DUBLIN--(BUSINESS WIRE)--The "Innovations in Artificial Intelligence, Cloud, Blockchain, and Analytics" report has been added to ResearchAndMarkets.com's offering.

This edition of IT, Computing and Communications (ITCC) TechVision Opportunity Engine (TOE) provides a snapshot of the emerging ICT led innovations in artificial intelligence, machine learning, cloud, and analytics. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as banking, oil & gas, healthcare, life sciences, and industrial sectors.

ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more.

Innovations in ICT have deeply permeated various applications and markets. These innovations have profound impact on a range of business functions for computing, communications, business intelligence, data processing, information security, workflow automation, quality of service (QoS) measurements, simulations, customer relationship management, knowledge management functions and many more.

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Companies Mentioned

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Innovations in Artificial Intelligence, Cloud, Blockchain, and Analytics, 2019: Advances in AI, Blockchain, and Business Intelligence -...

DUBLIN--(BUSINESS WIRE)--The "Innovations in Artificial Intelligence, Natural Language Processing, IoT, and Analytics" report has been added to ResearchAndMarkets.com's offering.

This edition of IT, Computing and Communications (ITCC) TechVision Opportunity Engine (TOE) provides a snapshot of the emerging ICT led innovations in artificial intelligence, machine learning, natural language processing (NLP), and advanced analytics. This issue focuses on the application of information and communication technologies in alleviating the challenges faced across industry sectors in areas such as retail, BFSI, manufacturing, supply chain and industrial sectors.

ITCC TOE's mission is to investigate emerging wireless communication and computing technology areas including 3G, 4G, Wi-Fi, Bluetooth, Big Data, cloud computing, augmented reality, virtual reality, artificial intelligence, virtualization and the Internet of Things and their new applications; unearth new products and service offerings; highlight trends in the wireless networking, data management and computing spaces; provide updates on technology funding; evaluate intellectual property; follow technology transfer and solution deployment/integration; track development of standards and software; and report on legislative and policy issues and many more.

The Information & Communication Technology cluster provides global industry analysis, technology competitive analysis, and insights into game-changing technologies in the wireless communication and computing space. Innovations in ICT have deeply permeated various applications and markets. These innovations have profound impact on a range of business functions for computing, communications, business intelligence, data processing, information security, workflow automation, quality of service (QoS) measurements, simulations, customer relationship management, knowledge management functions and many more.

Key Topics Covered

For more information about this report visit https://www.researchandmarkets.com/r/2v1ek7

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Innovations in Artificial Intelligence, Natural Language Processing, IoT, and Analytics, 2019 Study - ResearchAndMarkets.com - Business Wire

How maythe moral integrity and agency of military personnel be preserved and enhanced when artificial intelligence is implemented in practices of war?

Congratulations to Greg Reichberg on funding from the SAMKUL project of the Research Council of Norway for a four-yearproject with th title: Warring with Machines:Military Applications of Artificial Intelligence and the Relevance of Virtue Ethics. The PRIO team also consists of Henrik Syse and Mareile Kaufmann, and in addition a full time PhD position.

Artificial intelligence plays an ever-expanding role in the context of war. This project aims to determine how the moral integrity and agency of military personnel may be preserved and enhanced when artificial intelligence is implemented in practices of war.

The project will pursue this goal from the perspective of virtue ethics, philosophy of action and mind, and applied military ethics, in close dialogue with institutional stakeholders as well as technologists and representatives from cognitive neuroscience. Its three main research questions are as follows:

The project is built up around a unique institutional collaboration between leading national and international research institutions within the fields of military ethics, the philosophy of mind, and artificial intelligence research, as well as key military training institutions and technology manufacturers.

In addition to the PRIO team, the project will have the following externalmembers:

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Military Applications of Artificial Intelligence - Ethics Project Now Funded - Peace Research Institute Oslo (PRIO)

Artificial Intelligence is being accused of fueling inequality and climate change as revealed by a new report.

Recently, a paper was published by the AI Now Institute with a title AI Now 2019 Report and it is highlighting the societal impacts of artificial intelligence and is also putting in front some recommendations for the tech industry and policymakers.

The artificial intelligence is being controlled by the people who already have power and is promoting inequality, and disempowering people who lack power.

According to the claims by AI Now, the artificial intelligence industry is promoting the mistreatment and discrimination of workers as the tech companies are moving more towards facial recognition technologies and ignoring the facts that these energy-running A.I. systems are the reason behind the increase of carbon dioxide in the environment.

According to the co-founder of AI Now Kate Crawford, her organization is concerned about the effects of the recognition technology that is promoting to determine the personality or emotional state of a person via their facial expression and this type of technology is being used by vet job applicants, to track students and to gather data on the emotional states of shoppers inside the stores. So the organization is asking for a ban on this technology before it can be used in furthermore critical decisions like hiring.

In this report AI NOW 2019, the researchers also recommend Senator Bernie Sanders to make a ban on police facial recognition as a part of his presidential campaign to help stop the misuse before its too late.

Another major concern raised via this report is the impact of power-hungry artificial intelligence programs on the environment and AI Now also displayed a report of a group of University of Massachusetts, Amherst which determined that the energy consumed by A.I. training model produced 600,000 pounds of carbon dioxide emissions and due to this reason the only solution provided by the researchers of AI was to put completely end the usages of carbon dioxide emitting technology to stop the increase of climate change.

This report also highlights the issue of Biometric Information Privacy as it can be used to track a person easily so the recommendations also include the enabling of the Biometric Information Privacy act so that legal action can be taken against the collection of biometric information by a person or company without consent.

Another issue raised in this report was the lack of diversity in the tech industry, as the tech industry is slowly squeezing out the involvement of human employees which means the tech works cant speak about the ethical concerns regarding their work to A.I. enabled systems as most of the department decisions are now being down by the A.I. enabled systems which are inaccessible to the workers and the public.

According to the report, three municipalities in the United States have already banned the use of facial recognition from the government and the San Francisco, Oakland, and Somerville, Massachusetts have stalled its use so far.

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Read next: The Emerging Jobs to look out for in 2020 According to LinkedIn

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Artificial Intelligence might be a factor behind the Climate Change - Digital Information World

AI2 CEO Oren Etzioni spakes at the Technology Alliances AI Policy Matters Summit. (GeekWire Photo / Monica Nickelsburg)

Artificial intelligence is here, its just the beginning, and its time to start thinking about how to regulate it.

Those were the takeaways from the Technology Alliances AI Policy Matters Summit, a Seattle event that convened experts and government officials for a conversation about artificial intelligence. Many of those experts agreed that the government should start establishing guardrails to defend against malicious or negligent uses of artificial intelligence. But determining what shape those regulations should take is no easy feat.

Its not even clear what the difference is between AI and software, said Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, on stage at the event. Where does something cease to be a software program and become an AI program? Google, is that an AI program? It uses a lot of AI in it. Or is Google software? How about Netflix recommendations? Should we regulate that? These are very tricky topics.

Regulations written now will also have to be nimble enough to keep up with the evolving technology, according to Heather Redman, co-founder of the venture capital firm, Flying Fish Ventures.

Weve got a 30-40 year technology arc here and were probably in year five, so we cant do a regulation that is going to fix it today, she said during the event. We have to make it better and go to the next level next year and the next level the year after that.

With those challenges in mind, Etzioni and Redman recommend regulations that are tied to specific use cases of artificial intelligence, rather than broad rules for the technology. Laws should be targeted to areas like AI-enabled weapons and autonomous vehicles, they said.

My suggestion was to identify particular applications and regulate those using existing regulatory regimes and agencies, Etzioni said. That both allows us to move faster and also be more targeted in our application of regulations, using a scalpel rather than a sledgehammer.

He believes the rules should include a mandatory kill switch on all AI programs and requirements that AI notify users when they are not interacting with a human. Etzioni also stressed the importance of humans taking responsibility for autonomous systems, though it isnt clear whether the manufacturer or user of the technology will be liable.

Lets say my car ran somebody over, he said. I shouldnt be able to say my dog ate my homework. Hey I didnt do it, it was my AI car. Its an autonomous vehicle. We have to take responsibility for our technology. We have to be liable for it.

Redman also sees the coming tide of A.I. regulation as a business opportunity for startups seeking to break into the industry. Her venture capital firm is inundated with startups pitching an A.I. and M.L. first approach but Redman said there are two other related fields, or stacks as she describes them, that companies should be exploring.

If you talk to somebody on Wall Street, they dont care what tech stack theyre running their trading on theyre looking at new evolutions in law and policy as big opportunities to build new businesses or things that will kill existing businesses, she said.

From a startup perspective, if youre not thinking about the law and policy stack as much as youre thinking about the tech stack, youre making a mistake, Redman added.

But progress toward a regulatory framework has been slow at the local and federal level. In the last legislative session, Washington state almost became one of the first to regulate facial recognition, the controversial technology that is pushing the artificial intelligence debate forward. But the bill died in the state House. Lawmakers plan to introduce data privacy and facial recognition bills again next session.

Redman said shes disappointed Washington state wasnt a first-mover on AI regulation because the company is home to two of the tech giants consumers trust most with their data: Amazon and Microsoft. Amazon is in the political hot seat along with many of its tech industry peers but the Seattle tech giant has not been implicated in the types of data privacy scandals plaguing Facebook.

We are the home of trusted tech, Redman said, and we need to lead on the regulatory frameworks for tech.

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Tech experts agree its time to regulate artificial intelligence if only it were that simple - GeekWire

DUBLIN--(BUSINESS WIRE)--The "Innovations in Wound Care, Ophthalmic Devices, and Artificial Intelligence-enabled Diagnostics" report has been added to ResearchAndMarkets.com's offering.

The latest issue of Advanced MedTech TechVision Opportunity Engine (TOE) profiles some of the innovative technologies across the medical devices and imaging industry.

This issue of AMDT TOE profiles novel catheter devices, wound imaging and wound healing solutions, innovative diagnostic devices using artificial intelligence algorithms, an innovative drug delivery device, novel technologies such as CO2 laser radiation and photobiomodulation for treatment of ophthalmic conditions. This issue also profiles novel catheters and catheter securing device, innovative polymeric implant material, and a sinus dilation device that has obtained FDA approval.

The Advanced MedTech TOE analyzes and reports new and emerging technologies; advances in R&D, product development and regulatory matters specifically related to the areas of CT, MRI, NM, PET, ultrasound, X-ray, neurology, ophthalmology, respiratory/anesthesia, wound care and management, surgical tools and instrumentation, drug delivery, orthopedics, endoscopy, cardiology, and monitoring.

In addition, relevant developments in fusion technologies, functional imaging technology, interventional cardiology and image guided surgery and healthcare IT related areas such as PACS, medical information storage, and disaster recovery/business continuance will also be covered.

Medical devices and imaging technology and innovation research covers cutting-edge global developments in medical devices and imaging sectors such as biosensors, biomaterials, biomechanics, microtechnologies, nanotechnologies, assistive technologies, and imaging technologies and platforms.

Key Topics Covered

For more information about this report visit https://www.researchandmarkets.com/r/x4kqjw

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Latest Innovations in Wound Care, Ophthalmic Devices, and Artificial Intelligence-enabled Diagnostics, 2019 Research Report - ResearchAndMarkets.com -...

The "Innovations in Radar-on-Chip, Wireless Electric Vehicle Charging Systems, Artificial Intelligence, and In-vehicle Sensing Solutions" report has been added to ResearchAndMarkets.com's offering.

This Mobility TechVision Opportunity Engine (TOE) profiles advancements in radar-on chip, electric vehicle charging, AI chipsets, 5G communication, wearables, in-vehicle sensing and LiDAR solutions, autonomous vehicles, connected vehicles, and cybersecurity.

The purpose of the Mobility Technology TOE is to raise awareness of global technology innovations in self-propelled ground-based mobile platforms that are not only technically significant but potentially offering commercial value. Each monthly TOE provides subscribers valuable descriptions and analyses of 10 noteworthy innovations. The main focus is on highway-licensed motor vehicles (light, medium and heavy).

Passenger cars, trucks, buses, motorcycles, scooters and railway locomotives are within the product scope, energized by any fuel. Many of the innovations concern powertrains (internal combustion engines, turbines, battery electrics, fuel cell electrics, hybrid-electrics), as well as drivetrains (including transmissions), interiors seating and displays, advanced materials as for body/chassis, wireless connectivity, and self-driving technology that is currently receiving so much attention. The A&T TOE outlines and evaluates each innovation, notes which organizations and developers are involved, projects the likely timing for commercialization, furnishes a patent analysis and provides valuable strategic insights for industry stakeholders.

The Advanced Manufacturing and Automation (AMA) Cluster covers technologies that enable clean, lean and flexible manufacturing and industrial automation. Technologies such as three-dimensional (3D) and four-dimensional (4D) printing, wireless sensors and networks, information and communication technology, multi-material joining, composites manufacturing, digital manufacturing, micro- and nano-manufacturing, lasers, advanced software, and printing techniques, are covered as part of this cluster.

The technologies covered here impact a wide range of industries, such as the impact semiconductor, automotive and transportation, aerospace and defense, industrial, healthcare, logistics, and electronics industries.

Key Topics Covered:

For more information about this report visit https://www.researchandmarkets.com/r/61q6cl

View source version on businesswire.com: https://www.businesswire.com/news/home/20191218005386/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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Innovations in Radar-on-Chip, Wireless Electric Vehicle Charging Systems, Artificial Intelligence, and In-vehicle Sensing Solutions, 2019 Study -...

Artificial intelligence (AI) is transforming work and life as we know it, already boosting workplace efficiency and leading to noticeable improvements to the quality of for instance healthcare, lowering costs while giving clinicians time to work with their patients more closely, and with more insight. This was made clear in arecent MIT Technology Review Insights surveyproduced in partnership with GE Healthcare, where more than 82 percent of healthcare business leaders said their AI deployments were showing positive results across operational and administrative activities,.

When analysing the impact AI would have on the global education sector, founders of theInstitute for Ethical AI in Education (IEAIED) said there wasno need to fear the technology. Rather than replace the human element in education, AI would augment teaching and learning, they said.

There are highly beneficial applications of machine learning inside the area of education. Artificial Intelligence may enable personalised learning, especially important for students with specialized needs and challenges. Awell-designed AI can be used to identify learners particular needs so that everyone especially the most vulnerable can receive targeted support.

With global education and healthcare being just two of many sectors that AI has advanced so far, and withhundreds more AI technology developmentson the horizon, such as autonomous vehicles, manufacturing and financial services to add to the list, the need for expertise in the field appears limitless!

Firmly supporting this need and accepting this challenge is theFaculty of Science and TechnologyatUme University, Sweden.

Currently open forautumn 2020 intake, their newMasters in Artificial Intelligenceis a postgraduate programme that enables you to develop broad and core competence in AI and equips you with the digital tools necessary for future career success.

Youll also experience a combination of lectures, seminars, group work, and tutorials in conjunction with different types of assignments and laboratory work to advance your AI education in a multidisciplinary manner.

It is of critical importance to study in a more multidisciplinary manner, where humanities and social sciences are combined with science and technology. AI can no longer be seen as a purely technical or computer science discipline. It is per definition interdisciplinary, says Ume Department of Computing Science Professor, Virginia Dignum.

One of the first professors recruited to Sweden as part of the Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative and actively involved in several international initiatives on policy and strategy guidelines for AI research and applications, such as theEuropean Commission High Level Expert Group on Artificial Intelligence (AI HLEG), Dignum is one of the AI experts at Ume who are driving research and graduate success forward.

My position at Ume University makes it possible for me to look at societal, ethical and cultural consequences of AI. I will for instance be studying methods and tools to ensure that AI systems are formed not to violate human values and ethical principles, says Dignum, who also leads the research group Social and Ethical Artificial Intelligence at the Faculty.

Another integral member at the Faculty is Senior Lecturer Helena Lindgren.

Understanding the urgency of AI integration, Lindgren believes that the university needs to be driven to produce research that develops, educates and enhances the capabilities of AI in society, both in terms of system development and implementation.

One of the objectives at Ume is to raise societys AI competence, such as through continuing education and professional development of currently employed persons. Its very important for Sweden as a nation, as well as its companies and organisations, to be able to take the next step in digital development, says Lindgren.

Lindgren and Dignum reflect the high caliber of the 30-strong researchers at Ume University that are engaged in the development of AI in different areas.

To study here is to be under their expert guidance as you undertake courses that relate to human-AI interaction and complete student projects conducted in collaboration with an organisation addressing societal challenges.

In these projects, students are expected to collaborate in interdisciplinary teams and with representatives from industry and public organisations, adding a practical twist to the 2020 course.

In this English-taught Masters, you are also expected to take full responsibility for organising your tasks so that deadlines are met and collaborative work within student projects are manageable within office hours.

Despite being a new course, AI is not a new focus for Ume.

In the 1970s, Ume Professor Lars-Erik Janlert focused on Knowledge Representation, and in the early 1980s he formed the Swedish AI Society together with other Swedish researchers.

Since then, Ume has expanded its outreach into a variety of research and education activities across different departments and faculties and is now one of seven universities that are part of the governmental initiative AI Competence for Sweden.

Ume University

Continuously building its research efforts through its strong interdisciplinary traditions and close collaborations with society, AI@UmU initiatives have also established an expanding network of researchers, teachers, students and professionals who want to learn, discuss and collaborate around AI-related issues via seminars, panel discussions and courses.

Always welcome to discuss the latest tech revelations and AI advancements with their professors and visiting professionals, Ume students are motivated to unearth AI research angles of their own.

From day one of the new Masters programme, theyll deepen their insight into this exciting field and take their knowledge of AIs theoretical foundations, intelligent robotics, machine learning and data science further.

So, if youre ready to master all areas of AI and want to start your postgraduate study venture in Sweden, click here to find out about the application and eligibility process.

Follow Ume University on Facebook, Instagram, Twitter and YouTube.

Ume University: Preparing students for life after graduation

Ume University: Advancing Science through 5 strong research environments

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Ume University: Master all areas of Artificial Intelligence - Study International News

When AI goes into the machine learning space, it opens up a range of issues such as biases and privacy, she says. Boards have to be switched on to this and be able to ask the right questions.

According to Williams, a significant proportion of the challenges caused by AI usage within companies comes from the fact that the technology is far from transparent. Even the people who build it dont really know why it does what it does, she says. The board is critical. If it is successfulin understanding AI, developing strategies for it, and integrating it into mainstream business strategy, the payoff is huge.

Asked to nominate other technology-related issues occupying the minds board members, panel members pointed to a range including security and the ability to withstand cyber attacks.

Cyber security is really at the top of the list, says David Attenborough, managing director and chief executive at betting company Tabcorp. This is because any company is under permanent attack from different directions and you need to be protecting your customers, your networks and youremployees from those attacks.

The other major issue that keeps me awake at night is the resilience of networks because we have multiple systems supporting a massive retail network and a big digital network. On big days, such as the Melbourne Cup, if you have a system that goes down it is incredibly expensive and disruptiveand reputationally damaging.

David Attenborough, managing director and chief executive at betting company Tabcorp, says cyber security is top priority.Jesse Marlow

While information technology is a critical component for organisations of all sizes, the panellists also stressed that Australian businesses must be more than simply technology consumers.

To achieve long-term growth, it is vital to deploy new technologies to underpin sustained and far-reaching innovation.

The board wants too see a pipeline of ideas, says Stops. They want to know that the company is constantly thinking about new ways to do things and that the pipeline is constantly being filled and fed through.

She says innovation is not something that is unique to a particular group. Rather, it has to be a mindset and something that is in place right across an organisation.

The usual approach within a lot of companies has been to carve off a group and call it an innovation team, she says. Companies are now realising that this is not creating an innovation culture - its just putting some smart people in a corner.

Stops warns, however, that its important how innovation and new ideas are handled. Care needs to be taken that it doesnt get caught up in traditional multi layers of approval which can lead to a good idea dying before it can be fully developed.

The board should be keen to make sure there is a way in which those ideas can move through the organisation quite quickly, she says.

Also, there is a need to create a culture in which it is OK to fail. A lot of organisations spend money on innovation and new ideas and if they dont work people are shot and off they go. That is not what an innovation culture is all about.

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Artificial intelligence must be used with care - The Australian Financial Review

Artificial intelligence (AI) needs to be more accountable but ethical considerations are not keeping pace with the technologys rate of deployment, says a panel of experts.

This is partly due to the black box nature of AI, whereby its almost impossible to determine how or why an AI makes the decisions it does, as well as the complexities of creating an unbiased AI.

However, according to panelists at the Bristol Technology Showcase, transparency is not enough, with greater accountability being the key to solving many of the ethical issues surrounding AI.

Meaningful transparency doesnt simply follow from doing things like open sourcing the code, thats not sufficient, says Eamonn ONeill, professor of computer science at the University of Bath and director of the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI.

Code and deep learning networks can be opaque however hard you try to open them to inspection. How does seeing a million lines of code help you understand what your smartphones mid-ware is doing? Probably not a lot.

ONeill says that AI needs to be accompanied by a chain of accountability that holds the systems human operator responsible for the decisions of the algorithm.

We dont go to a company and say I cant tell if youve cooked the books because I cant access the neurons of your accountants nobody cares about accountants neurons, and we shouldnt care about the internal workings of AI neural networks either, he said.

Instead, ONeill says we should be focusing on outcomes.

John Buyers, chair of the AI and Ethics panel and a partner at law firm Osborne Clark, points to the example of Mount Sanai Hospital using an AI system called Deep Patient, which was made to trawl through thousands of electronic health records.

Over the course of doing that, Deep Patient became very adept at diagnosing, among other things, adult schizophrenia, which human doctors simply couldnt do, he says. They dont know how the system got to that, but it was of demonstrable public benefit.

Zara Nanu, CEO of human resources technology company Gapsquare, says: When we talk about bias, its bias in terms of the existing data we have that machines are looking at, but also the bias in algorithms we then apply to the data.

She gives the example of Amazon, which gathered a team of data scientists to develop an algorithm that would help it identify top engineers from around the world, who could then be recruited by the company.

All was going well except the machines had learnt to exclude women from the candidate pool, so it was down-scoring people who had woman on their CV, and it was actually scoring higher people who had words like lead or manage, she says.

Amazon came under scrutiny and tried to look how they could make it fairer, but they had to scrap the programme because they couldnt hand-on-heart say the algorithm wouldnt end up discriminating against another group.

Therefore, while accountability does not remove potential bias in the first place, it did make Amazon, as the entity operating the AI system, responsible for the negative effects or consequences of that bias.

However, Chris Ford, a Smith and Williamson partner responsible for a $270m AI investment fund, says theres a critical deficit in the way many corporate entities are approaching the deployment of the technology.

MIT Sloan and Boston Consultancy Group produced an interesting paper earlier this year surveying 3,000 companies globally, most of them outside North America, he says.

What was eye catching was that of those who responded, about half of them said they can see no strategic risk in the deployment of AI platforms within their business, and I find that quite extraordinary.

Ford says this is partly due to a fear of missing out on the latest technological trends, but also because there is not enough emphasis on ethics in education related to AI.

He notes the example of Stuart Russells book,Artificial intelligence: A modern approach, which has been through numerous iterations and is one of the most popular course texts in the world.

That textbook in its most recent form is up to 1,100 pages, he says. Its extraordinarily comprehensive, but the section that deals with ethics is covered in the first 36 pages.

So theres an issue on emphasis here, both in respect to the academic training of data scientists but also what theyre expected to engage with in the commercial world when they leave education.

In terms of bias, the panelists also note that what is socially normal or acceptable is biased in itself.

The question then becomes whose societal norms are we talking about? We are already seeing significant differences and perspectives in the adoption of AI in different parts of the world, says Ford.

Buyers summarised that a lack of bias is not the introduction of objectivity, but the application of subjectivity in accordance with societal norms, so its incredibly difficult.

The overall argument is that AI, like humans, will always be biased to a point of view, meaning transparency will only go so far in solving the ethical issues around the deployment of AI.

Using AI in contrast to humans can facilitate transparency we can fully document the software engineering process, the data, the training, the system performance these measures can be used to support systematic inspection, and therefore transparency and regulation, but accountability and responsibility must stay with the humans, says ONeill.

The Bristol Technology Showcase was held in November 2019, and focused on the impact of emerging technologies on both businesses and wider society.

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Accountability is the key to ethical artificial intelligence, experts say - ComputerWeekly.com

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

Most banks (80%) are highly aware of the potential benefits presented by AI, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence. Certain AI use cases have already gained prominence across banks' operations, with chatbots in the front office and anti-payments fraud in the middle office the most mature.

Banks can use AI to transform the customer experience by enabling frictionless, 24/7 customer interactions - but AI in banking applications isn't just limited to retail banking services. The back and middle offices of investment banking and all other financial services for that matter could also benefit from AI.

In this report, Business Insider Intelligence identifies the most meaningful AI applications across banks' front and middle offices. We also discuss the winning AI strategies used by financial institutions so far, and provide recommendations for how banks can best approach an AI-enabled digital transformation.

The companies mentioned in this report are: Capital One, Citi, HSBC, JPMorgan Chase, Personetics, Quantexa, and U.S. Bank

Here are some of the key takeaways from the report:

In full, the report:

Interested in getting the full report? Here are four ways to access it:

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The impact of artificial intelligence in the banking sector & how AI is being used in 2020 - Business Insider India

As artificial intelligence continues to grow and develop, its presence is affecting more and more industries. In this piece, Becky Holton explores the impact AI is having on the music business, as well as looking at some of the AI-based tools at play on the music market today.

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These days, we can all artificial intelligence (AI) all around us. The main purpose of AI is to simulate mental tasks and one of the most important subsets of this technological advancement is machine learning. This is because it has a significant effect on all the other fields within AI.

As AI gets incorporated in different industries, the music industry has now begun using it. With the introduction of AI, the competitive sustainability of organizations and individuals in the music industry has increased. Lets find out more about AI in the music industry and a few tools that those in the industry are now using.

How AI affects the music industry

Most people arent used to thinking about AI as something universal and pervasive. But in reality, it already exists all around us and we use different kinds of AI technology in our daily lives. Many industries and businesses use some form of AI technology from online shops,essay writer services, scientific institutions, and more.

But what about the music industry?

As human beings, we are all naturally creative and the best of us express their creativity through music. While we have come this far without using AI to make music, does this mean that we dont need such technology for this industry?

When you think about the capacity of AI, the first question that comes to mind is if AI can play real music. With all the smart devices that we use, we already know the answer to this question. But even if these technologies can play music, does this mean that it can compose music too? If so, how will this affect the market of the music industry?

While AI can play music created by people or even play music on its own, right now, AI doesnt have the capacity to compose original music. However, the long-term vision of those who develop AI is for this technological advancement to have the ability to produce certain styles of music after we (humans) feed the right kind of data to it.

AI music enthusiasts and experts feel excited about this because it will allow them to collect valuable data. Also, it will let technicians to see exactly how music gets shaped. But right now, AI can only produce some variations of songs that already exist, not write original scores. Still, it can help producers and musicians improve their skills to produce better music.

The most significant developments

A lot of people still fear the rise of AI because they dont want computers to take over the work that humans do. But AI does offer a lot of benefits too. In the music industry, the most significant benefits of AI come from the following developments:

Audio improvements

Right now, one of the current roles of AI is in the finalization of music. This occurs when producers master or smooth out aspects of their song production process. In the music industry, experts develop AI technology to understand the cues smoothing professionals look for, then remove them automatically without the need for human interaction.

Songwriting and song development

Erica Samson who works as a creative writer for anessay writing service UKsays that songwriting is something that you need to practice thoroughly if you want to become a true artist. She adds that when it comes to AI taking over this aspect, theres a very low risk of this happening.

Instead, AI can provide professional musicians the several opportunities to come up with new, exciting songs. There are now some AI-music development startups that have made the creation of music more accessible.

This is great news for musicians who are just starting out too. Of course, while such technologies can aid in the process, humans still have to do most of the work, especially in terms of coming up with creative content.

AI tools for the music industry

Its true that AI is gradually becoming an essential part of the music industry. This is good news for some while others remain uncertain. If youre wondering what tools are out there for the musically inclined, here are some examples:

Playbeat

Playbeat utilizes AI technology to randomize grooves through the manipulation of volume, pitch, and other elements of music. All you have to do is load your music samples, tap/click the randomize button, and Playbeat gets to work. You also have the option to adjust parameters manually if you want to.

Aiva Technologies

Aiva Technologies focuses on creating music for movies, commercials, and video games. It has analyzed musical creations of several classical composers and has created its own variations of these.

Jukedeck

Jukedeck is a paid AI tool for individuals and businesses. The developers have put in a lot of work to teach this AI by feeding it with large amounts of audio files and data.

Rhythmiq

This is one of the more unique AI tools for the music industry. Rather than building its own beats from scratch, this tool depends on beats that you have already uploaded then creates its own variations.

Rhythmiq focuses on live performance. For instance, if you already have a playlist that you listen to while working, but you want to have a few variations to fill the silence and change your mood, then this may be the perfect tool for you.

Popgun

This Australian AI startup has its basis on deep learning technology. Unlike other AI tools, this one can predict the music you will play, provide accompaniment as you play, and even create its own improvisations.

The main instrument for this AI tool is an electronic keyboard that the developers had trained using electronic samples. But the developers also plan to collaborate with other musicians for Popgun to learn to play other instruments too.

Conclusion

While the mere idea of AI creating music might seem threatening for musicians and music producers, the truth is, AI can actually help them think more creatively. AI eliminates the more tedious aspects of music production so musicians can focus on more important things like unleashing their creativity.

AI is one of the best solutions for amateurs, too, since it can help them create better music without spending too much time or effort. At the end of the day, creativity remains to be something only humans have so we shouldnt feel worried about AI taking over.

Becky Holton is a journalist and a blogger. She is interested in education technologies and is always ready to support informative speaking. Follow her on Twitter.

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8 Tools: How To Think About Artificial Intelligence In The Music Industry - hypebot.com

AI is coming to Wall Street

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Computers arent new to Wall Street, but for most of their lives, their function has been primarily quantitative. That, says Forbes William Baldwin, is about to change thanks to the rise of artificial intelligence.

Created by a trio of former MBA classmates at at UC Berkeley, EquBot is the answer to its creators dream of producing a computer capable of blending precise financial data with less clear information found in annual reports and news articles.

EquBot has since opened up AI Powered Equity ETF, adding AI Powered International Equity in 2018. According to Forbes, the system absorbs 1.3 million texts a day, including news, blogs, social media and SEC filings. IBMs Watson the AI system that proved artificial intelligence can be trained to recognize relationships and patterns previously thought to be the sole domain of the human mind when it bested two human Jeopardy champions in 2011 then digests the language, picking up facts to feed into a knowledge graph of a million nodes.

While Forbessays its too early to tell whether EquBot will succeed long term, theres no doubt AI is going to change things on Wall Street. Donna Dillenberger, an IBM scientist in Yorktown Heights, New York, is reportedly working on a stock market model with millions of nodes, while IBM is already selling AI up and down Wall Street.

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How Artificial Intelligence Is Reshaping the Future of Stock Picking - InsideHook

Dublin, Dec. 17, 2019 (GLOBE NEWSWIRE) -- The "AI Solutions and Market Opportunities: AI & Cognitive Computing Technologies, Infrastructure, Capabilities, Leading Apps, and Services (2019-2024)" report has been added to ResearchAndMarkets.com's offering.

This is the most comprehensive research available covering artificial intelligence in telecommunications, media, and digital technology as a whole. For example, it covers AI in everything from consumer devices to communications networks as well as AI in key markets and technologies such as AI in supply chain management and AI-bases smart machines used in various industry verticals. It also covers the convergence of AI with IoT (AIoT), which is also known as the Artificial Intelligence of Things. It also provides a look ahead towards general-purpose intelligence, which represents the evolution of AI towards a utility function in which cognitive capabilities are leveraged within virtually every product, service, application, and solution.

One of the fastest-growing areas for artificial intelligence is the AI chipset marketplace, which is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. This will also be transformational for existing critical business functions such as Identity management, authentication, and cybersecurity. Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden patterns among data, predict actionable insight, and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance.

For example, AI-enabled chatbots are taking Customer Relationship Management (CRM) to a new level as business-to-business, business-to-consumer, and consumer-to-business communications is both automated and improved by way of push and pull of the right information at the right time. Chatbots also provide benefits to customers as both existing clients and prospects enjoy the freedom to interact on their own terms. Our research indicates that over 50% of customer queries may be managed today via AI-based chatbots. As the interface between humans and computers evolves from an "operational" interface (Websites and traditional Apps) to an increasingly more "conversational" interface expectations about how humans communicate, consume content, use apps, and engage in commerce will change dramatically. This transformation is poised to impact virtually every aspect of marketing and sales operations for every industry vertical. For example, AI-enabled voice chat, also known as conversational AI, provides a completely human-like experience and will completely replace human-based CRM in some industries.

Smart machines collectively represent intelligent devices, machinery, equipment, and embedded automation software that perform repetitive tasks and solve complex problems autonomously. Along with AI, IoT connectivity, and M2M communications, smart machines are a key component of smart systems, which include many emerging technologies such as smart dust, neuro-computing, and advanced robotics. Smart machines will also benefit significantly from advancements in AIoT. The drivers for enterprise and industrial adoption of smart machines include improvements in the smart workplace, smart data discovery, cognitive automation, and more. Currently conceived smart machine products include autonomous robots (such as service robots), self-driving vehicles, expert systems (such as medical decision support systems), medical robots, intelligent assistants (such as automated online assistants), virtual private assistants (Siri, Google Assistant, Amazon Alexa, etc.), embedded software systems (such as machine monitoring and control systems), neurocomputers (such as purpose-built intelligent machines), and smart wearable devices.

Computing at the edge of IT and communications networks will require a different kind of intelligence. AI will be required for both security (data and infrastructure) as well as to optimize the flow of information in the form of streaming data and the ability to optimize decision-making via real-time data analytics. Edge networks will be the point of the spear so to speak when it comes to data handling, meaning that streaming data will be available for processing and decision-making. While advanced data analytics software solutions can be very effective for this purpose, there will be opportunities to enhance real-time data analytics by way of leveraging AI to automate decision making and to engage machine learning for ongoing efficiency and effectiveness improvements.

Cognitive informatics is poised to become an important aspect of every major vertical. The cognitive informatics market relies upon those technologies that improve human information processing. Technologies included within this interdisciplinary domain always include some degree of Artificial Intelligence and cognitive computing, but are increasingly involving Internet of Things (IoT) enabled devices, networks and systems. In fact, this multidisciplinary combination of cognition and information sciences includes the convergence of AI and IoT, which is also referred to as the AIoT market. As human beings have cognitive limitations (such as attention, comprehension, decision-making, learning, memory, learning, and visualization), the cognitive informatics market seeks to provide human cognition augmentation and enhancement. Advancements in the understanding human behavioral science, neuroscience, and psychology are combined with innovation in AI such as improved Natural Language Processing (NLP) mechanisms and linguistics processes. Machine learning improvements to areas such as what is said vs. what is meant and context-based AI are leading to an overall improvement in man-machine interfaces critical to successful cognitive informatics market implementation.

The role and importance of AI in 5G ranges from optimizing resource allocation to data security and protection of network and enterprise assets. However, the concept of using AI in networking is a relatively new area that will ultimately require a more unified approach to fully realize its great potential. In addition, AI will assist 5G network slicing, which represents the ability to dynamically allocate bandwidth, and enforce associated service level agreements, and a per-customer and per-application basis. AI will automate the process of assigning network slices, including informing enterprise customers when the slices they are requesting are not in their best interest based on anticipated network conditions.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange. AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks.

APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data. While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

IoT in consumer, enterprise, industrial, and government market segments has very unique needs in terms of infrastructure, devices, systems, and processes. One thing they all have in common is that they each produce massive amounts of data, most of which is of the unstructured variety, requiring big data technologies for management. AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. The author sees three different types of IoT Data: (1) Raw (untouched and unstructured) Data, (2) Meta (data about data), and (3) Transformed (valued-added data). AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.

AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

Experiential networking is a relatively new concept with ETSI forming an Industry Specification Group (ISG) focused on Experiential Network Intelligence (ENI) and holding an initial ISG ENI meeting in 2017. These efforts define an observe-orient-decide-act control model with the intent that networks will become increasingly more adaptive, supporting intelligent service operations by way of cognitive network management. Accordingly, core to the experiential networking market is the use of AI and cognitive computing. More specifically, ENI will leverage data and contextual information (such as AI-based decision making) to take actions based on device and system-related events. Responses to events, related processes, and machine learning, allows ENI to make automated decisions and provide recommendations for use by other systems such as management and orchestration platforms. This event-driven approach allows the experiential networking market to use various technologies to engage in intelligent analysis necessary for network and service policies and modeling.

Also known as Artificial General Intelligence (AGI), General Purpose Artificial Intelligence represents silicon-based AI that mimics human-like cognition to perform a wide variety of tasks that span beyond mere number crunching. Whereas most current AI solutions are limited in terms of the type and variety of problems that may be solved, AGI may be employed to solve many different problems including machine translation, natural language processing, logical reasoning, knowledge representation, social intelligence, and numerous others. Unlike many early AI solutions that were designed and implemented with a narrow focus, AGI will be leveraged to solve problems in many different domains and across many different industry verticals including 3D design, transforming customer service, securing enterprise data, securing public facility and personnel, financial trading, healthcare solution, highly personalized target marketing, detecting fraud, recommendation engines, autonomous vehicles and smart mobility, online search, and many other areas. AGI is rapidly evolving in many areas. However, scalability remains a challenge.

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations. Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of AI are being integrated into SCM solution to improve everything from process automation to providing greater visibility into static and real-time data as well as related management information systems. In addition to fully automated decision making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains).

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Report and Topics Covered:

Artificial Intelligence Market by Technology, Infrastructure, Components, Devices, Solutions, and Industry Verticals 2019-20241. Introduction2. Overview3. Technology Impact Analysis4. Market Solutions and Applications Analysis5. Company Analysis6. AI Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations

Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2019-20241. Executive Summary2. Introduction3. AIoT Technology and Market4. AIoT Applications Analysis5. Analysis of Important AIoT Companies6. AIoT Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations

Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services 2019-20241. Executive Summary2. Introduction3. Overview4. AI Technology in Big Data and IoT5. AI Technology Application and Use Case6. AI Technology Impact on Vertical Market7. AI Predictive Analytics in Vertical Industry8. Company Analysis9. AI in Big Data and IoT Market Analysis and Forecasts 2019-202410. Conclusions and Recommendations11. Appendix

Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, Planning and Logistics, Inventory, Fleet, Freight, Risk), and Region 2019-20241. Executive Summary2. Introduction3. AI in SCM Challenges and Opportunities4. Supply Chain Ecosystem Company Analysis5. AI in SCM Market Analysis and Forecasts 2019-20246. Summary and Recommendations

AI based Chatbot Market by Type (Text, Voice, and Hybrid), Use Case, Deployment Type, Value Chain Component, Market Segment (Consumer, Enterprise, Industrial, Government), Industry Vertical, Region and Country 2019-20241. Executive Summary2. Introduction3. Intelligent Chatbots Ecosystem Analysis4. Chatbot Market: SWOT Analysis and Use Cases5. Chatbot Company and Solution Analysis6. Conclusions and Recommendations7. AI Based Chatbot Market Analysis and Forecasts 2019-20248. Regional AI based Chatbot Market 2019-20249. Conversational AI Forecasts 2019-2024

Smart Machines in Enterprise, Industrial Automation, and IIoT by Technology, Product, Solution, and Industry Verticals 2019-20241. Introduction2. Smart Machine Ecosystem3. Smart Machine Market Analysis and Forecasts4. Company Analysis5. Conclusions and Recommendations6. Appendix: General Purpose AI Market Analysis and Forecasts

Artificial General Intelligence (AGI) Market: General Purpose Artificial Intelligence, AI Agent Platforms, and Software1. Executive Summary2. Introduction3. Technology and Application Analysis4. General Purpose AI Market Analysis and Forecasts5. Company Analysis6. Conclusions and Recommendations

Experiential Networking Market by Technology, Use Case, and Solutions 2019-20241. Executive Summary2. Overview3. Introduction4. Technologies Supporting Experiential Networking5. Company Analysis6. Experiential Networking Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations

AI Chipsets for Wireless Networks and Devices, Cloud and Next Generation Computing, IoT, and Big Data Analytics 2019-20241. Executive Summary2. Research Overview3. AI Chipsets Introduction4. Technologies, Solutions, and Markets5. Company Analysis6. AI Chipsets Market Analysis and Forecasts 2019-20247. Conclusions and Recommendations

Cognitive Informatics Market by Technology, Solution (Smart Data, Self-Adaptive Software, Self-Correcting Infrastructure, Cognitive Analytics), Sector (Consumer, Enterprise, Industrial, Government), Industry Vertical, and Region 2019-20241. Executive Summary2. Introduction3. Technologies and Applications4. Company Analysis5. Cognitive Informatics Market Analysis and Forecasts6. Conclusions and Recommendations

Companies Mentioned:

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As global losses rack up from climate change-exacerbated natural disasters from voracious wildfires to ferocious hurricanes communities are scrambling to prepare (and to hedge their losses).

While information technologies such as machine learning and predictive analytics may not be able to prevent these catastrophes outright, they could help communities be better prepared to handle the aftermath. Thats the spirit behind a unique collaboration between Chicago-based technology services company Exigent and the Schulich School of Business at York University in Toronto, one that aims to create a more cost-effective and efficient marketplace for disaster relief and emergency response services.

The idea is to help state and provincial governments collectively build a more centralized inventory of relief supplies and other humanitarian items based on the data from a particular wildfire or hurricane season.

Rather than buying supplies locally based on the predictions something many small towns in fire-prone areas can ill-afford a community would buy "options" for these services in the marketplace being developed through this partnership. If the town ultimately doesn't need the items, it could "trade" them to another region that does have a need, either in the same state or another location. In effect, towns across a state or region or even country could arrange for protection, without having to make that investment outright.

"Why are we not packing those crates in March, because they are going to go somewhere?" asked Exigent CEO David Holme, referring to the current system.

The most obvious reason is that its expensive: Relief suppliers won't invest in making items unless they have certainty of orders. The intention of the Exigent-Schulich project is to move from a system that is 100 percent reactive, and consequently very slow, to one that is at least 50 percent predictive that can deliver help far more quickly, he said.

To do this, Exigent is working with AI students at Schulich to use information about a communitys demographics, geology and topography, and existing infrastructure to predict what areas affect could need: how many first-aid kits to treat local citizens or how many cement bags to rebuild structures or how many temporary housing units for residents and relief workers. All sorts of data is being consulted, from census information to historical weather data to forward-looking models for wind direction, temperature and humidity, noted Murat Kristal, program director for the Schulich masters program that is involved in the project.

Governments and decision makers are acting in a reactive way right now.

The initial focus of the joint Exigent-Schulich work is on gathering data related to wildfires in Canada and the United States. The prevalence of Californias fires captures many headlines: the insurance losses from the Camp, Hill and Woolsey fires in November 2017 have topped $12 billion. Although it gets far less attention, Texas is also highly prone to wildfires and 80 percent of them are within two miles of a community. To the north, Canadian provinces such as Alberta and Ontario are also at risk: There are an average of 6,000 fires in Canada annually.

Exigent estimates that by deploying supplies to affected regions more quickly, the platform its developing a pilot version is due in June might cut recovery costs by 20 percent and drive down premiums in at-risk regions. "The municipalities and insurers can collaboratively benefit," Holme said. "The more Ive studied the idea, the more useful it seems."

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It's artificial intelligence to the rescue (and response and recovery) - GreenBiz

This product has emerged in a sector that is arguably the most difficult to generate online estimates for. Solar panel power output varies due to the specific characteristics of each roof, which include direction, angle, and available roof space.Once the panel layout is created for a specific roof, there is still the issue of several-thousand electric utility companies with even more rate plans to take into consideration.

Read More: SuperData Launches First-Ever XR Market Intelligence Platform

Accounting for all of these factors is extremely complex and has meant that, to date, a consumer could not find the solar savings potential of their home unless they spoke to a solar company. This is no longer necessary, as a consumer using Solar-Estimate.org can generate an accurate estimate simply by entering their address and the dollar amount of their most recent power bill. It is amazingly simple, given the complexity of the factors that need to be measured.

With the new home solar estimator, machine-learned artificial intelligence has been utilized to measure each individual roof and determine roof plane direction. An in-house-developed algorithm was then used to fill the appropriate parts of each roof with solar panels in a particular order, favoring areas that offered the most energy output potential with the least amount of shading.

The tool is groundbreaking not only for what it does for the solar industry, but the way in which it shows other AI developers how they should be bringing their technology to market. However, its near-impossible for an AI to deliver a perfect result 100% of the time.

Presenting faulty automated results to prospective customers brings down conversion rates and ultimately results in financial loss. Because of this, the integration of AI into home improvement calls to action has been nearly non-existent. Solar-Estimate.org has addressed this concern by putting a backup plan into place.

Read More: FiO Fixes Wine, Gaming and Fitness Industry Pain Points

In the event that the calculator detects a faulty automated result, the user is presented with a manual solar panel layout option instead of being given incorrect information. Nothing fails from the users point of view, and they can seamlessly continue through the rest of the estimation process.

Meanwhile, the faulty result is then passed back to Solar-Estimate.org to further train the machine learning brain via supervised learning. This continuously improves the calculators AI opening the door for greater accuracy and the possibility of additional use cases.

Solar-Estimate.orgs new Solar Panel Layout Tool is trailblazing on several fronts. Its the first online roof layout tool that incorporates machine learning. It requires less knowledge than any professional tool to use. Its also the first free tool available to consumers that takes into account roof direction and angle, determines how many panels can fit on their roof, and then generates an accurate financial estimate.

Read More: AiThority Interview with Pedro Arellano, VP of Product Marketing at Looker

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New Solar Estimator Shows How AI Will Transform the Home Improvement - AiThority

Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services.

Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services.

Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services.

Oilfield service giant Schlumberger has inked a deal to expand its offerings of artificial intelligence products and services.

Schlumberger inks deal to expand artificial intelligence in the oil field

Oilfield service giant Schlumberger has inked a deal to expand the use of artificial intelligence technology in the oil patch.

In a statement, Schlumberger announced it had entered into an agreement the New York software company Dataiku.

Under the agreement, the two companies will work together to develop artificial intelligence products and services for Schlumberger's exploration and production customers.

Service Sector: Baker Hughes enters deal to boost AI in the oil field

With U.S. crude oil prices stuck in the mid-$50 per barrel range, many energy companies are adopting digital tools to increase efficiency and lower costs.

The deal between Schlumberger and Dataiku comes less than a month after oilfield service company rival Baker Hughes entered into a similar deal withtech giant Microsoft and Silicon Valley artificial intelligence company C3.ai.

Fuel Fix: Get daily energy news headlines in your inbox

Headquartered in Paris with its principal offices in Houston, Schlumberger is the largest oilfield service company in the world with more than 100,000 employees in 85 nations.

The company posted a $2.2 billion profit on $32.8 billion of revenue in 2018.

Read the latest oil and gas news from HoustonChronicle.com

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Schlumberger inks deal to expand artificial intelligence in the oil field - Chron

Most cars sold new in 2019 are equipped with technology that lets them scope out the road ahead. They can brake when a pedestrian crosses the road in front of them, for example, or accelerate on their own when a semi passing a slower vehicle moves back into the right lane. Now, Bosch is developing artificial intelligence-powered technology that opens new horizons by teaching cars how to see what and who is riding in them. It sounds creepy, but it could save your life.

Boschs system primarily relies on a small camera integrated into the steering wheel. Facial-recognition technology tells it whether the driver is falling asleep, looking down at a funny video on a phone, yelling at the rear passengers, or otherwise distracted. Artificial intelligence teaches it how to recognize many different situations. The system then takes the most appropriate action. It tries to wake you up if youre dozing off, and it reminds you to look ahead if your eyes are elsewhere. Alternatively, it can recommend a break from driving and, in extreme cases, slow down the car to prevent a collision.

Driver awareness monitoring systems are already on the market in 2019. Cadillacs Super Cruise technology notably relies on one to tell whether the driver is paying attention, but Boschs solution is different because its being trained to recognize a wide variety of scenarios via image-processing algorithms. This approach is similar to how the German firm teaches autonomous cars to interpret objects around them. Real-world footage of drivers falling asleep (hopefully on test tracks, and not on I-80) shows the software precisely what happens before the driver calls it a night.

This technology can also keep an eye on your passengers. Thanks to a camera embedded in the rearview mirror, the system can keep an eye on the people riding in the back, and warn the driver if one isnt wearing a seat belt. It can even detect the position a given passenger is sitting in, and adjust the airbags and seat belt parameters accordingly. Safety systems are designed to work when someone is sitting facing forward and upright, but thats not always the case. If youre slouching in the back seat (admit it, it happens), the last thing you want is the side airbag to become a throat airbag.

Smartphone connectivity plays a role here, too. The same mirror-mounted camera recognizes when a child is left in the back seat, and it automatically sends an alert to the drivers smartphone. It notifies the relevant emergency services if the driver doesnt come back after a predetermined amount of time.

Looking further ahead, when autonomous technology finally merges into the mainstream, this tech could tell the car if the driver is ready to take over. Theres no sense in asking someone to drive if theyre asleep, or if theyve hopped over the drivers seat to chill on the rear bench. Autonomy will come in increments, so its not too far-fetched to imagine a car capable of driving itself at freeway speeds, when the lane markings are clear, but not in crowded urban centers.

The footage captured by the cameras cant be used against you or yours, according to Bosch, because its neither saved nor shared with third parties. Still, its a feature that will certainly raise more than a few concerns about privacy.

The technology could reach production in 2022, when European Union officials will make driver-monitoring technology mandatory in all new cars. Lawmakers hope the feature will save 25,000 lives and prevent at least 140,000 severe injuries by 2038. Theres no word yet on when (or whether) it will come to the United States. Bosch doesnt make cars it never has so its up to automakers to decide whether its worth putting in their new models.

See the article here:

Boschs A.I.-powered tech could prevent accidents by staring at you - Digital Trends

Artificial Intelligence, Machine Learning and Cloud Predictions by Jerry Kurata and Barry Luijregts, Pluralsight. In this article, they share their predictions for the ways that AI, ML and the cloud will be used differently in 2020 and beyond.

This decade has seen a seismic shift in the role of technology, at work and at home. Just ten years ago, technology was a specialist discipline in the workplace, governed by experts. At home things were relatively limited and tech was more in the background. Today technology is at the centre of how everyone works, lives, learns and plays. This prominence is shifting the way we think about, use, interact with and the expectations we have for technology, and we wanted to share some reflections and predictions for the year ahead.

AI Jerry Kurata

Increased User Expectations

As users experience assistants like Alexa and Siri, and cars that drive themselves, the expectations of what applications can do has greatly increased. And these expectations will continue to grow in 2020 and beyond. Users expect a stores website or app to be able to identify a picture of an item and guide them to where the item and accessories for the item are in the store. And these expectations extend to consumers of the information such as a restaurant owner.

This owner should rightfully expect the website built for them to help with their business by keeping their site fresh. The site should drive business to the restaurant by determining the sentiment of reviews, and automatically display the most positive recent reviews to the restaurants front page.

AI/ML will go small scale

We can expect to see more AI/ML on smaller platforms from phones to IoT devices. The hardware needed to run AI/ML solutions is shrinking in size and power requirements, making it possible to bring the power and intelligence of AI/ML to smaller and smaller devices. This is allowing the creation of new classes of intelligent applications and devices that can be deployed everywhere, including:

AI/ML will expand the cloud

In the race for the cloud market, the major providers (Amazon AWS, Microsoft Azure, Google Cloud) are doubling down on their AI/ML offerings. Prices are decreasing, and the number and power of services available in the cloud are ever increasing. In addition, the number of low cost or free cloud-based facilities and compute engines for AI/ML developers and researchers are increasing.

This removes much of the hardware barriers that prevented developers in smaller companies or locales with limited infrastructure from building advanced ML models and AI applications.

AI/ML will become easier to use

As AI/ML is getting more powerful, it is becoming easier to use. Pre-trained models that perform tasks such as language translation, sentiment classification, object detection, and others are becoming readily available. And with minimal coding, these can be incorporated into applications and retrained to solve specific problems. This allows creating a translator from English to Swahili quickly by utilizing the power of a pre-trained translation model and passing it sets of equivalent phrases in the two languages.

There will be greater need for AI/ML education

To keep up with these trends, education in AI and ML is critical. And the need for education includes people developing AI/ML applications, and also C-Suite execs, product managers, and other management personnel. All must understand what AI and ML technologies can do, and where its limits exist. But of course, the level of AI/ML knowledge required is even greater for people involved with creating products.

Regardless of whether they are a web developer, database specialist, or infrastructure analyst, they need to know how to incorporate AI and ML into the products and services they create.

Cloud Barry Luijbregts

Cloud investment will increase

In 2019, more companies than ever adopted cloud computing and increased their investment in the cloud. In 2020, this trend will likely continue. More companies will see the benefits of the cloud and realize that they could never get the same security, performance and availability gains themselves. This new adoption, together with increased economies of scale, will lower prices for cloud storage and services even further.

Cloud will provide easier to use services

Additionally, 2020 will be the year where the major cloud providers will offer more and easier-to-use AI services. These will provide drag-and-drop modelling features and more, out-of-the-box, pre-trained data models to make adoption and usage of AI available for the average developers.

Cloud will tackle more specific problems

On top of that, in 2020, the major cloud vendors will likely start providing solutions that tackle specific problems, like areas of climate change and self-driving vehicles. These new solutions can be implemented without much technical expertise and will have a major impact in problem areas.

Looking further ahead

As we enter a new decade, we are on the cusp of another revolution, as we take our relationship with technology to the next level. Companies will continue to devote ever larger budgets to deploying the latest developments, as AI, machine learning and the cloud become integral to the successful running of any business, no matter the sector.

There have been murmurings that this increase in investment will have an impact on jobs. However, if the right technology is rolled out in the right way, it will only ever complement the human skillset, as opposed to replacing it. We have a crucial role to play in the overall process and our relationship with technology must always remain as intended; a partnership.

Jerry Kurata and Barry Luijregts are expert authors at Pluralsight and teach courses on topics including Artificial Intelligence (AI) and machine learning (ML), big data, computer science and the cloud. In recent years, both have seen first-hand the development of these technologies, the different tools that organisations are investing in and the changing ways they are used.

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8 Artificial Intelligence, Machine Learning and Cloud Predictions To Watch in 2020 - Irish Tech News

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Saying the most important thing to do is just dive in, Lt. Gen. Jack Shanahan, director of the Department of Defense Joint Artificial Intelligence Center, talked to U.S. Naval War College students and faculty on Dec. 12 about the challenges and opportunities of fielding artificial intelligence technology in the U.S. military.

On one side of the emerging tech equation, we need far more national security professionals who understand what this technology can do or, equally important, what it cannot do, Shanahan told his audience in the colleges Mahan Reading Room.

On the other side of the equation, we desperately need more people who grasp the societal implications of new technology, who are capable of looking at this new data-driven world through geopolitical, international relations, humanitarian and even philosophical lenses, he said.

At the Joint AI Center, established in 2018 at the Pentagon, Shanahan is responsible for accelerating the Defense Departments adoption and integration of AI in order to quickly affect national security operations at the largest possible scale.

He told the Naval War College audience that the most valuable contribution of AI to U.S. defense will be how it helps human beings to make better, faster and more precise decisions, especially during high-consequence operations.

AI is like electricity or computers. Like electricity, AI is a transformative, general-purpose enabling technology capable of being used for good or for evil but not a thing unto itself. It is not a weapons system, a gadget or a widget, said the Air Force general whose prior position was director of Project Maven, a Defense Department program using machine learning to autonomously extract objects of interest from photos or video.

If I have learned anything over the past three years, its that theres a chasm between thinking, writing and talking about AI, and doing it, Shanahan said.

There is no substitute whatsoever for rolling up ones sleeves and diving in an AI project, he said.

Shanahan said adapting the Department of Defense to the AI world will be a multigenerational journey, requiring both urgency and patience.

He compared this moment in history to the period between World War I and World War II, when new ideas led to an explosion not just in military innovation but in technology advancement that eventually helped create Silicon Valley.

Now, the private sector is leading the way on AI, which leaves the Defense Department playing catch-up, Shanahan said. However, he added that he sees the U.S. militarys efforts running at a tempo comparable to commercial industry in five years from now.

China, he said, sees AI as a way to leapfrog over the current U.S. defense advantages.

The Chinese military has identified intelligent-ization as a military revolution on par with mechanization from the internal combustion engine, Shanahan said. They are sprinting to incorporate AI technology in all aspects of their military, and the Chinese commercial industry is more than willing to help.

After the speech, in an interview, Shanahan said AI isnt an arms race, but it is a strategic competition.

Regardless of what China does or does not do in AI, we have to accelerate our adoption of it. Its that important to our future, he said.

For example, Shanahan said, in 15 years, what if China has a fully AI-enabled military force, and the United States does not.

To me that scenario brings us an unacceptably high risk of failure because of the speed of the fight in the future, which we have not been prepared for as a result of fighting in the Middle East for 20-some years, he said. That, to me, is the best stark example of why we have to move in this direction.

Looking at the importance of military higher education in the effort, Shanahan said the role of institutions such as the Naval War College is to make a place for the militarys rising stars to think about new ways to harness AI.

What you are here to do is think strategy, the strategic and societal implications of using emerging and disruptive technology, he said.

You will find somebody comes out of here that has a spark, a lightbulb moment, that wants to go back and try this idea they developed while they were here, said Shanahan, who is a 1996 graduate of the Naval War Colleges College of Naval Command and Staff.

The Joint AI Center director said another role for military higher-education institutions is research on practical applications of AI.

Its the thinking about grand strategy and technology together that may be as important to the future of operating concepts as anything else, he said.

Source: USNWC Public Affairs Office | Jeanette Steele, U.S. Naval War College Public Affairs

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Joint Artificial Intelligence Center Director tells Naval War College audience to 'Dive In' on AI - What'sUpNewp