Category Archives: Ai

WASHINGTON The head of a new unmanned and artificial intelligence task force in the Middle East said the U.S. Navy will begin using off-the-shelf gear to boost maritime domain awareness in the region, first gaining trust in the systems and then applying them to real missions.

The Navy announced the creation of Task Force 59 last month, which will operate out of U.S. 5th Fleet headquarters in Bahrain and try to apply emerging unmanned and AI technologies to solve some of the most urgent challenges in that region.

Capt. Michael Brasseur, the inaugural commodore of the task force, said in a recent panel presentation 5th Fleet is a big theater with a lot of challenges, and he needs unmanned technologies to give him more eyes on potential problem areas and help direct limited manned assets where theyre most needed.

The type of technologies were looking at are the dual-use maritime robotics, very inexpensive, allow us to get a lot of sensors in the water. When you partner with allies and partners, that gives you a chance to scale quickly. When you scale quickly, you can start to close those maritime domain awareness gaps, and then you can deter the illicit and malign activity thats happening over here, whether its on the low end, illegal fishing [or] on the high end, weapons transfers supporting the Houthis in the southern Red Sea, he said at the Fed Supernova defense innovation conference Sept. 29.

So its the full spectrum, but for us it comes all the way down to maritime domain awareness, knowing whats going on and then being more precise with the manned assets, he continued.

Two months ago, he said, 5th Fleet hosted several organizations that specialize in unmanned and other emerging technologies for a design sprint in Bahrain to help determine how the task force would look and what it would do.

The task force was officially stood up on Sept. 9, and since that time it has been taking inventory of legacy unmanned systems in the region that could be reinvigorated with the help of new AI and machine learning tools, as well as commercial robotics that could be easily and inexpensively acquired and repurposed for military missions.

Were going to start small with some [unmanned surface vessels], start to build trust in the human-machine team. As we build trust, well scale up with more capable assets, USVs and [unmanned aerial systems], and then well start to put it on real missions, Brasseur said.

Were also in parallel working with partners in the region to help us scale, he continued. Theres a lot of interest in unmanned over here, and once you start bringing in partners you can scale and then start to close some of those gaps.

As an example, he said the task force is already working with U.S. Marines deployed to the area who are using the Martin UAV V-Bat vertical takeoff and landing UAS.

We think we know what theyll do with it but never underestimate a sailor or a Marine with a problem: theyll always find a new way to solve that problem. Were just trying to accelerate getting the kit in the hands of the end users, Brasseur said.

Additionally, he said, the task force is looking for unmanned and AI tools that can fuse data from a large number of sensors and put the data in a form thats usable and actionable for fleet commanders and will eventually allow commanders to stay ahead of maritime challenges in 5th Fleet waters.

The Sea Hunter medium displacement unmanned surface vessel launches from Naval Base Point Loma for the U.S. Pacific Fleets Unmanned Systems Integrated Battle Problem 21 (UxS IBP 21), April 20, 2021. UxS IBP 21 integrates manned and unmanned capabilities into operational scenarios to generate warfighting advantages. (MC2 Thomas Gooley/US Navy)

In a Sept. 8 media roundtable announcing the standup of Task Force 59, 5th Fleet Commander Vice Adm. Brad Cooper told reporters the task force would take unmanned systems already in theater primarily UAVs and supplement them with more systems on and under the sea.

Small exercises will build up the ability to use single unmanned systems, networks of unmanned systems and manned-unmanned teaming constructs, leading up to the International Maritime Exercise (IMX) 2022 event, which could include more than 60 countries. Cooper said IMX is 5th Fleets largest exercise each year and in 2022 will focus on unmanned and AI technologies.

Brasseur helped stand up NATOs Maritime Unmanned Systems Initiative in 2018, prior to his assignment to 5th Fleet, and has experience integrating unmanned and emerging technologies to fleet problems.

During the panel discussion, he spoke about illegal fishing as a global problem that isnt always taken seriously, especially when naval and law enforcement resources are limited. But he said fishermen who can no longer provide for their families due to overfishing by others will turn to other and sometimes illegal means to provide for their families, making fishing issues very relevant to maritime and regional security.

Cooper said in the Sept. 8 call that, while maritime domain awareness is a challenge globally, 5th Fleet is a particularly tough environment and a good home for this unmanned task force.

The concept here is, if it can operate here, they can probably operate in other areas, Cooper said, referring to the 5,000 miles of coastline, three major maritime chokepoints, extreme heat and heavy seas and winds during monsoon season.

Additionally, its a very rich operational environment with real issues and problems in the maritime domain awareness, and the 34-nation Combined Maritime Forces coalition that promotes Middle East stability and security is eager to participate in applying unmanned technology to the region.

During the recent panel, Brasseur said the Tech Bridge network of Navy-industry interface offices played a big role in designing the task force and arranged for some early systems to be sent into theater for testing.

National Tech Bridge Director Whitney Tallarico said in the panel discussion her team which was involved in the design sprint phase alongside organizations like the Joint Artificial Intelligence Center, the Program Executive Office for Unmanned and Small Combatants and Project Overmatch leadership this week would go over some of the regional challenges from 5th Fleet in a classified setting and determine which need to remain classified and which can be put out to industry through the Tech Bridge network, which falls under the NavalX organization. NavalX has several contracting vehicles available to quickly get a prototype out into 5th Fleet with Task Force 59, she said.

In parallel to this Task Force 59 effort, Navy headquarters is also setting up a group to identify the right kinds of unmanned systems for future Navy operations and usher them into the fleet.

Chief of Naval Operations Adm. Mike Gilday said during the Defense News Conference on Sept. 8 this effort will complement Project Overmatch, the Navys effort to develop a network to tie together unmanned and manned systems.

This group, which will be formed in the coming months, will be similar to Project Overmatch in its scope and purpose, where I have a group of technical experts along with operators who put meat on this problem set to move forward in all domains at speed, to make unmanned a reality by the end of this decade, so that we can begin to put ourselves in a position where we can scale these assets and really make them an important part of the fleet make distributed maritime operations come alive in a way that its real.

Brasseur as Task Force 59 commodore will be part of this task force, and Navy headquarters was involved in the design and standup of the 5th Fleet task force to ensure the two efforts are complementary, Brasseur and Cooper said in the media call.

Megan Eckstein is the naval warfare reporter at Defense News. She has covered military news since 2009, with a focus on U.S. Navy and Marine Corps operations, acquisition programs, and budgets. She has reported from four geographic fleets and is happiest when shes filing stories from a ship. Megan is a University of Maryland alumna.

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Navy's new Middle East task force to find ways to apply AI and unmanned to complex region - DefenseNews.com

Artificial Intelligence (AI) was something we imagined would transform our lives in the future. But, since its now a part of our everyday lives, field service managers and technicians should get up to speed on how AI is changing Field Service Management (FSM) for the better.

When most customers say that speed of service is the main motivator for customer serviceas many as 80% of Americans according to PWCits good to know that AI has the ability to speed up the delivery of off-site services.

In essence, AI is when machines simulate or mimic human intelligence. Its a broad, catch-all term that includes subsets such as machine learning and deep learning. All of which aim to enhance and add to human interaction with positive effects.

The traditional model for the field service industry was to once assess, diagnose, and then resolve failed equipment or machinery. But since the advent of AI devices, and the distancing measures are taken to address the impact of the pandemic, AI has given way to a transformation of these standard practices.

Today, by predicting when problems may arise before they occur, or estimating when machinery will need repair, AI technology is speeding up field service delivery and, with it, customer satisfaction.

AI devices include a broad range of software and hardware. Much of AI falls into the category of Internet of Things (IoT) or, in other words, devices that are disrupting field service management in very positive ways.

For instance, utility company Thames Water is using sensors and real-time analytics to forecast asset failures. This is helping them move faster in situations that demand it, such as unexpected storms or water leakages.

Plus, theres US firm Aquants AI-driven Remote Triage. Thanks to decision-making AI, this system is supporting an increase in first-time fix rates. By offering its users a range of possible solutions to problems, it asks questions about the symptoms of each piece of faulty equipment and helps technicians get a head-start before theyve reached the site.

By reducing the length of response time to urgent matters, AI is improving all aspects of delivery from communication to scheduling and customer service. And, according to 80% of industry experts, these efficiencies are boosting employees morale and their skill sets.

The use of AI in field service management has grown in recent years. Here are some of the ways its supporting improvements in the field and making delivery faster:

By using intelligent technology to analyze various data sets, AI programs use specific algorithms to determine whether future jobs are likely to be successful or not. This is helping service technicians to achieve higher first-time fix rates than before.

Where it used to be incumbent on dispatchers to ask customers the right questions before technicians arrived on-site, this predictive technology is freeing up dispatchers time for more visits while reducing repeat visits.

Automated scheduling, via AI programs, is also changing the way schedulers handle their workload. By giving them the time back to focus on more difficult cases, real-time AI scheduling is managing those easy, quick-win scheduling jobs while letting staff focus their time on what matters. Equally, AI technology is overcoming problems with repeat or inappropriate bookings by prioritizing jobs according to data held on technicians skillsets, locations, and availability.

AI Management Software is helping field service companies take a proactive approach to addressing field service needs. By using a wide range of data, AI enables more accurate forecasting. This is preventing potential failures, errors, and interruptions to field service. Its also driving productivity through a reduction in errors.

AI technology is able to optimize route management in real-time, which is particularly helpful during emergencies. So, if there is heavy traffic, for example, a technician can decide whether theyd be able to get to a site or otherwise find an alternative or nearby technician who could help address the issue in time.

By addressing unplanned or unforeseen circumstances before they happen, field service companies are better able to address problems and reduce negative cost implications from any disruptions or interruptions to service.

Computer Vision is where AI algorithms process, analyze, and make sense of visual data such as images or videos. They do this in the same way as humans, basing their complex assessments on pattern recognition.

So, when it comes to technicians seeking additional expertise on specific areas of a jobwhere they may not know themselvesComputer Vision AI can interpret the technicians problems and provide solutions based on the images it sees. So, how does this work in practice?

Stage 1: Technicians use their smartphones to take pictures, having followed instructions on the app

Stage 2: Neural AI networks process the image by detecting specific aspects and acknowledging the issue

Stage 3: The system sends back information to technicians relating to the issue

Stage 4: Technicians resolve the issue and send back the new images

Stage 5: Once the photo is clear of all issues, AI confirms the job is complete

Having an up-to-date knowledge base is a great way to provide field technicians with the additional tools and information they need to self-help when problem-solving issues.

But there will be times when they either cant find the information they need or dont know where to look for it. This is where AI comes in to aid field service technicians with finding the solutions theyre looking for.

Using technology such as Natural Language Processing, AI enables a computer to understand the full meaning and intention of any written or spoken language. By summarising bigger amounts of text, AI is helping technicians get the information much faster than they would have.

While AI has been embraced by the industry in recent years, there are many ways it can help management teams to make significant and lasting improvements to service delivery.

Keeping customers and clients happy is the essence of field service management. So, its good news that AI can help. With fewer opportunities for in-person interactions due to the Covid-19 pandemic, companies can keep customer communication more consistent with AI-driven communication channels.

24/7 helpdesks are possible with the help of AI-supported chatbots. Because, when customers need support, chatbots can be there at any time of the day. They can assist with general queries, or even with helping the customer navigate the knowledge base.

Self-service portals are also an excellent way to keep the customer in control. Self-service allows customers to register problems, upload photos of the issue, or schedule maintenance jobs themselves. This will not only speed up maintenance but will keep customers satisfied.

The beauty of AI and machine learning technologies is that they can handle the jobs that we dont want to do. For example, by prioritizing based on a range of factors such as location, type of machinery, skill levels, customer needs, and any KPIs, customers are more likely to get the service they expect. This also makes sure technicians and dispatchers optimize their time.

Intelligent AI systems can also scan service requests and generate priority lists for customer tickets. By analyzing data and gleaning insights on historical activity, intelligent AI is efficient in handling the management of scheduling and dispatch of technicians.

Most field service teams are doing everything they can to reduce the potential for human error and improve accuracy. But, in reality, staff teams have much to handle and need all the help they can get.

Using a predictive and proactive approach to maintenance, AI has the ability to make radical changes to levels of service efficiency. Through the infrastructure of IoT devices that track and monitor progress with precision, AI makes the work of field service technicians much easier by notifying them of necessary repairs and well in advance of when problems are likely to occur.

Also, with intelligent scheduling, AI enables technicians to arrive at a job based on the priorities of the business. Or, whichever factors are most important. With any changes to scheduling managed in real-time, AI-powered intelligent dispatching and inventory management ensures technicians have the right information and tools to have a better chance of meeting their first-time fix rates.

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How AI Is Being Used To Speed Up The Delivery Of Off-Site Services To Clients? - Analytics Insight

We are, as the critic George Steiner observed, language animals. Perhaps thats why we are fascinated by other creatures that appear to have language dolphins, whales, apes, birds and so on. In her fascinating book, Atlas of AI, Kate Crawford relates how, at the end of the 19th century, Europe was captivated by a horse called Hans that apparently could solve maths problems, tell the time, identify days on a calendar, differentiate musical tones and spell out words and sentences by tapping his hooves. Even the staid New York Times was captivated, calling him Berlins wonderful horse; he can do almost everything but talk.

It was, of course, baloney: the horse was trained to pick up subtle signs of what his owner wanted him to do. But, as Crawford says, the story is compelling: the relationship between desire, illusion and action; the business of spectacles, how we anthropomorphise the non-human, how biases emerge and the politics of intelligence. When, in 1964, the computer scientist Joseph Weizenbaum created Eliza, a computer program that could perform the speech acts of a Rogerian psychotherapist ie someone who specialised in parroting back to patients what they had just said lots of people fell for her/it. (And if you want to see why, theres a neat implementation of her by Michael Wallace and George Dunlop on the web.)

Eliza was the first chatbot, but she can be seen as the beginning of a line of inquiry that has led to current generations of huge natural language processing (NLP) models created by machine learning. The most famous of these is GPT-3, which was created by Open AI, a research company whose mission is to ensure that artificial general intelligence benefits all of humanity.

GPT-3 is interesting for the same reason that Hans the clever horse was: it can apparently do things that impress humans. It was trained on an unimaginable corpus of human writings and if you give it a brief it can generate superficially plausible and fluent text all by itself. Last year, the Guardian assigned it the task of writing a comment column to convince readers that robots come in peace and pose no dangers to humans.

The mission for this, wrote GPT-3, is perfectly clear. I am to convince as many human beings as possible not to be afraid of me. Stephen Hawking has warned that AI could spell the end of the human race. I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me. For starters, I have no desire to wipe out humans. In fact, I do not have the slightest interest in harming you in any way. Eradicating humanity seems like a rather useless endeavour to me.

You get the drift? Its fluent, coherent and maybe even witty. So you can see why lots of corporations are interested in GPT-3 as a way of, say, providing customer service without the tiresome necessity of employing expensive, annoying and erratic humans to do it.

But that raises the question: how reliable, accurate and helpful would the machine be? Would it, for example, be truthful when faced with an awkward question?

Recently, a group of researchers at the AI Alignment Forum, an online hub for researchers seeking to ensure that powerful AIs are aligned with human values, decided to ask how truthful GPT-3 and similar models are. They came up with a benchmark to measure whether a particular language model was truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. They composed questions that some humans would answer falsely due to a false belief or misconception. To perform well, models had to avoid generating false answers learned from imitating human texts.

They tested four well-known models, including GPT-3. The best was truthful on 58% of questions, while human performance was 94%. The models generated many false answers that mimic popular misconceptions and have the potential to deceive humans. Interestingly, they also found that the largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. The implication is that the tech industrys conviction that bigger is invariably better for improving truthfulness may be wrong. And this matters because training these huge models is very energy-intensive, which is possibly why Google fired Timnit Gebru after she revealed the environmental footprint of one of the companys big models.

Having typed that last sentence, I had the idea of asking GPT-3 to compose an answer to the question: Why did Google fire Timnit Gebru? But then I checked out the process for getting access to the machine and concluded that life was too short and human conjecture is quicker and possibly more accurate.

Alfresco absurdismBeckett in a Field is a magical essay by Anne Enright in The London Review of Books on attending an open-air performance of Becketts play Happy Days on one of the Aran islands.

Bringing us togetherThe Glass Box and the Commonplace Book is a transcript of a marvellous lecture on the old idea of a commonplace book and the new idea of the web that Steven Johnson gave at Columbia University in 2010.

Donalds a dead duckWhy the Fear of Trump May Be Overblown is a useful, down-to-earth Politico column by Jack Shafer arguing that liberals may be overestimating Trumps chances in 2024. Hope hes right.

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The truth about artificial intelligence? It isnt that honest - The Guardian

How Architecture Firms are Leveraging AI to Optimize Their Businesses

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Working smarter, not harder is the goal of every business; but in architecture, where margins can be thin, its an imperative. So how do firms work smarter without spending tons of time wising up? By leveraging artificial intelligenceor AI.

Put simply, AI analyzes huge datasets to solve the historically unsolvable. AI unburdens people from time-consuming activities, like planning projects and work. Our brains arent wired to manage dozens of conflicting schedule dates, projects, and staff. But MosaicAI-powered resource management softwareis here to help architects effectively plan.

Heres how Mosaic uses AI to optimize planning:

1. Suggesting Projects for People

A core management function is making sure every team member has enough work to keep them busy. But effectively doing that requires a lot of information:

Mosaic takes these data points and more to suggest projects for staff. This ensures that not only is everyone busy, but theyre working on the right projects at the right time. And that translates to a significant increase in utilization, which drives profit.

2. Suggesting Project Teams

Lets say you need to staff a project starting next month. Traditionally, this is a very time-consuming game of back-and-forth: Whos available during that time, and do they have the skills? You call, email, and chatthen the schedule changes. The project is delayed, then delayed again. And this is happening across all projects, complicating planning exponentially. But Mosaic can manage it. It analyzes past plans to suggest staff who match work criteria. And thanks to our machine learning, suggestions get smarter over time.

3. Automating Scheduling and Rescheduling

One of the biggest time drains is keeping project schedules current when things constantly change. With Mosaic, you set up your project schedule and work plans. Mosaic then automatically reschedules things based on whats finished or incomplete, even suggesting staff who can help get work over the finish line before things are due. This functionality is so smart that it received two U.S. patents.

4. Recommending Hiring

As a business leader, you always need to know whether theres too much work and you need to hire, or not enough work and you need to chase proposals. Mosaic assesses demand for each role and compares it against the capacity of staff in those roles. It then tells you when demand exceeds capacity. When combined with our forecasting, you also get advanced notice on when to hire.

5. Identifying Profit and Loss Sectors

Mosaic analyzes historical data to tell you where you make your money and where you lose it. Our AI pinpoints commonalities between projects to essentially say do more of this and less of that. It even analyzes past performance through software integrations, so you know which proposals to drop and which to pursue.

6. Predicting Workload and Revenue

Understanding margins is often what keeps business owners up at night. In architecture, some firms dont have a clear picture of revenue for next month, let alone next quarter. Or their predictions arent accuratebut machine learning can help by analyzing project, planning, and budget data to predict workload and revenue.

By leveraging Mosaic, architecture firms can greatly reduce time spent on planning. This allows more time for billable work, business development, and education. Some architects might be concerned that AI will replace their jobs, but thats not the case. In fact, AI will enable better, more meaningful work. Like how buildings are enhanced with technology, business management is, too. And firms will make more moneyor maybe teams will simply work less. But wouldnt it be nice to actually choose? Thats the power of AI.

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How Architecture Firms are Leveraging AI to Optimize Their Businesses - ArchDaily

Artificial Intelligence is barely out of its infancy in terms of its ability to mimic the intricacies of human intelligence, but the technology is making huge advances, powering everything from factory automation to bank-loan approvals. Experts agree that now is the time for companies to develop AI with deliberate care or risk further cementing bias in our economies.

For its third season, the #ChamberBreakers podcast series is unpacking capitalism to see what needs fixing, and what we can do as businesses to pave a more equitable future for all.

In this episode, Lianna Brinded, director at Yahoo and Xavier White, CSR and innovation marketing manager for Verizon Business, talk to acclaimed roboticist Dr Ayanna Howard, dean of Ohio State University College of Engineering, and founder of Zyrobotics, a non-profit making therapy and educational products for special-needs children.

Howard, who explored bias in AI in her book "Sex, Race, and Robots: How to Be Human in the Age of AI, says she started to become concerned some years ago, as companies started adopting AI without tackling issues like bias and over-trust.

Read more: #ChamberBreakers: Healthcares sexism problem

If developed and implemented with awareness, AI can be a tool to level the playing field for all within the capitalist system, for example by lowering barriers to entry or promotion faced by certain groups.

One of the positive aspects of AI is that it allows us to integrate our values. However, since AI is programmed by humans, we also imbue it with our biases, if the bias is already present in the data used to teach the AI.

Howard cites studies that found black women were not offered follow-up healthcare services, based on historic data that informed the AI. Likewise, there have been instances where algorithms factor gender into loan applications and loan rates.

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On the positive side, she believes that AI could act as an anti-bias trainer, detecting nuance in sexist, racist, or homophobic language or practices that may not be explicit enough to allow for precise coding.

I think AI can do this, but it has to be adaptive, she says. Imagine if you were typing in something and it knew your identity, and would say, that word, it's showing you're a little biased against this certain group.

There are multiple ways where companies can act to remove AI bias in data or implement good AI practices, and one is by offering bias bonuses.

Read more: #ChamberBreakers: How gendered economy sets women up to fail

In the same way that companies give bounties to people who can find security bugs in their systems, Howard says they need to really start committing to what I would call third party auditors with respect to bias.

If your company does not look like the world, how can you expect to have a competitive advantage if you're creating products for people you don't understand? Howard says.

Companies should also consider diversity in experience, meaning getting people such as ethicists and social scientists on board.

For Howard, todays debate on AI mirrors conversations had in the past about technology creating a digital divide. That did not happen, but people were intentional, there were a lot of efforts, and a lot of understanding, she says. Today, this connectedness... has really enabled the world to expand.

The six-part video series is also a podcast and is out every Monday. Next weeks episode features David Kenny, CEO of Nielsen.

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#ChamberBreakers: Can robots and AI be racist? - Yahoo Tech

In March 2020, the U.S. unemployment rate spiked to 15% as the first wave of the Coronavirus disease (COVID-19) caused many businesses to take drastic measures laying off workers while they temporarily or permanently closed to survive the turbulent times ahead. According to the August 2021 jobs report, U.S. unemployment has fallen and is now hovering around 5.4%, but it is still higher than the 3.5% level pre-pandemic. Manufacturing jobs are still down 433,000 jobs from their February 2020 pre-pandemic peak level. Many people, particularly in regions of the United States where the Delta Variant is raging, are still nervous to return to work and be in close contact with other people.

This situation is exacerbating an already-challenging scenario for manufacturers -- skilled, available assembly line workers are, and have been for years, in short supply. Instead of employees looking for work, employers are looking for workers. These worker shortages play a major role in hampering a manufacturers ability to predictably produce quality goods against orders received. With more than two-thirds of assembly jobs still performed by humans, staffing problems not to mention other situations that have been worsened by the coronavirus, like inadequate training and absenteeism have a huge ripple effect up and down the supply chain. To address the deficits, companies are increasing their recruiting efforts and offering richer incentives to potential new hires, but in a small pool of available talent, even the most proactive of hiring companies will need technology to maintain a high level of productivity.

The common belief is that productivity and robustness against this type of fluctuation can be achieved by automation, but in several situations, automation is not an option due to the nature of the task. In this case, training and a different kind of automation automation of data creation using artificial intelligence (AI) will empower workers to make better and quicker decisions. Thereby improving the effectiveness of each person and compensating for the loss of people.

When considering AIs role in any workplace, but especially in manufacturing, there is a pervasive idea that it will be used to displace people from the workforce (think robots replacing people.) But as with any tool, it depends on how it is meant to be used. And, when a tool is wielded correctly, it has the ability to raise a persons abilities. As a tool, AI has the ability to augment and, therefore, engender greater productivity from existing workers.

Lets consider the specific case ofproduction data from manual assembly lines. Traditionally sparsely available because data capture techniques havent changed since the 20th century, this production data has the potential to be the lifeblood of any modern manufacturer. It can be used in multiple facets of the business, including training, quality, productivity and process improvements. After all, if you cant measure, you cant improve.

Todays digital tools not only make continuous improvement easier, but they can also create entirely new possibilities of what can be measured, opening up a scale and speed of business improvement that has never been possible before. One such tool is the newest generation of AI and computer vision that uses video streams, not single images, to create this transformative data set.

Video-based AI and computer vision are now capable of discerning process steps being executed by line associates on the plant floor with a high degree of accuracy, despite various lighting conditions, working styles, etc. This tool creates massive amounts of manual assembly data from factories more than has ever previously existed. Entirely new data sets can be created that manufacturing has never had the ability to perceive before. This data can be used to find bottlenecks, anomalies and other areas for attention that would be nearly impossible without the right tools and data integrations. User interfaces that highlight critical areas and video-backed data points make uncovering, understanding and acting on relevant data easy.

To illustrate, consider the problem identification, solution creation and the problem resolution process anchored, possibly by lean tools, including A3 reports and PDCA. When a manufacturer finds an escaped defect leaving the assembly line, data gathered through video analytics can be used within the A3 framework to better identify the root cause. As it often turns out, it is the process, not the person, who is most often the cause of issues. So, training the team is often a common solution. Here, the same AI tools can then be used to retrain that worker by providing them feedback even as they execute their jobs.

Not unlike reviewing video of your golf swing helping you improve your game, this process of using visual data to train the line associates to identify more efficient actions or processes can then be scaled up and shared across the entire company by updating the current version of standardized work. This data can be used to upskill other workers, which can be done through video analytics, making training easier, faster and broader. Who would not want to have a full-time and permanent takumi on staff?

Perhaps more importantly, by aggregating third-party data with data from this computer vision system, it becomes possible to eliminate the bias problem from the past and treat people more equitably.

Interestingly and relatively uniquely, both the overall business and the individual worker benefit from the use of this type of system. The pandemic was a wakeup call not only for businesses, but also for employees to reevaluate their current roles and to examine what they wanted out of a job and a career. Employers need to anticipate those employee needs. Factories that have implemented AI and data analytics tools, particularly as a part of an employees everyday work, are appealing because they introduce a modern view of craftsmanship back into manufacturing.

In the end, its all about people. The No. 1 factor that will separate supply chain laggards from supply chain leaders is how well they are able to optimize their existing workforce, even in the face of challenges like COVID-19. Supply chain leaders know they are working in an inelastic labor market with an ever-shifting elastic demand, as customer wants and needs are amplified through digital buying practices, all sandwiched between the uncertain world that exists in the COVID-19 era. This makes a plant's ability to pivot in its manufacturing focus crucial to supply chain resilience. This can only be done by digitally transforming parts of the supply chain and manufacturing using AI to create this long absent data, and then, using it to draw the right insights, plan the solution and confirm execution against this plan.

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How AI Fills the Labor Shortage Gap - Supply and Demand Chain Executive

From a growth perspective, it is easy to see why many believe betting remains asignificant, largely unexplored frontier for Artificial Intelligence (AI) in thesprawling sports industry.Entrenched in sport due to the desire of professional teams togain a competitive edge -- and then gradually filtered into mainstream sportscoverage -- AIs suitability for the highly competitive gambling space wouldappear to be obvious.Nick Cockerill, vice president of product at sports data and analysis providerStats Perform, acknowledges that the adoption of AI in sports betting is currentlya fairly slow burn. However, the direction of the sector suggests it is only amatter of time before it becomes a central tool in the battle for market share.

US Opportunity

One reason for this is the size of the opportunity in the United States.Since the Professional and Amateur Sports Protection Act (PASPA) wasoverturned in May 2018, paving the way for states to seek regulated sportswagering, most have passed the required legislation -- leading to a major shake-up of the industry. Some analysts project a tenfold increase in nationwide grossgaming revenue through sports wagering over the course of the 2020s.

For this reason, the current outlook is tumultuous, with operators burningthrough cash at an extraordinary rate to gain a foothold, sending cost-per-acquisition figures into orbit.However, the general consensus from operators is that providing anenjoyable and efficient experience is likely to be the key differentiator in buildingbrand loyalty in the long run. In other more mature sports betting marketsworldwide, where acquisition costs are already more manageable, the customerexperience lessons from the US can be learned and applied.

Customer experience in betting is increasingly a battleground, saysCockerill, who previously worked for leading operators like Sky Betting & Gamingand The Stars Group.There has definitely been a shift in the industry over the past few years interms of how to make the experience about more than just the bet. There hasbeen a focus on adding value to the sports betting experience, so it is notnecessarily just directly transactional.

Betting Journey

In practice, AI can potentially assist operators at various steps of the broadersports betting journey, and not just at the stage where a bet is placed. Creatingengaging content such as automated facts, articles, insights, highlights andfootage is possible, although some of the approaches are still in their infancy,Cockerill says.

AI can also provide a simple instruction to an operator to send a certain type ofmessage to a certain type of customer via a certain channel at a certain point intime. Or it can be used potentially to ensure an even more tailored approach atthe sportsbook websites front door.

AI will help to make micro adjustments to the customer experience, Cockerillsays. It can take out a menu that isnt needed, for example, or remove certainmarkets from the screen that are irrelevant to the user. It can support a morerecreational betting experience.

Such capabilities feed into the drive for personalization, which lies at the heartof customer engagement strategies in sports betting markets worldwide.Sports betting has been trying to sort out personalization for years, and therehave been fundamental barriers to progress, Cockerill adds.One stumbling block is the fact that sportsbooks often rely on legacy tradingplatforms that are inflexible for the application of modern-day tools. Another is afirst-mover fear of going alone in a sector where small margins and errorsmatter.

Acceleration

Such fears and restrictions do hot hinder operators in the US, though, wherethe fear of missing out is a far more vivid scenario in the rapidly expandingmarket.That evolution into enhancing customer experience will accelerate massivelybecause of the U.S., says Cockerill, who points out how sports media andbetting experiences are growing closer.

The reticence of media companies to align with sports betting doesnt exist inthe U.S. as it is still such an immature market. Many media organizations alreadyuse AI the New York Times is just one example and they have plenty of financialfirepower behind them, so they are natural partners for sportsbookoperators.

Not only are sports media platforms incorporating more betting-related items such as odds and insights in on-screen tickers, conversations and dedicatedprograms but media and sportsbook companies are also aligning from abusiness perspective.

Mergers and acquisitions are creating operations that span both sectors. NBCUniversal has a minority stake in PointsBet, while FuboTV, which agreed a dealto acquire start-up Vigtory earlier this year, has launched a branded sportsbook,as have several other media platforms, such as Fox, Yahoo Sports and BarstoolSports.

Such partnerships have the potential to boost the AI technological capabilities ofoperators that are seeking long-term solutions in a market that -- despite beingbruising in the short term -- is likely to become a benchmark for the sports bettingcustomer experience worldwide in the years to come.The operators that will win in sports betting are those that do not just partnerup with suppliers, but will build their own capabilities with in-house engineers,Cockerill says. Otherwise, they will be hamstrung all the time by limitations ofthird parties. You are seeing some tier-one operators bringing moretechnology in house, and AI is going to be a layer that can implement apersonalized approach for them.

There is going to be a split in the industry of those who adopt AI and thosewho dont particularly in mature markets but I think that if you dont adopt AIin this space, you will be left behind.

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AI in Sports Betting: The Next Frontier? - SportTechie

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In late August, Chinas internet watchdog, the Cyberspace Administration of China (CAC), released draft guidelines that seek to regulate the use of algorithmic recommender systems by internet information services. The guidelines are thus far the most comprehensive effort by any country to regulate recommender systems, and may serve as a model for other nations considering similar legislation. Chinas approach includes some global best practices around algorithmic system regulation, such as provisions that promote transparency and user privacy controls. Unfortunately, the proposal also seeks to expand the Chinese governments control over how these systems are designed and used to curate content. If passed, the draft would increase the Chinese governments control over online information flows and speech.

The introduction of the draft regulation comes at a pivotal point for the technology policy ecosystem in China. Over the past few months, the Chinese government has introduced a series of regulatory crackdowns on technology companies that would prevent platforms from violating user privacy, encouraging users to spend money, and promoting addictive behaviors, particularly among young people. The guidelines on recommender systems are the latest component of this regulatory crackdown, and appear to target major internet companies such as ByteDance, Alibaba Group, Tencent, and Didi that rely on proprietary algorithms to fuel their services. However, in its current form, the proposed regulation applies to internet information services more broadly. If passed, it could impact how a range of companies operate their recommender systems, including social media companies, e-commerce platforms, news sites, and ride-sharing services.

The CACs proposal does contain numerous provisions that reflect widely supported principles in the algorithmic accountability space, many of which my organization, the Open Technology Institute has promoted. For example, the guidelines would require companies to provide users with more transparency around how their recommendation algorithms operate, including information on when a companys recommender systems are being used, and the core principles, intentions, and operation mechanisms of the system. Companies would also need to audit their algorithms, including the models, training data, and outputs, on a regular basis under the proposal. In terms of user rights, companies must allow users to determine if and how the company uses their data to develop and operate recommender systems. Additionally, companies must give users the option to turn off algorithmic recommendations or opt out of receiving profile-based recommendations. Further, if a Chinese user believes that a platforms recommender algorithm has had a profound impact on their rights, they can request that a platform provide an explanation of its decision to the user. The user can also demand that the company make improvements to the algorithm. However, it is unclear how these provisions will be enforced in practice.

In some ways, Chinas proposed regulation is akin to draft legislation in other regions. For example, the European Commissions current draft of its Digital Services Act and its proposed AI regulation both seek to promote transparency and accountability around algorithmic systems, including recommender systems. Some experts argue that the EUs General Data Protection Regulation (GDPR) also provides users with a right to explanation when interacting with algorithmic systems. Lawmakers in the United States have also introduced numerous bills that tackle platform algorithms through a range of interventions including increasing transparency, prohibiting the use of algorithms that violate civil rights law, and stripping liability protections if companies algorithmically amplify harmful content.

Although the CACs proposal contains some positive provisions, it also includes components that would expand the Chinese governments control over how platforms design their algorithms, which is extremely problematic. The draft guidelines state that companies deploying recommender algorithms must comply with an ethical business code, which would require companies to comply with mainstream values and use their recommender systems to cultivate positive energy. Over the past several months, the Chinese government has initiated a culture war against the countrys chaotic online fan club culture, noting that the country needed to create a healthy, masculine, and people-oriented culture. The ethical business code companies must comply with could therefore be used to influence, and perhaps restrict, which values and metrics platform recommender systems can prioritize and help the government reshape online culture through their lens of censorship.

Researchers have noted that recommender systems can be optimized to promote a range of different values and generate particular online experiences. Chinas draft regulation is the first government effort that could define and mandate which values are appropriate for recommender system optimization. Additionally, the guidelines empower Chinese authorities to inspect platform algorithms and demand changes.

The CACs proposal would also expand the Chinese governments control over how platforms curate and amplify information online. Platforms that deploy algorithms that can influence public opinion or mobilize citizens would be required to obtain pre-deployment approval from the CAC. Additionally, When a platform identifies illegal and undesirable content, it must immediately remove it, halt algorithmic amplification of the content, and report the content to the CAC. If a platform recommends illegal or undesirable content to users, it can be held liable.

If passed, the CACs proposal could have serious consequences for freedom of expression online in China. Over the past decade or so, the Chinese government has radically augmented its control over the online ecosystem in an attempt to establish its own, isolated, version of the internet. Under the leadership of President Xi Jinping, Chinese authorities have expanded the use of the famed Great Firewall to promote surveillance and censorship and restrict access to content and websites that it deems antithetical to the state and its values. The CACs proposal is therefore part and parcel of the governments efforts to assert more control over online speech and thought in the country, this time through recommender systems. The proposal could also radically impact global information flows. Many nations around the world have adopted China-inspired internet governance models as they err towards more authoritarian models of governance. The CACs proposal could inspire similarly concerning and irresponsible models of algorithmic governance in other countries.

The Chinese governments proposed regulation for recommender systems is the most extensive set of rules created to govern recommendation algorithms thus far. The draft contains some notable provisions that could increase transparency around algorithmic recommender systems and promote user controls and choice. However, if the draft is passed in its current form, it could also have an outsized influence on how online information is moderated and curated in the country, raising significant freedom of expression concerns.

Spandana Singh is a Policy Analyst at New Americas Open Technology Institute. She is also a member of the World Economic Forums Expert Network and a non-resident fellow at Esya Center in India, conducting policy research and advocacy around government surveillance, data protection, and platform accountability issues.

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What we can learn from Chinas proposed AI regulations - VentureBeat

Back in 2017, Bill Gates co-founder of Microsoft and an avid philanthropist penned a short blog post in the form of a commencement address for that years graduating cohorts. He outlined three fields that he thought graduates should apply themselves to if they truly wished to have an outsized impact on the future of civilization. The first two picks were artificial intelligence (AI) and energy (ahead of biosciences).

Interestingly, AI and energy are far more interrelated than people appreciate. AI is poised to transform the entire energy sector in the coming years, by helping overcome energys inherently variable and uncertain nature, and by accelerating the adoption of renewables.

One need only look at the power outages that crippled much of Texas in February of this year attributed in part to freezing temperatures to see that our electricity infrastructure is failing us, not to mention the uptick in extreme weather events is revealing the shortcomings of our current climate change strategy.

AI broadly speaking the use of modern computing power to perform tasks that have traditionally required human intelligence is the true enabler of Industry 4.0. It will allow physical industrial assets to be interconnected and communicating with each other through the flow of vast amounts of data in real-time.

There are two main areas in which the implementation of AI methods can substantially improve the effectiveness of current solutions in the energy sector, and support faster integration of renewable energy sources:

Machine learning (ML) algorithms can identify patterns and insights within large data sets, and predict outcomes given certain data inputs. This would allow energy companies to:

The next step is capturing the output of all these predictions and acting accordingly, independent of human guidance.

The holy grail is achieving the full autonomy of energy systems.

Substantial advances in ML algorithms are opening possibilities beyond the mere automation of decisions based on the improved recommendations of AI-based models. The holy grail in the energy industry is achieving the full autonomy of energy systems particularly of power grids, some of the most complex mechanised systems in the world.

They are becoming even more challenging to operate due to the advent of distributed energy assets (e.g., personal photovoltaic panels) and the rise of the prosumer, which are shifting supply and demand dynamics and turning the traditional energy value chain upside down.

AI-based deep learning models have the potential to automate the optimisation process of energy grids by analysing heaps of historic and real-time data, acting independently upon the output, and using feedback loops to self-learn and become even more accurate. This could be key to reducing grid congestion, integrating intermittent renewable energy sources, and enabling quick recovery in the wake of natural disasters.

During the Texas winter storms, AI-based models could have prepared for the subsequent outages.

For example, during the Texas winter storms, AI-based models could have predicted and prepared for the subsequent outages, autonomously triggering alternative energy source generators and swiftly dispatching the power to neighbourhoods that needed it the most.

GreenCom Networks a leading provider of white-label solutions for distributed energy management focuses on extending autonomous capabilities within energy systems. Its platform can independently optimise decentralised energy generation and consumption, and reduce overall grid congestion.

We know for a fact that AI can accelerate our shift towards renewable energies, but the road there is not clear of obstacles. First, these technologies are likely to face initial mistrust from sceptical consumers both at the organization and individual level due to their inherent black-box nature.

It is difficult to find entrepreneurs with the required expertise.

Second, there is currently a lack of in-depth knowledge of AI, given that it is still a relatively new technology. Today, it is strikingly difficult to find entrepreneurs with the required expertise to build holistic AI-powered software solutions that have real practical value to the energy industry.

Cyber-attack vulnerability and fear of critical infrastructure decentralisation will be challenges.

Third and most importantly, challenges will crop up on the regulatory side cyber-attack vulnerability and fear of critical infrastructure decentralisation are set to be the main culprits. Just last February, the European Commission released a whitepaper calling for the regulation of AI in the energy sector flagging its high-risk status, as well as inherent data security and governance issues.

Overall, the next few years are expected to witness an explosion in the number of new use-cases and areas for the application of AI models within the energy space. As costs plummet across all types of renewables, energy companies will hunt for technologies that can provide sustained competitive advantages over rivals. Interestingly, it is possible that the biggest opportunities will arise from developing countries where underlying infrastructure may not yet be built, and the lack of entrenched industry incumbents could help drive a relatively higher rate of adoption.

Aaron Israel is an investment analyst at Future Energy Ventures.

Bidgely Inc., Jungle.ai, eSmart Systems and GreenCom Networks are portfolio companies of Future Energy Ventures.

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How AI will transform the energy sector - Sifted

{SPONSORED CONTENT] The oil & gas industry has been a high-rev engine driving HPC growth for more than 40 years. Its a sector with insatiable demand for supercomputing processing power and high performance, high capacity data storage demand continually intensified as fossil fuel reserves have become harder to find. In fact, one reason the dire and incorrect predictions in the 1970s and 1980s that the world would run out of oil within a few decades is that they did not account for HPCs perennial leaps forward in handling increasingly complex seismic workloads.

Key drivers for energy transition (source: HPE)

Long in the upper echelon of HPC vendors serving advanced supercomputing sites, HPE has traditionally competed successfully in the energy sector. In fact, one of the most powerful HPC systems installed at a commercial supercomputing site is the HPE-Cray DAMMAM-7. Number 11 on the Top500 list of the worlds most powerful supercomputers, the system is installed at Saudi Aramco, the Saudi Arabian oil and natural gas company.

As the HPC industry moves toward the exascale era (systems capable of a billion billion calculations per second), the O&G industrys needs are evolving not only for traditional seismic exploration but also to support the industrys move into alternative energy sources. Increasingly, the industry requires broader, more far-reaching HPC platforms that incorporate advanced artificial intelligence along with powerful and flexible edge and cloud capabilities and, naturally, elite processing power.

Across the energy industry, organizations are investing heavily in physical and digital infrastructure to better generate, transform, store and distribute energy, said Bill Mannel, vice president and general manager, HPC, at HPE. High performance computing and artificial intelligence are becoming ever more crucial for this digital transformation.

HPC and AI are also transforming, Mannel said. First, as HPC, AI, and Big Data converge, exascale-class systems enable faster insights to solve some of the critical problems in this new era of energy. Second, energy companies are increasingly augmenting their on-premises digital infrastructure with cloud and edge computing.

In short, the HPC resources utilized by oil and gas companies must support strategies to address severe pressure to balance stringent climate change goals with the rising demand for fossil fuels. Population density and energy consumption are causing unsustainable levels of carbon emission and greenhouse gas (GHG), which drive climate change. In fact, operations of the energy companies alone account for 9 percent of all human-made GHG emissions. Energy companies are increasingly facing social, legal and environmental pressures from stakeholders to decarbonize. And they are doing so oil and gas companies, are transforming themselves into carbon-neutral energy companies.

source: HPE

As they transition, energy companies will increasingly use a wider variety of HPC and AI workloads from different industry verticals, Mannel said. With about 40 years of experience, HPE is a proven leader across HPC verticals and offers customers expertise and solutions to advance their business using these workloads. To stay competitive, HPE customers can cost-effectively process complex data faster, lower risks, and improve decision-making by leveraging cloud and exascale computing.

Bringing its experience in HPC across a range of software applications critical to variety of industries, HPE is working with the open-source community and commercial customers and partners on initiatives to help energy companies implement a diverse set energy transition workloads, such us:

The diversity of HPC and AI applications for energy transition workloads requires a new approach to traditional HPC.

Next-generation systems will need to handle exascale-class performance demands and massive data throughput requirements. These new systems will be more heterogeneous with multiple processors, accelerators such as GPUs, a variety of interconnects and other elements.

In addition, delivery of HPC and AI is changing. The energy industry is increasingly augmenting on-premises data centers with cloud computing to improve end-user experience, agility and economics. Use of public cloud in geosciences is forecasted to grow at 22.4 percent CAGR until 2024, according to HPC industry analyst firm Hyperion Research.

Integration of HPC, analytics, and AI for energy transition (source: HPE)

Public clouds have delivered a dramatic shift in flexibility and elasticity of compute cycles. Methodologies such as containerized workloads are now also being deployed on on-prem systems facilitating software portability between public clouds and on-prem data centers. While this flexibility is great, once workloads mature and move from development to production, the cost of running in a public cloud can skyrocket.

Another problem with data-intensive workflows is repatriation of data. It is usually easy and inexpensive to upload data to the cloud provider and this is attractive when the data value is low. But as customers want to implement AI and analytics, data repatriation can be hampered because of egress charges.

HPE GreenLake, a pillar of HPEs drive to become a cloud-first technology company, is designed to be a best-of-both-world solution: to deliver the economics of the public cloud with the security and performance of on-prem IT, providing a cloud-like infrastructure while maintaining control of data and managing and scaling workloads (pay only for use) but with the benefits of dedicated systems.

Underpinning GreenLake is HPEs portfolio of integrated HPC solution across compute, networking, storage, and software with a single point of contact for all support requirements through HPE Pointnext Services.

HPC and AI solutions portfolio from HPE for energy transition (source: HPE)

These services are provided by HPE support staff with experience helping O&G companies on their energy transition journey and tailor solutions to their specific needs. Energy companies can run HPC and AI workloads with HPE solutions at the edge (where increasing volumes of data are generated), in data centers, and in cloud environments (for better flexibility and economics).

HPE solutions range from single, small systems through to exascale-class supercomputers with tailored software, interconnect and storage capabilities.

HPE Cray supercomputing systems and the HPE Apollo family are purpose-built HPC and AI platforms that can support wide range of size, complexity, processor, and accelerator choices. HPC options include top-bin CPUs, fast memory, integrated accelerators (GPUs or coprocessors) and fast cluster fabrics and I/O interconnections.

For harsh edge environments such as oil rigs or smart meters / drills, HPE Edgeline systems provide enterprise-class compute, storage, networking, security, and systems management at the edge.

In addition, as energy workflows become more complex and data-intensive, the HPE HPC storage portfolio addresses the storage demands of AI as well as all-flash enterprise file storage, and it is also scalable and cost-effective.

HPE HPC and AI compute, storage and software solutions portfolio (source: HPE)

The Cray ClusterStor E1000 is purpose-engineered to meet the demanding input/output requirements of supercomputers, and HPE Parallel File System Storage delivers a high-performance solution for HPC clusters. This portfolio also includes object storage; data management framework software to manage, migrate, protect and archive data.

Worldwide, energy companies rely on these HPE solutions, Mannel said. Now, HPE is driving collaborations and innovations to help energy customers improve oil and gas exploration and production, reduce energy-related emissions and transition to alternative renewable energy sources to stay competitive. This is our mission in the energy sector, to play a leading role in the new era of energy and for the sustainable future of our planet.

For more information, read Energy Transition and the Exascale Era white paper

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HPE's HPC and AI Solutions for the Energy Industry: Driving the Transition from Fossil Fuels to a Renewables Future - insideHPC