be applied to medicine, medically-oriented human biology, and health care, respectively. They have also to provide a comparison with other proposals, and explicitly discuss elements of novelty. Theoretical papers focus on more fundamental, general and formal topics of AI and must show the novel expected effects of the proposed solution in some medical or healthcare field.

Following the information explosion brought by the diffusion of Internet, social networks, cloud computing, and big-data platforms, Artificial Intelligence in Medicine has broadened its perspective.Particular attention is given to novel research work pertaining to:

If you are considering submitting to Artificial Intelligence in Medicine, make sure that your paper meets the quality requirements mentioned above. English exposition must also be clear and revised with due care. Authors are kindly requested to revise their manuscripts with the help of co-authors that are fluent in English or language editing services before submitting their contribution. Papers written in poor English are likely to be rejected.

The mere application of well-known or already published algorithms and techniques to medical data is not regarded as original research work of interest for Artificial Intelligence in Medicine, but it may be suitable for other venues.

Artificial Intelligence in Medicine features the following kinds of papers:

Special Issues are regularly published and included among regular issues. Artificial Intelligence in Medicine is looking for special issues about current theoretical/methodological research or convincing applications related to AI in medicine. Special Issues compiled by one or more guest editors who are outstanding experts on the selected topic.

Artificial Intelligence in Medicine does not publish conference volumes or conference papers. However, selected and high-quality research results presented earlier at conferences may be published in Artificial Intelligence in Medicine, in the form of a thoroughly revised (rephrased) and extended (including new research results) original research paper.

Information for authors and further details about the editorial process can be found in the Guide for Authors section of the Artificial Intelligence in Medicine web page.

Originally posted here:
Artificial Intelligence in Medicine | Journal ...

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.

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

Text size

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

The rest is here:
How Artificial Intelligence Could Lead to Better Investment Decisions - Barron's

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.

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

VALENCIA, Spain, July 6,2020 /PRNewswire/ --As spectacular and futuristic as it may seem, using Artificial Intelligence to automatically analyze embryos in a standardized way to improve pregnancy rates is already a reality. This is confirmed by an IVIRMA Global study entitled 'A universal algorithm is available in last generation time-lapse incubators: embryo score provided by the KIDScoreD5 is strongly correlated with chromosomal status and clinical outcomes'.

IVIRMA Global has already participated in the development of the EmbryoScope (incubator with time-lapse technology) from its beginnings, helping in its evolution and laying the foundations for automatic embryo selection. In its latest development, EmbryoScope presents its newest software system, KIDScoreD5, which automatically performs embryo selection and classification.

The study has been carried out over the last three years and has become the most extensive case study in the history of embryology to date (more than 20,000 embryos and more than 3,000 patients have been analyzed). In the study, IVIRMA Global has demonstrated that universal, standardized and automatic embryo selection is a reality for the field of embryology. As the study's principal researcher, Dr. Marcos Meseguer, scientific supervisor of IVI Valencia, comments, "The KIDScoreD5 system automatically classifies embryos using Artificial Intelligence, it detects and evaluates all the steps in the development of the embryo and also classifies its morphology".

Dr. Meseguer points out that, "We have seen that the KIDScoreD5 system makes an assessment to distinguish between those embryos that are more likely to be chromosomally normal, called euploid embryos, and those that are not, called aneuploid embryos." Based on the score the system gives each embryo, we know its probability of gestation and the possibility of taking a healthy baby home.

The KIDScoreD5 system analyzes the embryos automatically classifying them from one to ten depending on their quality and morphology. Since automated embryo selection is more accurate than manual selection, the probability of a successful pregnancy is directly linked to the percentage score and, therefore, the patient has a greater chance of success.

Main values of the study and the KIDScoreD5 system

About IVIRMA Global

IVI was founded in 1990, as the first medical institution in Spain fully dedicated to assisted reproduction. Since then it has helped with the birth of more than 200,000 babies thanks to the application of the latest technologies. In early 2017, IVI merged with RMANJ, becoming the largest assisted reproduction group in the world. It currently has more than 65 clinics in 9 countries and is the leading centre for reproductive medicine. http://www.ivi.es- http://www.rmanetwork.com.

For more information:

SOURCE IVIRMA Global

http://www.ivi.es

Read the rest here:
Artificial intelligence in embryo selection: a reality thanks to IVIRMA Global - PRNewswire

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.

Here is the original post:
The main beneficiaries of artificial intelligence success are IT departments themselves - ZDNet