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A Fresh Perspective on Ports and Artificial Intelligence – The Maritime Executive

April 4, 2021 | Author: admin

The highly automated port of Shanhai Yangshan (file image)

By Christopher Dodunski 04-02-2021 02:16:00

There seems no end to the plethora of software solutions suddenly seeming to have acquired the quality of artificial intelligence (AI).Little more than a decade after phones reportedly grew "smart,"you might now be wondering whether technology had crossed yet another historic threshold.For those of us who grew up watching 2001 A Space Odyssey and Knight Rider, the concept of non-human intelligencewhether benevolent or malevolentis nothing new.Not only does science fiction fuel our expectations, it has often demonstrated an uncanny ability at predicting real life technological advancements.Is the age of artificial intelligence now upon us?

What actually is Artificial Intelligence?

Wikipedia describes artificial intelligence this way: "Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality."An alternative source states: "Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence."Put simply, AI is the endeavour to replicate human intelligence in machines.This makes artificial intelligence a very broad concept.Aside from the obvious lack of consciousness and emotionality that comes from a machine, artificial intelligence can mean pretty much anything that replaces what normally required human intelligence.You might be thinking that this has been occurring for decadesfar longer than the current AI phenomenon.And you'd be right.

For a meaningful discussion on artificial intelligence, we need to consider what it means to those it's being marketed to.Which of their needs or expectations are being appealed to by those marketing systems boasting AI?Much of what we see and hear about artificial intelligence, after all, originates with software system providersthose with a vested interest in selling a product.Considered in this light, things become somewhat clearer.Generally, customers are expecting something capable of augmented decision making, at least matching but probably exceeding what an actual person is able to deliver.In light of this, let's reword the question raised at the beginning: Have we entered an era in which computer-based decision making will prove to be the difference between businesses that excel, and those that get left behind?This obviously includes ports.

To answer this, let's consider some simple truths.

What is real?

As humans we solve most of our problems using fast, intuitive judgments.Computers, on the other hand, employ algorithms to imitate simple step-by-step reasoning that humans use when solving puzzles or making logical deductions.There's a real difference between the two, and given that brain research has revealed only limited insights into how even basic thoughts get processed, efforts at closing the gap have been severely hampered.Consequently, the cognitive capabilities of current computer architectures are extremely limited, using only a simplified version of what intelligence is really capable of.The plain truth is that despite the brain's limitations, which we daily grapple with, to compare it with something as rudimentary as an electronic CPU and digital memory is egregiously wrong.

Does the apparent insurmountability in replicating human intelligence in a machine mean, however, that hardware and software engineers have reached the end of the road as to what is physically possible?Have we reached maximum 'smartness', so to speak?Far from it.

Without question computers have markedly improved over the years.Electronic processors (CPU) are vastly quicker today, and memory is faster, larger and cheaper.This has allowed software developers like myself to create ever larger and more complex applications, performing computations at speeds well beyond that of the brain.Whilst these gains don't in any way represent credible intelligence, they do however allow us to model the world in more expansive and detailed ways.Furthermore, we can dovetail and concurrently run larger numbers of algorithms.Paradoxically, this increased back-end sophistication has facilitated the creation of simpler and more intuitive user interfaces.This is because users are interacting with virtual models of the business domain, not raw data representations of it.And users just love it. It is this, coupled with advances in mobile device technology, that makes 2021 a truly exciting time to be involved in port related software systems.

All things considered, what is it then that really matters?

Not being swept along by unsubstantiated or unrealistic claims of artificial intelligence and machine learning certainly matters.Why?Because this distracts from what remains truly important when it comes to evaluating and implementing computer-based business tools.This begins with proper and complete requirements analysis, with well-grounded business objectives in mind.Too often overlooked, requirements analysis is the 80% of prep work that, as with a freshly painted home, largely determines the quality of the finish.

Flowing on from requirements analysis, appropriate modelling is another vital ingredient for any computer system intended to support complex, multi-party work environments such as ports or marine terminals.

In conclusion, the success of a software project has a great deal more to do with good requirements analysis and data modelling than any attempt at emulating intelligence.Therein lies the key message of this article.By remaining firmly focused on these few core areas, a port is more likely to achieve the goal of complementing a highly competent workforce with the best possible tools for the job.

Christopher Dodunski is the founder and lead developer of the MarineBerth port and marine terminal operating system (TOS).

The opinions expressed herein are the author's and not necessarily those of The Maritime Executive.

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A Solution for the Future Needs of artificial intelligence – ARC Viewpoints

April 4, 2021 | Author: admin

Arm introduced the Armv9 architecture in response to the global demand for ubiquitous specialized processing with increasingly capable security and artificial intelligence (AI). Armv9 is the first new Arm architecture in a decade, building on the success of Armv8.

The new capabilities in Armv9 are designed to accelerate the move from general-purpose to more specialized compute across every application as AI, the Internet of Things (IoT) and 5G gain momentum globally.

To address the greatest technology challenge today securing the worlds data the Armv9 roadmap introduces the Arm Confidential Compute Architecture (CCA). Confidential computing shields portions of code and data from access or modification while in-use, even from privileged software, by performing computation in a hardware-based secure environment.

The Arm CCA will introduce the concept of dynamically created Realms, useable by all applications, in a region that is separate from both the secure and non-secure worlds. For example, in business applications, Realms can protect commercially sensitive data and code from the rest of the system while it is in-use, at rest, and in transit.

The ubiquity and range of AI workloads demands more diverse and specialized solutions. For example, it is estimated there will be more than eight billion AI-enabled voice-assisted devices in use by the mid-2020s, and 90 percent or more of on-device applications will contain AI elements along with AI-based interfaces, like vision or voice.

To address this need, Arm partnered with Fujitsu to create the Scalable Vector Extension (SVE) technology, which is at the heart of Fugaku, the worlds fastest supercomputer. Building on that work, Arm has developed SVE2 for Armv9 to enable enhanced machine learning (ML) and digital signal processing (DSP) capabilities across a wider range of applications.

SVE2 enhances the processing ability of 5G systems, virtual and augmented reality, and ML workloads running locally on CPUs, such as image processing and smart home applications. Over the next few years, Arm will further extend the AI capabilities of its technology with substantial enhancements in matrix multiplication within the CPU, in addition to ongoing AI innovations in its Mali GPUs and Ethos NPUs.

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Security In The Cloud Is Enhanced By Artificial Intelligence – Forbes

April 4, 2021 | Author: admin

Artificial Intelligence

One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe.

The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud. Much of that was taking knowledge from on-premises security and scaling it to the larger systems in the cloud. However, theres also more flexibility for attacks in the cloud, so new techniques had to be added. In addition, most organizations are in a hybrid ecosystem, so the on-premises and cloud security must coordinate.

This means an opportunity for AI to provide enhanced security. As mentioned with other machine solutions, security is a mix different AI and non-AI techniques to fit the problem. For instance, theres deep learning. Supervised learning can be used for known attacks, while unsupervised learning can be used to detect anomalous events in a sparse dataset. Reinforcement learning classification can even be done with statistical analysis in time series, and not always require AI. That can provide faster performance in appropriate cases.

On a quick tangent, lets talk about supervised learning and reinforcement learning. Some folks present them as different; I think of the latter as an extension of the former. Classic supervised learning is when input is labeled and the labels are important for the AI system, as they are used to understand and organize the data. When there are errors, humans add more annotations and labels to existing data, or they add more data. In reinforcement learning, feedback for the neural network is given as to how far the results of an iteration are from a set goal. That feedback can be put back into the system by programmers changing weights or, in more advanced systems, by the AI software doing the comparison and adapting on its own. That is a type of supervision, but Ill admit its a philosophical argument.

Back on track, lets add another complexity. In the early days of the cloud, applications were larger but still followed a similar pattern of scale-up and scale-out. Now theres something changing both environments: containers. Simply put, a container is a piece of software that wraps around an application, it has basic services and even a virtual operating system. That allows containers to run on multiple operating systems regardless of internal application code. It also allows cloud platforms and servers to more finely control services to their clients in order to meet service level agreements (SLAs) that provide quality performance to the end customer.

As more applications migrate to a container architecture, its important for security to keep up, said Tanuj Gulati, CTO, Securonix. Light weight collectors can run within application containers, such as with Docker, collecting and sending relevant event logs to the more robust security monitoringapplications running separately. This provides strong security in the new environments without significant burden being added to application performance.

In my discussion with Tanuj Gulati, he explained that they first worked in the virtual machine (VM) environment in local data centers. That provided both an understand that helped extend security to Docker, but also in integrating security between on-premises and cloud systems in a hybrid environment.

Artificial intelligence is focused on detection, but a complete system must also address the response to a perceived threat. The basic system can detect attacks, and based on known problems rules can then determine responses. Unknown problems have unknown responses. Humans must be flagged to handle those questionable transactions, then feedback can be given to reinforce the system. Depending on how complex a system is created, those new rules can be incorporated into the neural network or added to a rules set.

The state of the industry, both in technology and human comfort levels, shows that there will continue to be human oversight before responses to new attacks as the predominant method in the next few years. Advances will push the security industry into more system action and then reporting, review, and adjustment by humans, but that will happen slowly. What will help is that better explainability will be required, as the deep learning black box will have to become more transparent.

Cloud computing and artificial intelligence are growing in parallel. The complexity of the cloud is driving the need for AI, but the complexity of AI is also creating the need for it to work better in the cloud environment with efficiency, transparency and control.

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What the CSPC Has to Say About Artificial Intelligence – The National Law Review

April 4, 2021 | Author: admin

Wednesday, March 31, 2021

American households are increasingly connected internally through the use of artificially intelligent appliances.1But who regulates the safety of those dishwashers, microwaves, refrigerators, and vacuums powered by artificial intelligence (AI)? On March 2, 2021, at a virtual forum attended by stakeholders across the entire industry, the Consumer Product Safety Commission (CPSC) reminded us all that it has the last say on regulating AI and machine learning consumer product safety.

The CPSC is an independent agency comprised of five commissioners who are nominated by the president and confirmed by the Senate to serve staggered seven-year terms. With the Biden administrations shift away from the deregulation agenda of the prior administration and three potential opportunities to staff the commission, consumer product manufacturers, distributors, and retailers should expect increased scrutiny and enforcement.2

The CPSC held the March 2, 2021 forum to gather information on voluntary consensus standards, certification, and product-specification efforts associated with products that use AI, machine learning, and related technologies. Consumer product technology is advancing faster than the regulations that govern it, even with a new administration moving towards greater regulation. As a consequence, many believe that the safety landscape for AI, machine learning, and related technology is lacking. The CPSC, looking to fill the void, is gathering information through events like this forum with a focus on its next steps for AI-related safety regulation.

To influence this developing regulatory framework, manufacturers and importers of consumer products using these technologies must understand and participate in the ongoing dialogue about future regulation and enforcement. While guidance in these evolving areas is likely to be adaptive, the CPSCs developing regulatory framework may surprise unwary manufacturers and importers who have not participated in the discussion.

The CPSC defines AI as any method for programming computers or products to enable them to carry out tasks or behaviors that would require intelligence if performed by humans and machine learning as an iterative process of applying models or algorithms to data sets to learn and detect patterns and/or perform tasks, such as prediction or decision making that can approximate some aspects of intelligence.3To inform the ongoing discussion on how to regulate AI, machine learning, and related technologies, the CPSC provides the following list of considerations:

Identification: Determine presence of AI and machine learning in consumer products. Does the product have AI and machine learning components?

Implications: Differentiate what AI and machine learning functionality exists. What are the AI and machine learning capabilities?

Impact: Discern how AI and machine learning dependencies affect consumers. Do AI and machine learning affect consumer product safety?

Iteration: Distinguish when AI and machine learning evolve and how this transformation changes outcomes. When do products evolve/transform, and do the evolutions/transformations affect product safety?4

These factors and corresponding questions will guide the CPSCs efforts to establish policies and regulations that address current and potential safety concerns.

As indicated at the March 2, 2021 forum, the CPSC is taking some of its cues for its fledgling initiative from organizations that have promulgated voluntary safety standards for AI, including Underwriters Laboratories (UL) and the International Organization for Standardization (ISO). UL 4600 Standard for Safety for the Evaluation of Autonomous Products covers fully autonomous systems that move such as self-driving cars along with applications in mining, agriculture, maintenance, and other vehicles including lightweight unmanned aerial vehicles.5Using a claim-based approach, UL 4600 aims to acknowledge the deviations from traditional safety practices that autonomy requires by assessing the reliability of hardware and software necessary for machine learning, ability to sense the operating environment, and other safety considerations of autonomy. The standard covers topics like safety case construction, risk analysis, safety relevant aspects of the design process, testing, tool qualification, autonomy validation, data integrity, human-machine interaction (for non-drivers), life cycle concerns, metrics and conformance assessment.6While UL 4600 mentions the need for a security plan, it does not define what should be in that plan.

Since 2017, ISO has had an AI working group of 30 participating members and 17 observing members.7This group, known as SC 42, develops international standards in the area of AI and for AI applications. SC 42 provides guidance to JTC 1a specific joint technical committee of ISO and the International Electrotechnical Commission (IEC)and other ISO and IEC committees. As a result of their work, ISO has published seven standards that address AI-related topics and sub-topics, including AI trustworthiness and big data reference architecture.8Twenty-two standards remain in development.9

The CPSC might also look to the European Unions (EU) recent activity on AI, including a twenty-six-page white paper published in February 2020 that includes plans to propose new regulations this year.10On the heels of the General Data Protection Regulation, the EUs regulatory proposal is likely to emphasize privacy and data governance in its efforts to build[] trust in AI.11Other areas of emphasis include human agency and oversight, technical robustness and safety, transparency, diversity, non-discrimination and fairness, societal and environmental wellbeing, and accountability.12

***

Focused on AI and machine learning, the CPSC is contemplating potential new consumer product safety regulations. Manufacturers and importers of consumer products that use these technologies would be well served to pay attention toand participate infuture CPSC-initiated policymaking conversations, or risk being left behind or disadvantaged by what is to come.

-------------------------------------------------------

1SeeCrag S. Smith,A.I. Here, There, Everywhere, N.Y. Times (Feb. 23, 2021),https://www.nytimes.com/2021/02/23/technology/ai-innovation-privacy-seniors-education.html.

2Erik K. Swanholt & Kristin M. McGaver,Consumer Product Companies Beware! CPSC Expected to Ramp up Enforcement of Product Safety Regulations(Feb. 24, 2021),https://www.foley.com/en/insights/publications/2021/02/cpsc-enforcement-of-product-safety-regulations.

385 Fed. Reg. 77183-84.

4Id.

5Underwriters Laboratories,Presenting the Standard for Safety for the Evaluation of Autonomous Vehicles and Other Products,https://ul.org/UL4600(last visited Mar. 30, 2021). It is important to note that autonomous vehicles fall under the regulatory purview of the National Highway Traffic Safety Administration.SeeNHTSA,Automated Driving Systems,https://www.nhtsa.gov/vehicle-manufacturers/automated-driving-systems.

6Underwriters Laboratories,Presenting the Standard for Safety for the Evaluation of Autonomous Vehicles and Other Products,https://ul.org/UL4600(last visited Mar. 30, 2021).

7ISO, ISO/IEC JTC 1/SC 42,Artificial Intelligence,https://www.iso.org/committee/6794475.html(last visited Mar. 30, 2021).

8ISO, Standards by ISO/IEC JTC 1/SC 42,Artificial Intelligence,https://www.iso.org/committee/6794475/x/catalogue/p/1/u/0/w/0/d/0(last visited Mar. 30, 2021).

9Id.

10See Commission White Paper on Artificial Intelligence, COM (2020) 65 final (Feb. 19, 2020),https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

11European Commission, Policies,A European approach to Artificial Intelligence,https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence(last updated Mar. 9, 2021).

12Commission White Paper on Artificial Intelligence, at 9, COM (2020) 65 final (Feb. 19, 2020),https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf.

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Spring 2021 HiPerGator Symposium highlights artificial intelligence research – News – University of Florida – University of Florida

April 4, 2021 | Author: admin

Winners of UF Research's Artificial Intelligence (AI) Research Catalyst Fund presented how they are pursuing multidisciplinary applications of artificial intelligence across the university at the Spring 2021 HiPerGator Symposium Tuesday.

Presenters covered a wide range of topics, sharing how they are using AI to tackle issues like uncovering decades of ecological change to detecting online students who are at risk of dropping out. In the afternoon, attendees and presenters interacted in Q-and-A panels. Roughly 275 people attended the event.

We established the AI Research Catalyst Fund as a way to encourage multidisciplinary teams of faculty and students to rapidly pursue imaginative applications of AI across the institution, said David Norton, UFs vice president for research. The research shared in this Symposium clearly indicated that is whats happening. We anticipate that this initial research will lead to significant external research funding in the future.

The Symposiums focus on AI is part of a sweeping initiative to establish UF as a national leader in the field, which is widely expected to fuel future advances in research and workforce development. The projects presented at the Symposium will leverage the capabilities of HiPerGator AI, the most powerful AI supercomputer in higher education, which UF recently made widely available for teaching and research purposes. The supercomputer, as well as the broader initiative, is made possible by a $100 million public-private partnership with Silicon Valley-based technology company NVIDIA and UF alumnus and NVIDIA co-founder Chris Malachowsky.

The universitys AI initiative empowers faculty to explore real world problems, like how to eliminate bias and create culturally inclusive communications via machine learning. Sylvia Chan-Olmsted, telecommunications professor in the College of Journalism and Media Consumer Research director, posed the question of how to find ways to increase cultural resonance in sharing information.

"Fairness has been touted as one of the most important issues for responsible AI as AI-powered systems increasingly impact human minds," she said. "At the same time, access to information is essential in today's knowledge economy and fundamental to our democracy."

Obstacles that stem from cross-cultural communication means certain groups of the population might be excluded or lack access and not be able to participate fully. To address this, Chan-Olmsted and Huan Chen, College of Journalism and Communications Advertising associate professor, will use social theories to build a culturally aware machine learning system that addresses communication in a multicultural society.

The Spring HiPerGator Symposium was a success for UF in many ways, said Erik Deumens, director of UFIT Research Computing. Having more than 275 faculty and students attend this virtual event was great."

The Symposium started three years ago as a fall semester event as a way to showcase graduate and postdoctoral work. When COVID-19 struck, the symposium transitioned online, which enabled a much wider audience to attend and learn about the research happening at UF.

"Sharing ideas with the panelists and hearing how the catalyst awardees are using machine learning will spur even more ideas for using HiPerGator AI. Plus, the attendance by faculty representing 25 universities across the U.S., shows the interest of the University of Florida's AI initiative, Deumens said.

Emily Cardinali April 1, 2021

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Artificial Intelligence and Diabetes – Know the prospects and potential offered by AI – Times Now

April 4, 2021 | Author: admin

Artificial Intelligence and Diabetes - Know the prospects and potential offered by AI | Photo Credit: Pixabay 

New Delhi:Every day, technology is developing and with its application, technological advancements are being made in the healthcare sector. Artificial Intelligence, commonly known as AI, is proving to be a blessing for humans as it is aiding us in our everyday lives, thereby making it easier. The prominence of AI in the healthcare sector is not new. From diagnosis of conditions to treatments, AI can be useful for various purposes. Healthcare professionals and experts are utilising it for tackling several chronic diseases such as diabetes.

Artificial intelligence is equipped with brilliant abilities in data monitoring and analysing. When provided with sufficient data, it can analyse and produce substantial results such as reports and predictions. Diabetes mellitus is a chronic disease that affects millions of people around the world. With the emergence of the application of AIin the healthcare sector, the treatment of diabetes is yet another area that has gained due to newer technological aids. Some methods in which AI can contribute to diabetes care are as follows:

Technological advances apart, diabetes (at least Type 2) is an avoidable (lifestyle) disease. If we just master a few rules and incorporate them into our daily regimen, they can help us keep diabetes away or at least under control.

Here are some effective methods to manage diabetes:

Despite the benefits offered by AI, there are certain challenges that come along with it. Some common challenges include quality of data, data credibility, and patient privacy. However, these challenges can be sorted with the imposition of rules and appropriate regulations. In conclusion, with AI in the healthcare sector, we can be sure of a bright future ahead.

Disclaimer: Tips and suggestions mentioned in the article are for general information purpose only and should not be construed as professional medical advice. Always consult your doctor or a dietician before starting any fitness programme or making any changes to your diet.

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Now, cotton farmers in Telangana to fight pests with Artificial Intelligence – The New Indian Express

April 4, 2021 | Author: admin

By Express News Service

HYDERABAD: Now, Artificial Intelligence (AI) will be used in the State to help cotton farmers. The Agriculture Department has signed an MoU with the Wadhwani Institute for AI to assist cotton farmers beat pest infestations.

Wadhwani has developed an AI solution that equips smallholding cotton farmers with the scientific knowledge of an agriculture expert, with the help of a smartphone. The solution, delivered via an app, provides real-time localised advisory and surveillance.

It enables farmers to catch pest infestations early and take corrective measures to avoid significant crop damage. The immediate action on pest will improve the quality of the cotton and fetch more revenues to the farmers. The AI solution is able to predict pink bollworm and Amercian bollworm - the two of the most devastating pests for cotton crop.

In Kharif 2020, the AI solution was deployed in 150 villages for 7,000 farmers. Management consultancy E&Y is conducting an impact evaluation of Kharif 2020 on crop yields and price, as well as impact on farmers' income. The result will be available in May, 2021.

The project is also supported by Google and partners including IDH and IGS. While Wadhwani AI is providing their solution free-of-cost to the State, IICT Hyderabad has agreed to procure traps and lures which will be deployed along with Wadhwani AI's app in 2,800 villages.

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Artificial Intelligences Impact On Jobs Is Nuanced – Forbes

April 4, 2021 | Author: admin

AI will shift tasks around,

Well, is artificial intelligence a job-killer or not? We keep hearing both sides, from projections of doom for many professions that will necessitate things such as universal basic income to help sidelined workers, to projections of countless unfilled jobs needed to build and manage AI-powered enterprises. For a worker losing his or her job to automation, knowing that an AI programming job is being created elsewhere is of little solace.

Perhaps the reality will be somewhere in between. An MIT report released at the end of last year states recent fears about AI leading to mass unemployment are unlikely to be realized. Instead, we believe thatlike all previous labor-saving technologiesAI will enable new industries to emerge, creating more new jobs than are lost to the technology, the reports authors, led by Thomas Malone, director of the MIT Center for Collective Intelligence, conclude. But we see a significant need for governments and other parts of society to help smooth this transition, especially for the individuals whose old jobs are disrupted and who cannot easily find new ones.

The future of AI and job growth or losses may be nuanced, a recent report from BCG and Faethm suggests. Though these technologies will eliminate some jobs, they will create many others, the reports team of authors, led by BCGs Rainer Strack. Governments, companies, and individuals all need to understand these shifts when they plan for the future.

What needs to be understood? For starters, the net number of jobs lost or gained is an artificially simple metric to gauge the impact of digitization, Strack and his co-authors state. For example, eliminating 10 million jobs and creating 10 million new jobs would appear to have negligible impact. In fact, however, doing so would represent a huge economic disruption for the countrynot to mention for the millions of people with their jobs at stake.

Theres even a paradox in play. Computers tend to perform well in tasks that humans find difficult or time-consuming to do, but they tend to work less effectively in tasks that humans find easy to do, the report notes. Also, in many areas, technologies will improve the quality of work that humans do by allowing them to focus on more strategic, value-creating, and personally rewarding tasks.

In other words, AI cant take over many of the soft skills essential to businesses growth initiative, intuition, passion, and ability to sell ideas and concepts. Add that to more technical abilities needed to build and maintain AI and digital environments and keep them focused on what the business needs. In many sectors, severe shortages of skilled workers will mean that growth in demand for talent will be unmet, Strack and his co-authors state. This is particularly true for computer-related occupations and jobs in science, technology, engineering, and math, since technology is fueling the rise of automation across all industries. This is why the computer and mathematics job family group is likely to suffer by far the greatest worker deficits.

At the same time, there will also be increasing demand for jobs requiring compassionate human contact, such as health care, social services, and teaching, they add.

Along with the BCG-Faethms observations, it should be noted that AI cannot replicate the entrepreneurial skills that will be pulling together technology solutions and platforms to connect to the needs of markets. Humans are the innovators.

What to do? Strack and his team urge people to take charge of their professional development through lifelong learning. Individuals will have to take greater responsibility for their own professional development, whether that means through upskilling or reskilling, they state. Pay attention to sources of information and update skills accordingly, either by searching out high-quality providers of education or by charting your own course amid the vast amount of online-learning offers.

The BCG-Faethm team also makes the following recommendations from a corporate perspective:

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This is what happens when artificial intelligence meets emotional intelligence – The Hindu

April 4, 2021 | Author: admin

(Subscribe to our Today's Cache newsletter for a quick snapshot of top 5 tech stories. Click here to subscribe for free.)

Advances in artificial intelligence (AI) over the years has become foundational technology in autonomous vehicles and security systems. Now, a team of researchers at the University of Stanford are teaching computers to recognise not just what objects are in an image, but also how those images make people feel.

The team has trained an algorithm to recognise emotional intent behind great works of art like Vincent Van Goghs Starry Night and James Whistlers Whistlers Mother.

The ability will be key to making AI not just more intelligent, but more human, a researcher said in the study titled ArtEmis: Affective Language for Visual Art.

Also Read | Artificial Intelligence knows when you feel lonely

The team built a database of 81,000 WikiArt paintings and over 4 lakh written responses from 6,500 humans indicating how they felt about a painting. This included their reason for choosing a particular emotion. The team used the responses to train AI to generate emotional responses to visual art and justify those emotions in language.

The algorithm dissected the artists work into one of eight emotional categories including awe, amusement, sadness and fear. It then explained in written text what it is in the image that justifies the emotion.

Also Read | AI finds Bollywoods association of beauty with fair skin unchanged

The model is said to interpret any form of art, including still life, portraits and abstraction. It also takes into account the subjectivity of art, meaning that not everyone feels the same way about a piece of work, the team noted.

The tool can be used by artists, especially graphic designers, to evaluate if their work is having the desired impact.

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Acoustic Quality Control with the Help of Artificial Intelligence – Innovation Origins

April 4, 2021 | Author: admin

Although they can bring great benefits in everyday work, many small and medium-sized enterprises (SMEs) shy away from applications based on artificial intelligence. But AI offers a lot of potential, especially in quality control. Nevertheless, training the models is difficult and hardly feasible without mathematical knowledge, as there are countless parameters that can go into such an analysis. And once an AI algorithm is learned, it is trained only on the specifications it learns. If a product design or the geometry of a component is later changed even slightly, the algorithm recognizes this as an error and the AI must be retrained.

Researchers at the Fraunhofer Institute for Digital Media Technology IDMT in Ilmenau, Germany, have now developed the IDMT-ISAAC software, which can be operated even without extensive expert AI knowledge. IDMT-ISAAC stands for Industrial Sound Analysis for Automated Quality Control. We want to enable SMEs to adapt and customize AI algorithms themselves, says Judith Liebetrau, group leader of Industrial Media Applications at Fraunhofer IDMT. They can apply IDMT-ISAAC to their own audio data, retrain it, and thus get fast and reliable results and decision support for their quality assurance.

IDMT-ISAAC relies on acoustics for analysis, since in many cases it is possible to detect defects just by the sound of the process. To train the AI, the scientists use recorded acoustic data from welding processes. The AI analyzes the typical noises that occur and draws conclusions about the quality of the respective weld seam from the audio data. If, for example, the geometry of a product is then changed, the user can teach this to IDMT-ISAAC with just a few clicks. As early as summer 2021, the software should be adapted to live operation to the extent that the system can immediately analyze real-time data from production and optimize quality assurance. In three to four years, it should even be able to actively intervene in production.

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But the framework at the heart of IDMT-ISAAC doesnt offer new analysis options just for welding. We have integrated various methods in the modular system to be able to map other processes, such as milling, relatively quickly, Liebetrau explains. Companies that already have their own software should also be able to use it in the future. They will also be able to access the institutes AI via an interface on the Fraunhofer IDMT server. It is important to the developers here to emphasize that data protection and data security would always be observed and that the data would be processed anonymously, regardless of whether companies access the AI via an interface or it is integrated into the company via the framework.

For different user groups AI novices as well as AI experts the software can be customized via different user profiles. For example, developers of AI algorithms are very interested in getting a feel for how AI makes its decisions and the sounds it uses to make them, says Judith Liebetrau. So we are also moving a bit in the direction of Explainable AI with the framework to make AI more comprehensible, Liebetrau says.

The researchers will present IDMT-ISAAC at the Hannover Messe from April 12 to 16, 2021. At the virtual booth, Bescher will apply artificial intelligence models using the IDMT-ISAAC software to industrial audio data to verify its quality.

Cover photo: Fraunhofer IDMTs new IDMT-ISAAC software framework provides AI-based audio analysis tools that can be used by users without expert AI knowledge. istock.com/Byjeng, istock.com/TIMETOFOCUS

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