Category Archives: Ai

Vistra, a major U.S. power producer, had a problem. For its plants to operate efficiently, workers had to continuously monitor hundreds of different indicators, tracking temperatures, pressures, oxygen levels, and pump and fan speeds and they had to make adjustments in real time. The process involved a huge amount of complexity, and it was too much for even the most skilled operator to get right all the time. To address this challenge, the plant installed an AI-powered tool a heat-rate optimizer that analyzed hundreds of inputs and generated recommendations every 30 minutes. Result: a 1% increase in efficiency. That may not sound like much, but it translates into millions in savings as well as lower greenhouse gas emissions.

Companies in a wide range of industries are trying to integrate analytics and data to improve their operations. Wayfair, the e-commerce company, was an early mover in shifting its data to the cloud and investing in machine learning. When Covid-19 hit, and rapid changes to consumer demand followed, it was able to optimize container ship logistics, continually adjusting what goods were sent to which ports. Result: an astonishing 7.5% reduction in inbound logistics costs.

Not all companies have been as successful as Wayfair, however. In fact, top performers can have more than twice the impact in half the time compared to the average company implementing machine intelligence. Why do some companies do so much better than others?

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To answer that question, McKinsey and MITs Machine Intelligence for Manufacturing and Operations (MIMO) studied 100 businesses in sectors from automotive to mining. Through interviews, research, and a survey, we sought to get a sense of how they used digital, data analytics, and machine intelligence (MI) technologies; what they wanted to achieve; and how they kept track of their progress. By looking at 21 performance indicators across nine categories strategy, opportunity focus, governance, deployment, partnerships, people, data execution, budget, and results we were able to divide the 100 companies into four categories: leaders, planners, executors, and emerging organizations to identify the relationships between actions taken and investments made, and tangible and sustainable outcomes.

Any company with ambitions to gain from advanced digital technologies has the opportunity learn from best practice approaches, whether it is a planner, an executor, or an emerging company today. We take a look beyond the top-level numbers to explore the underlying drivers of success.

The race to leverage data and analytics could be won with multiple coordinated actions rather than any single bold move. All four segments leaders, planners, executers, and emerging companies are operating in a dynamic space where the bar is rising and the number of machine learning use cases will continue to increase and embed themselves into business-as-usual.

Not everyone should strive to be a leader immediately; they should instead strive to move to the next better state.

Leaders are the highest performers and comprise about 15% of the sample. By investing in the right places, they have captured the largest gains from advanced digital technologies. Leaders are much more likely to have a defined process for the assessment and implementation of digital innovation. They are also more likely to follow that process regularly and to update it continually. As a result, they have achieved significantly larger improvements than the rest in 20 of the 21 key performance indicators evaluated and were in the top 25% in all nine performance categories.

Planners comprise about a quarter of the data set. Planners often have strong people skills and considerable data execution expertise; they are methodical and focused on making the right investments. In many cases, though, these havent paid off yet, though a few are on the cusp of joining the leaders. While some planners are able to point to successful implementations, several have been unable to crack the code on scaling the use cases that really count. Others are struggling to escape from the pilot purgatory McKinsey described in 2018.

Executors, approximately a third of the respondents, tap into the ever-increasing pool of expertise and work with partners to create specific solutions directed at the most promising opportunities. Then they implement these solutions as broadly as they can. Executors are results-oriented. They can and have achieved significant gains, despite building less infrastructure than the leaders or planners. On the other hand, they sometimes find it difficult to knit together disparate efforts into company-wide performance.

Emerging companies, about a quarter of the pool, have the lowest level of maturity and have seen the smallest gains; many are just getting started. Some emerging companies report moderate success with select use cases, but others are finding it difficult even to figure out where to invest. Few have the strategy, skills or infrastructure in place to go much further.

In general, we found that companies that succeeded in the deployment of advanced digital technologies did an honest assessment of where they were in terms of the nine performance indicators. On that basis, they were able to form a vision of where they wanted to be in three or four years. At the same time, they identified a few promising use cases to rack up quick wins. More specifically, the research identified five areas where the top performers stand out.

Machine intelligence is a strategic priority for leading companies. Many have built dedicated centers of excellence to support their implementation efforts, either within business units or as a centralized function to support the entire organization, ensure standards, and accelerate deployment. A dedicated and centralized support function also helps keep their digital programs on track and documents how their portfolio is progressing. Leaders are much more likely than lower-performing companies to have a defined process for the assessment of and implementation of digital innovation. For example, the pharmaceutical firm Bayer uses a well-documented governance process to deploy multiple applications at one plant, which it then rolled out across its network, resulting in a revenue lift.

However, leaders also recognize that change is inevitable in this fast-moving space. Most of the leaders in our data set continually refine and improve their processes, whereas executors and planners in our data set often get stuck, which limits the ability to scale successfully.

Leading organizations apply MI more widely and use more sophisticated approaches. For example, every single leader implemented MI in forecasting, maintenance optimization, and logistics and transportation. The leaders are also much more likely to adopt advanced approaches, such as the application of machine vision to product quality assurance. One biopharma player, Amgen, found that visual inspection system operations posed great opportunities to automate and leverage AI technologies. Amgen is developing a fully validated visual inspection system using AI that will boost particle detection 70% and cut false rejects by 60%.

While applications like these can have tremendous impact, these firms also realize that any long-term impact requires pulling multiple levers in concert, and that broad, enterprise-wide deployment is key.

Partnerships are common, often with academia, start-ups, existing technology vendors, and external consultants. Leaders, however, worked with a wider range of partners, and more intensively, in order to maximize speed and learning. For example, Colgate-Palmolive and Pepsico/Frito-lay, two consumer product companies worked with a systems vendor, Augury, deployed AI-driven machine health diagnostics on their production lines; in one case, this prevented an eight-day outage. Analog Devices, a semiconductor firm, collaborated with MIT to develop a novel MI quality-control that allowed it to identify which production runs and tools might have a fault. This meant that company engineers only had to review 5% of the process data they had to before.

Leaders, despite their higher capabilities, actually relied more on external partners to further accelerate their learning and time to impact.

Leading companies take steps to ensure that as many stakeholders as possible have the skills and resources they need to employ advanced digital approaches, rather than keeping this expertise the preserve of specialists. More than half train their front-line personnel in MI fundamentals, for example, compared to only 4% of other companies. McDonalds, a global quick-service restaurant, used MI to improve a wide range of operational tasks, from predicting customer response to forecasting real time footfall. The company adopted a hybrid approach to do this: its corporate center of excellence tests and develops new approaches before packaging them into easy-to-use tools that are made widely available. This system helps team members in the field understand the importance of good data and hone their problem identification skills.

It became clear that leaders view the use of data and analytics as deeply embedded to how they operate, rather than keeping it siloed and restricted to a few employees.

Leaders make data accessible. All of the leaders in our research give frontline staff access to data, compared to 62% of the rest. The leaders also all acquire data from customers and suppliers, and 89% share their own data back. Leading companies are almost twice as likely as others to enable remote access to data and to store a significant fraction of their data in the cloud. In short, the democratization of data is a critical aspect to the effective use of analytics. A good example comes from Cooper Standard, an automotive supplier. It requires teams to address data strategy early in the development process for new MI applications; this ensures that all uses cases are built on robust, well-managed data. This democratization of data stands in stark contrast to many firms where information is power and zealously guarded.

We found that the five areas governance, deployment, partnerships, people, and data were most effective when integrated into a playbook, often coordinated by a center of excellence. But first, companies need an honest assessment of their starting point across the nine dimensions. From there, a transition plan can start to take shape. Even if its rough, it assigns realist medium-term targets that account for the barriers to change skilled talent, investment capacity, and critical infrastructure such as the migration of data from legacy systems to the cloud. While the ambition can be boundless, the steps cannot be too small most leaders started with using data and simple tools to make decisions, then moved to more advanced techniques as they built maturity and familiarity with their data.

Despite the recent and significant advances in MI, the full scale of the opportunity is just beginning to unfold. And that brings us to one more important difference between the leaders and the rest: money. The leaders spent 30 to 60% more and they expected to increase their budgets 10 to 15%, while the others reported little or no rises. That means the gap between the leaders and the rest could actually widen.

Depending on its starting point, each companys path will be different. But in terms of what works, the leaders are showing the way.

The authors would like to thank Duane Boning, Erez Kaminski, Pete Kimball, Retsef Levi, Ingrid Millan, and Aaron Wang, along with MITs LGO program, for their contributions to this research and article.

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What Makes a Company Successful at Using AI? - Harvard Business Review

It is expected that, in 2022, artificial intelligence (AI) and machine learning will continue to impact healthcare in a multitude of ways, not the least of which are predictive modeling, diagnoses, patient experience, and drug discovery. Indeed, this is a promising turn of events, given the aging U.S. population and the dearth of doctors that is expected in the years ahead.

With about 10,000 Baby Boomers turning 65 every day, there are expected to be 95 million in the U.S. by 2060, nearly twice as many as in 2018. That will result in enormous strains on the healthcare system, especially given the fact that eight of every 10 American seniors suffer from at least one chronic condition and nearly seven in 10 suffer from two or more.

Couple that with the fact that there is expected to be a shortage of as many as 104,900 doctors by 2030and its easy to see that the need for time-saving (and ultimately life-saving) technology is only going to rise.

If there has been any good news to come out of the pandemic, it is that healthcare organizations have been forced to confront the emerging reality more quickly than they might have otherwise. According to the Accenture Digital Health Technology Vision 2021 report, 81% of the healthcare leaders believe their organizations digital transformation has been accelerated and 93% had made it a priority for 2021.

All indications are that thats going to continue, and that AI will be a particular focal point. The AI healthcare market is expected to rise in value to $39.5 billion by 2026, over six times more than it is currently.

While some have sounded cautionary notes about AI in this sectorconcerns we will cover later in this piecehere are the areas in which AI is making its presence felt:

Because AI can use the information provided by genome sequencing to project which compounds might work against a given target, European pharmaceutical companies, like the UK-based firm Exscientia, have been able to use the technology in an attempt to develop a Covid-19 vaccine. That led to Exscientia entering into a one-year agreement in September 2021 with the Bill and Melinda Gates Foundation to develop medication that is more accessible to patients and less susceptible to variants.

That same month, researchers at Baylor College of Medicine and Indias Amity University announced that they had developed an AI platform that can target not only Covid-19 but Chagas disease, an infectious ailment common in South America that results in damage to the heart and central nervous system.

AI is capable of analyzing data from various sourceselectronic health records, images, therapies, etc.and developing models that will predict the best possible approach to any given patients care journey, thereby streamlining operations and ensuring the most favorable outcomes.

IBM researchers, for example, have partnered with scientists from two healthcare systems to use AI to examine EHRs for clues about the warning signs of heart failure, which has long been the leading cause of death in the U.S. As a result, the team was able to develop a model that predicted this malady as much as two years earlier than previous methods.

This might be the most dramatic example of all, as AI applications have been developed that simplify EHR data entry/retrieval and enhance telemedicine, which has risen to prominence during the pandemic.

In October 2021, Amazon announced plans to install Alexa technology at various healthcare facilities throughout the U.S., enabling patients to remain connected not only with healthcare professionals but also their loved ones. (This calls to mind the PadInMotion technology we use at The Allure Group, a network of six New York City-based skilled nursing facilities, which, while not AI-based, accomplishes the same task.)

In addition, there is the expectation that, in 2022, 45% of all operating rooms will feature AI integration, making more efficient operations possible through the use of robotic surgery and the like.

Not to be forgotten, either, is the manner in which wearables and wellness apps enable users to track such things as heart rate and oxygen level while also alerting others to falls. Still in development is something called acoustic epidemiology, a form of AI that can determine the severity of a patients illness simply by hearing a person cough into a smartphone.

As noted, there are those who point out that AI, while obviously promising, still has its shortcomings in the healthcare sector. In an article on Innovation Origins, tech expert Jarno Duursma said there are many things he likes about the technology but noted that software does not necessarily provide all the answers. He cited AI that analyzes images of moles and makes a determination about whether it might be able to detect a malignancy or not as an example. Other experts cite privacy and security concerns.

Still, the upside of AI in this sector appears to be limitless and much-needed, given the short- and long-term challenges facing those working within it.

Joel Landauis the Chairman and Founder of The Allure Group, a New York-based healthcare group of skilled nursing and rehab facilities.

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The pluses and minuses of AI in healthcare - Fast Company

Figuring out when I needed more space or capacityit was a mess before. We needed to get information from so many different points when we were planning. We never got the number correct, says Cardoso. Now, I have an entire view of the infrastructure and visualization from the virtual machines to the final disk in the rack. AIOps brings visibility over the whole environment.

Before deploying the technology, Cardoso was where countless other organizations find themselves: snarled in an intricate web of IT systems, with interdependencies between layers of hardware, virtualization, middleware, and finally, applications. Any disruption or downtime could lead to tedious manual troubleshooting, and ultimately, a negative impact on business: a website that wont function, for example, and irate customers.

AIOps platforms help IT managers master the task of automating IT operations by using AI to deliver quick intelligence about how the infrastructure is doingareas that are humming along versus places that are in danger of triggering a downtime event. Credit for coining the term AIOps in 2016 goes to Gartner: its a broad category of tools designed to overcome the limitations of traditional monitoring tools. The platforms use self-learning algorithms to automate routine tasks and understand the behavior of the systems they monitor. They pull insights from performance data to identify and monitor irregular behavior on IT infrastructure and applications.

Market research company BCC Research estimates the global market for AIOps to balloon from $3 billion in 2021 to $9.4 billion by 2026, at a compound annual growth rate of 26%.1 Gartner analysts write in their April Market Guide for AIOps Platforms that the increasing rate of AIOps adoption is being driven by digital business transformation and the need to move from reactive responses to infrastructure issues to proactive actions.

With data volumes reaching or exceeding gigabytes per minute across a dozen or more different domains, it is no longer possible for a human to analyze the data manually, the Gartner analysts write. Applying AI in a systematic way speeds insights and enables proactivity.

According to Mark Esposito, chief learning officer at automation technology company Nexus FrontierTech, the term AIOps evolved from DevOpsthe software engineering culture and practice that aims to integrate software development and operations. The idea is to advocate automation and monitoring at all stages, from software construction to infrastructure management, says Esposito. Recent innovation in the field includes using predictive analytics to anticipate and resolve problems before they can affect IT operations.

Network and IT administrators harried by exploding data volumes and burgeoning complexity could use the help, says Saurabh Kulkarni, head of engineering and product management at Hewlett Packard Enterprise. Kulkarni works on HPE InfoSight, a cloud-based AIOps platform for proactively managing data center systems.

IT administrators spend tons and tons of time planning their work, planning the deployments, adding new nodes, compute, storage, and all. And when something goes wrong in the infrastructure, its extremely difficult to debug those issues manually, says Kulkarni. AIOps uses machine-learning algorithms to look at the patterns, examine the repeated behaviors, and learn from them to provide a quick recommendation to the user. Beyond storage nodes, every piece of IT infrastructure will send a separate alert so issues can be resolved speedily.

The InfoSight system collects data from all the devices in a customers environment and then correlates it with data from HPE customers with similar IT environments. The system can pinpoint a potential problem so its quickly resolvedif the problem crops up again, the fix can be automatically applied. Alternatively, the system sends an alert so IT teams can clear up the issue quickly, Kulkarni adds. Take the case of a storage controller that failed because it doesnt have power. Rather than assuming the problem relates exclusively to storage, the AIOps platform surveys the entire infrastructure stack, all the way to the application layer, to identify the root cause.

The system monitors the performance and can see anomalies. We have algorithms that constantly run in the background to detect any abnormal behaviors and alert the customers before the problem happens, says Kulkarni. The philosophy behind InfoSight is to make the infrastructure disappear by bringing IT systems and all the telemetry data into one pane of glass. Looking at one giant set of data, administrators can quickly figure out whats going wrong with the infrastructure.

Kulkarni recalls the difficulty of managing a large IT environment from past jobs. I had to manage a large data set, and I had to call so many different vendors and be on hold for multiple hours to try to figure out problems, he says. Sometimes it took us days to understand what was really going on.

By automating data collection and tapping a wealth of data to understand root causes, AIOps enables companies to reallocate core personnel, including IT administrators, storage administrators, and network admins, consolidating roles as the infrastructure is simplified, and spending more time ensuring application performance. Previously, companies used to have multiple roles and different departments handling different things. So even to deploy a new storage area, five different admins each had to do their individual piece, says Kulkarni. But with AIOps, AI handles much of the work automatically so IT and support staff can devote their time to more strategic initiatives, increasing efficiency and, in the case of a business that provides technical support to its customers, improving profit margins. For example, Sercompes Cardoso has been able to reduce the average time his support engineers spend on customer calls, reflecting better customer experience while increasing efficiency.

Download the full report.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Reviews editorial staff.

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The AI promise: Put IT on autopilot - MIT Technology Review

Opinions expressed by Entrepreneur contributors are their own.

The number of companies using AI has grown significantly in recent years, from small companies up to giants like Google and Facebook. The rise of AI can be attributed to advancements in machine learning, big data analyticsand cloud computing.

But there is a lot of misinformation about what exactly AImeans. Lets take a look at what it is all about and why you should care about it as an individual or as a business owner.

An AI writing assistant is software that automates one or more phases in the process of generating content for marketing purposes. AI writing assistants can help with ideation, structureand even tone and style, giving marketers more time to focus on their unique skills and to brainstorm new ideas.

Due to its wide range of capabilities, an AI writing assistant can potentially make marketers' jobs easier: It can generate content at scale while also making sure that every post has the right tone and structure according to client specifications. In addition, it can also take care of error-prone tasks such as copy editing

Many copywriters are now using AI writers to create content because software can identify the best structure and vocabulary to use, which can be a time-consuming process for copywriters. It also helps them with writers blockby giving them new ideas on what they should write about.

AI writers are also much cheaper than human copywriters and can generate content at scale. This is thanks to their ability to learn from large sets of data and performefficiently.

Related:3 Entrepreneurial Uses of Artificial Intelligence That Will Change Your Business

So how do you know which one AI assistant isright for your business? To start, you want to think about what kind of content you need. Do you need data-rich articles with keyword-rich titles that can rank well on search engines? If so, then an automated content creator that specializes in SEO is ideal.

Alternatively, if you're looking for something more creative and less technical, then an AI writing assistant that specializes in creative writing or emotive content may be perfect for your needs.

There are also AI assistants that specialize in designing infographics or editing video scripts.

Related:How to Design the Ideal AI Assistant

The emergence of AI has changed the way we do business. It does away with the need for human intervention in many cases, which means that there is no need for staffing and management of huge teams to handle customer service issues. For example, AI-assisted call centers can manage support tickets with accuracy and speed without requiring human labor all day long.

We will start seeing more jobs being replaced by AI as it becomes more advanced, so it's important now to look at how AI can be used for better productivity.

The way you use AI is going to depend on what your business is and what you plan to do with it. When you're starting a startup or a company, AI can be incredibly useful. It can provide you with insights into who your target audience is and how they're going to react to certain changes in your product or service offerings.

To work on a project, you need to have a good idea of what you want to do. In some cases, this may be as simple as just listing all of your ideas and then going from there. In other cases, it may be more difficult. That is where AI can come in the software will help you find the best idea for your project and help you with your workflow.

In reality, AI is already impacting businesses today. The main question is not what AI can do for a business, but rather how a business can leverage AI to improve customer experience and increase profitability. AI assistants are on the rise and they have been used for a variety of tasks from content generating to automatic translation. As more companies start using these technologies, they will change the way we work and live.

Related:The Future of Productivity: AI and Machine Learning

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What Is an AI Writing Assistant and How Can it Help Me as an Entrepreneur? - Entrepreneur

EDINBURGH, Scotland--(BUSINESS WIRE)--The current demand on network infrastructure is growing steeply. As new applications appear daily placing greater strains on our infrastructure, telecom operators need to have greater visibility into mobile traffic patterns to provide greater efficiency, reduce future CAPEX and increase profitability.

Net AIs mission is to put mobile network management on autopilot in the cloud. The companys first of its kind technology provides real-time AI-driven service demand estimation with low complexity and high-accuracy, while being non-intrusive.

The deployment of 5G and the applications that it enables will increase network complexity to the point that it becomes no longer viable for humans to operate and optimise the network manually. The infrastructure must be able to operate at machine speed and learn as it goes, which is why the O-RAN Alliance calls for embedding intelligence at all network levels. Net AIs technology is at the forefront of automation and fits perfectly into this framework. O-RAN creates opportunities to advance this technology within the expanding universe of participants, offering significant value to operators in combination with various flow management and resource allocation services.

Mobile operators can deploy Net AIs solutions at cell and/or core level, giving visibility into end-to-end resource utilisation. Having such knowledge of service-wise traffic consumption in real-time enables them to determine where best to place network functions/compute units.

Net AIs CEO and co-founder, Dr Paul Patras, led the Mobile Intelligence Lab and the Internet of Things research programme in the School of Informatics at Edinburgh University. An expert in Mobile Intelligence, a cross between mobile networking and AI, Pauls years of research in this field have led to the creation of Net AIs pioneering traffic analytics technology.

We are witnessing significant changes in the Telecom sector as 5G is being rolled out and operators appetite for cloud/edge computing and artificial intelligence is growing. Net AI is excited to be riding this wave and we are very keen to engage with potential early adopters of our technology said Dr Patras.

Net AI is at this weeks MWC event (Hall 6, stand 6C31.9) showcasing the capabilities of their innovative technology on a global stage, connecting with those who want to stay ahead of the curve, competitive in their markets, and provide higher quality services to their customers.

Please visit https://netai.tech/

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Net AI Showcases Their Mobile Traffic Analysis and Demand Estimation Tool at Mobile World Congress - Business Wire

Hodling really is the way when it comes to accumulating Bitcoin (BTC). At least, thats the conclusion made by an artificial intelligence (AI) trading bot coded up by a Portuguese software developer.

Bitcoiner Tiago Vasconcelos is the man behind the trading experiment. Vasconcelos built an AI trading bot that would help him accumulate more Bitcoin and test his coding skills. Almost inconceivably, instead of trading, the bot quickly concluded that the best way to trade Bitcoin is to buy and hold onto it.

Vasconcelos is the lead founder of Aceita Bitcoin, a Portuguese organization promoting the adoption, education and sharing of information about Bitcoin. A keen Bitcoiner, he also dabbles in Bitcoin-related side projects.

He told Cointelegraph, It was a reinforcement learning AI experiment, where I went and got a truckload of historical data from BTC/USDT. The code sourced and scraped the daily price action from 2014 to 2021.

Vasconcelos then trained it, or told him [the bot] the rules, here are the candles, you can either buy, sell, or do nothing.

For every profitable trade, the bot would be rewarded with one point. The bot loses one point as a punishment for lost trades. Finally, a reward is granted to the bot for the total amount of Bitcoin the bot finishes with:

The beauty of AI is that the bot begins to observe patterns and what Vasconcelos describes as moves that the bot makes to maximize its trading score.

There you have it, now its not just popular talking heads andeven banks in the Bitcoin space that are crying Hodl. Even the robots are hodlers.

Related: Bitcoin inactive supply nears record as over 60% of BTC stays unspent for at least 1 year

Hodl is a popular meme in the Bitcoin space, originating from a Bitcointalk forum post in 2013 by an inebriated contributor who misspelled hold. The reason why the original commenter, GameKyuubi decided to hodl is because Im a bad trader and I KNOW IM A BAD TRADER.

Turns out, he might have been just as smart as artificial intelligence all along.

Original post:

Hodl, dont trade, says the AI Bitcoin trading bot - Cointelegraph

Driving emerging AI driven solutions that provide measurable return on value across multiple ... [+] stakeholders and that are trustworthy, safe, human-centered for the benefit of people and humanity.

The insights and predictions in this article stem from my daily pro bono work across more than 100,00 CEOs, investors, experts, and scientists.

By 2030 AI will measurably influence and impact more than 8.5 billion people, across all sectors, and human & earth diverse ecosystems on an unprecedented scale.

In 2022, AI is the foundation for more than 70% global internet usage, explosion in edge devices / cloud computing / automation / autonomous growth, 40 plus billion IoT usage, new computer chip developments such as 3D / photonic / novel materials, biomedical innovation, digital transformation / reshaping, new education paradigms, quantum science, six plus hours of daily mobile usable in major markets across major devices amplified by 5G with 6G on the horizon, the rapid growth of the metaverse (trillions marketplace), businesses more than 60% adoption and scaling across the enterprise to more than 85% (2025), talent and reskilling programs, society and cultural changes. Thus, the recent news and events from UNESCO demands attention and inspires models of programmatic adoption of AI.

UNESCO ETHICS OF AI

UNESCO (United Nations Educational, Scientific and Cultural Organization) recommendations on the ethics of AI adopted by member states in November 2021 provides a foundational global agreement on AI Ethics. In turn this guides governments, research & development, business, industry, education, academia, non-profits, United Nations, media, investments, and startups. The objectives ultimately drive emerging AI driven solutions that provide measurable return on value across multiple stakeholders and that are trustworthy, safe, human-centered for the benefit of people and humanity.

INTERNATIONAL RESEARCH CENTER ON ARTIFICIAL INTELLIGENCE GLOBAL TOP 10 AI SOLUTIONS

IRCAI (International Research Center on Artificial Intelligence) under the auspices of UNESCO, launched an unprecedented program to identify the global top 100 AI solutions supporting the United Nations Sustainable Development Goals (SDGs). As noted on the UN Foundation site, Businesses embracing the SDGs await a $12 trillion economic opportunity. Combining SDGs with AI holds the potential to serve humankind and to bring benefits to individuals, countries, and businesses. Furthermore, AI can also help tackle economic, social, and environmental (ESG) challenges such as inequality, poverty, and climate change. AI is contributing solutions to the efforts of the global community to attain sustainable development goals.From a business standpoint, this is increasingly important as corporations report on their ESG (environmental, social, governance) and SDG-aligned activities. Examples in this area are the 4000 CEOs and leaders who are members of the Danish Management Society. For several years, they have undertaken measurable programs embracing digital reshaping and aligned to the SDGs.

TheIRCAI with global reach and scope, partially funded by the Republic of Slovenia,is uniquely focused on Artificial Intelligence. IRCAI functions as a Network of institutions and experts across the world, and a clearinghouse for relevant global AI projects. It provides an open and transparent dialogue, research on AI, discussions in the AI field, policy support to stakeholders around the world, and action plans and research roadmaps in the field of AI.IRCAI co-organizes events, in theWorld Series Events on AI.

The Global Top 100 were announced on the IRCAI website in December 17 2021. I estimate more than 6 million AI solutions across all domains, thus the top 100 is noteworthy. In addition, IRCAI created a program to spotlight the global top 10 AI solutions. These are AI solutions across governments, industry, non-profits, startups, all areas so again, makes the top 10 significant. These are the initial cohort of the Accelerator that IRCAI will be setting up in 2022.

This top 10 program rolled out in a series of posts from January 13, 2022 (profiling ASMSpotter) to February 15, 2022 (detailing NatureAlpha); each post dedicated to one of the top 10 and culminating in an international live event Feb 18, 2022, and now available on demand where leaders/founders from the global top 10 speak about their innovation solutions. The Permanent Mission of Slovenia to the UN and the International Research Centre on Artificial Intelligence organized the event on Feb 18.

The event details included keynote speakers, link to projects with the AI solution, industry category, and SDGs. The event is summarized below. I added the roles of the speakers from their LinkedIn profiles. I recommend examining the project links since it includes significant details of the AI solution. For example, country, SDG categories, industry categories, extensive project information details such as company or institution or organization / project name / description of AI solution, project excellence and scientific quality, scaling of impact to SDGs, scaling of AI solution, ethical aspects, website(s). You can hear their project summaries in each of their allocated 3 minutes speaking time at the Feb 28 event:

Bertie Vidgen (CEO co-founder),Rewire Socially Responsible AI for Online Safety (Internet),(SDG 5, 9, 16)

SDG 5: Gender Equality

SDG 9: Industry, Innovation and Infrastructure

SDG 16: Peace and Justice Strong Institutions

Angela Jorns (Senior Manager Good Governance and Responsible Mining),ASMSpotter (Mining),(SDG 12, 15)

SDG 12: Responsible Consumption and Production

SDG 15: Life on Land

Catherine Nakalembe (Associate Research Professor University of Maryland),NASA Harvest (Agriculture) ,(SDG 1, 2, 10, 13, 15, 17)

SDG 1: No Poverty

SDG 2: Zero Hunger

SDG 10: Reduced Inequality

SDG 13: Climate Action

SDG 15: Life on Land

SDG 17: Partnerships to achieve the Goal

Jonas Gramse (Advisor AI for local Innovation giz),FAIR Forward Artificial Intelligence for All(Health, Clean Energy), (SDG 3, 5, 7, 9, 10, 13, 17)

SDG 3: Good Health and Well-being

SDG 5: Gender Equality

SDG 7: Affordable and Clean Energy

SDG 9: Industry, Innovation and Infrastructure

SDG 10: Reduced Inequality

SDG 13: Climate Action

SDG 17: Partnerships to achieve the Goal

Lyric Jain (CEO Founder),Logically Intelligence(Civil Servants/Public Officials), (SDG 16)

SDG 16: Peace and Justice Strong Institutions

Vian Sharif (Cofounder),NatureAlpha Biodiversity & nature metrics platform(Finance / Credit Companies), (SDG 6, 13, 14, 15, 17)

SDG 6: Clean Water and Sanitation

SDG 13: Climate Action

SDG 14: Life Below Water

SDG 15: Life on Land

SDG 17: Partnerships to achieve the Goal

Vincenzo Di Nicola (Head of Technological Innovation and Digital Transformation INPS),An AI-powered classification email system to help the Italian Public Administration to better serve citizens(Public Employees), (SDG 8, 9)

SDG 8: Decent Work and Economic Growth

SDG 9: Industry, Innovation, and Infrastructure

Ulrich Scharf (Managing Director and Founder),SkillLab(Labor), (SDG 1, 4, 8, 10)

SDG 1: No Poverty

SDG 4: Quality Education

SDG 8: Decent Work and Economic Growth

SDG 10: Reduced Inequality

Mohammed Ashour (Co-Founder and CEO Aspire Food Group),Novel Application of Advanced Manufacturing Approaches to High Quality Protein (Food Products Manufacturing),(SDG 2, 3, 9, 12, 13, 17)

SDG 2: Zero Hunger

SDG 3: Good Health and Well-being

SDG 9: Industry, Innovation and Infrastructure

SDG 12: Responsible Consumption and Production

SDG 13: Climate Action

SDG 17: Partnerships to achieve the Goal

* Speaker unavailable for the event, MedCheX: An e-Alert system for automatically detecting pneumonia from chest X-rays (Health), (SDG 3, 10)

SDG 3: Good Health and Well-being

SDG 10: Reduced Inequality

SUMMARY DETAILS OF THE OUTSTANDING TOP 10 AI SOLUTIONS

Quoting extensively from the IRCAI:

-Rewire (UK) [company/institution], Rewire Socially Responsible AI for Online Safety [project]Rewire is developing AI tools for keeping people safe by automatically detecting whether online content contains hate. It can be used by platforms to moderate content and by other stakeholders (such as government, civil society and research agencies) to gain critical intelligence and monitor activity. Rewires AI is scalable, fast and can be used anywhere. Users of Rewire feed their text to the software and it automatically gives back scores showing whether the content is hateful. They have developed AI for English language and are now expanding it to other languages, including French, German, Spanish and Italian. Rewire leverages a unique human-and-model-in-the-loop approach to training AI, and offers unmatched performance, robustness and fairness. Many of the core innovations have been published in top-tier computer science academic conferences.

-INPS (the Social Security Administration of the Republic of Italy) and Accenture [company/institution], AI-powered classification email system to help the Italian Public Administration to better serve citizens [project]The project, developed by INPS (the Social Security Administration of the Republic of Italy) with Accenture, improves the current manual process of classifying emails sent by citizens and dispatching them to the appropriate office. It achieves its goal through innovative AI techniques and automatizing analysis of content and context of massive amounts of emails. Italian citizens send INPS more than 4 million emails each year (likely to increase with the pandemic): this automated email classification system is extremely valuable to INPS offices to respond more promptly to the citizens.

-dida Datenschmiede GmbH (Germany) [company/institution], ASMSpotter [project]ASMSpotter is a machine learning and AI software to automatically detect and monitor artisanal and small-scale mining (ASM) on satellite imagery using novel computer vision technique. ASM is the source of livelihoods for more than 44 million people across 80 countries worldwide, and thus has huge potential to contribute to the achievement of the SGDs. At the same time, ASM can have immense negative impacts on human rights, development and the environment if not governed properly. Effective, continuous monitoring of ASM activity is a crucial component in addressing these challenges. ASMSpotter provides an efficient and effective AI solution by combining cutting edge machine learning with specialist expertise on ASM through the partnership between Dida and Levin Sources.

-GIZ GmbH (Germany) [company/institution], FAIR Forward [project] Artificial Intelligence for AllThe German Development Cooperation initiative FAIR Forward Artificial Intelligence for All strives for a more open, inclusive, and sustainable approach to AI on an international level. To achieve this, they are working together with six partner countries: Ghana, Rwanda, Kenia, South Africa, Uganda and India. Together, they pursue three main goals: 1) Strengthen local technical know-how on AI, 2) Remove entry barriers to AI by helping to build open AI training data sets as digital public goods, and 3) develop policy frameworks ready for AI.

-Logically (UK) [company/institution], Logically Intelligence [project]Founded in 2017 by MIT and Cambridge alum Lyric Jain, Logically combines advanced AI with one of the worlds largest dedicated fact-checking teams to help government bodies uncover and address harmful misinformation and deliberate disinformation. The companys mission is to enhance civic discourse, protect democratic debate and process, and provide access to trustworthy information.

-National Cheng Kung University (Taiwan) [company/institution], MedCheX: an e-Alert system for automatically detecting pneumonia from chest X-rays [project]AI pneumonia detection platform for suspected COVID-19 patients. The purpose, only takes a single second to assist front-line doctors in recognizing patients who are infected.

-NASA Harvest, University of Maryland (USA) [company/institution], NASA Harvest [project]Harvest is developing solutions that provide information on agricultural production and land use that support the attainment of several SDGs as well as monitoring their achievement via the Global Indicator Framework. Since November 2017, NASA Harvest has initiated or been involved in ~30 projects globally to improve tools and grow regional and local capacity to address food insecurity. Harvest maintains a satellite-based Global Agriculture Monitoring system (GLAM) developed by the University of Maryland with NASA and USDA. GLAM was customized for East Africa, enabling the implementation of the World Banks Disaster Risk Financing and Insurance Program. In Uganda, this program has supported >300,000 individuals in Karamoja, providing alternative livelihoods to smallholder farmers affected by drought. This system also enables the delivery of newer maps and solutions using ML including crop maps and yield forecasts. Harvests 2019 crop map of Togo was used to implement the YOLIM program which has served more than 50,000 people.

-NatureAlpha (UK) [company/institution], NatureAlpha Biodiversity & nature metrics platform [project] NatureAlpha provides science-based analytics for investors on nature and biodiversity, powered by an R&D partnership with Oxford University, leading geospatial and machine learning technology. NatureAlphas ability to influence and redirect capital towards biodiversity preservation could have far-reaching effects for billions of global citizens.

-Aspire Food Group & DarwinAI (Canada) [company/institution], Novel Application of Advanced Manufacturing Approaches to High Quality Protein [project]Aspire Food Group is a world leader in the commercial production and processing of crickets and cricket waste (frass) into nutritional ingredients for people, pets and plants. Aspire has developed a proprietary, innovative process to produce exceptional protein using minimal resources, a modular and scalable production, tight supply chains and low costs to generate outsized global impact. DarwinAI was founded by a world class academics team from the University of Waterloo. Their patented explainability technology enables human-in-the-loop decision making in AI systems. DarwinAI-Aspires deep learning solution analyzes more than 50 input parameters to unearth insights that can improve more than 15 output parameters, creating a loop that changes facility conditions to produce healthy crickets and maximize facility yields while minimizing costs (e.g., due to water, natural gas, electricity, etc.).

-SkillLab B.V. (Netherlands) [company/institution], SkillLab [project]SkillLabs AI-based solution empowers people to capture their skills, find education and jobs as well as generate tailored job applications. SkillLab makes career guidance accessible to marginalized people and provides a pathway to employment based on a skill-recognition system that is granular, technology-enabled, and data-driven. Users create a skill profile through an AI-based interview that builds on the European Skills, Competences, Qualifications and Occupations framework (ESCO) which contains 13,485 skills and describes 2,942 occupations.

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UNESCO International Research Center Spotlights In 2022 Global Top 10 Outstanding AI Solutions - Forbes

AI is already transforming supply chain operations. Retailers, for instance, are becoming more informed in terms of what stock is available and can even forecast with far more precision meaning less waste and increased customer satisfaction. This means that organisations can better handle disruption across their supply chain at any given time.

Retailers that utilised Blue Yonder during the Suez Canal blockade, for example, had enhanced visibility of their stock and could foresee exactly how it would impact them and then adapt to continue business-as-usual and minimise disruption.

With AI, organisations can learn from previous experiences using their historical data to manage situations better in the future. This means that companies will have a better grasp of potential uncertainties, leading to higher efficiencies and increased resilience. It also empowers retailers to predict future demand and make mass decisions based on what the data tells them.

Advancements in AI technology are also supporting organisations in becoming more sustainable. AI is helping to reduce waste and reduce carbon footprints by introducing greater efficiencies. Organisations can know exactly what should be distributed and where reducing transportation and overall cost on a global scale.

Retailers see the benefits every day. Through AI,Morrisonshas increased both employee and customer satisfaction. AI optimisation has increased sustainability (due to less waste), improved on-shelf availability by 30%, and led to better financial KPIs. When stores have the right amount of availability on shelves, it results in less repetitive work, freeing up employees to focus on customer service. Here, technology has allowed humans to have more time and make better decisions to do their jobs effectively, which is where companies will see actual value from AI.

AI is helping to optimise processes and inform decisions, and organisations that dont choose to adopt this technology will not survive for long. This is especially a concern given the constant shift in consumer demands. Consumers expect what they want when they want and at their convenience AI can help businesses achieve this.

Some find it difficult to see how AI and algorithms can benefit a business. For years, many have relied on gut feelings and feel that AI will replace humans. While these obstacles are not easy to overcome, organisations must realise the business benefits.

One-way engineers are looking to develop AI for the future is by working on making it more individualised. The development of fairer algorithms will help to avoid human biases and discrimination. A part of this is that AI needs to develop a causal structure and answer questions such as what happens if? This will support humans to understand why AI is recommending the decisions and improve the scope of AI for broader use across a business.

The future for the industry is really exciting. The COVID-19 pandemic saw retailers and supply chain operators wake up to the importance of technology and how it can provide business benefits. At Blue Yonder, its our job to build on this momentum and educate retailers on how they can continue to improve business operations.

Our focus will be increasing the end-to-end view and optimising the whole supply chain for our customers, which will be crucial moving forward. To move forward, the industry needs to implement a more horizontal integration of operations, from sourcing and warehousing to transportation and distribution.

Our partnership with Panasonic will also introduce more IoT technologies and increase intelligent hardware devices (such as cameras or intelligent shelves for cashier-less shopping). With such hardware, retailers will have a more precise view of what is in stock when, and with this more reliable input data, the AI systems can make better decisions and generate even more value.

The future holds more integrated, automated, and autonomous supply chains to make intelligent decisions. Blue Yonder is ready and prepared to support customers looking to make this transition.

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Panasonic buy out 'upped AI & ML offerings' - Blue Yonder - Supply Chain Digital - The Procurement & Supply Chain Platform

RICHMOND, Calif.--(BUSINESS WIRE)--BioAge Labs, Inc. (link), a clinical-stage biotechnology company and global leader in using artificial intelligence (AI) and machine learning (ML) to develop drugs that target the molecular causes of aging, today announced that the Company will participate in a panel discussion at the Truist Securities Life Sciences AI Symposium, the first of three investment banking corporate access events to which BioAge will contribute in the coming two months.

BioAge CEO and co-founder Kristen Fortney, PhD, will speak on the panel Integrated platforms for AI-based drug discovery on March 1, 2022 from 11:20 AM to 12:20 PM EST. Additional panelists include Carl Hansen, CEO of AbCellera Biologics; Don Bergstrom, President of R&D at Relay Therapeutics; Abraham Heifets, CEO and Jonathan Barr, CFO of Atomwise; and Krishnan Nandabalan, CEO of InveniAI. Robyn Karnauskas, Senior Research Analyst and Kripa Devarakonda, Research Analyst, Truist Securities will moderate. The event will be held virtually.

A live audio webcast may be accessed through the conference website: https://truist-securities-2022-ai-symposium-biotech-tools.videoshowcase.net/login. (In the login form, enter BioAge Labs in the Sales Contact field.)

BioAge uses its discovery platform, which combines AI and ML analysis of proprietary longitudinal human samples with detailed health records tracking individuals over the lifespan, to map out the key molecular pathways that impact healthy human aging, thus revealing the causes of age-related disease. By targeting the mechanisms of aging with a mechanistically diverse portfolio of drugs, BioAge is unlocking opportunities to treat or even prevent these diseases in entirely new ways. BioAge currently has three clinical-stage programs in its growing portfolio.

From March 29 to March 31, 2022, BioAge will participate in SVB Leerink Biopharma Private Company Connect, an event intended to bring together private companies with institutional investors and to facilitate discussions on the trends and opportunities shaping the future of healthcare.

From April 11 to April 14, 2022, BioAge will participate in the 21st Annual Needham Virtual Healthcare Conference, which features presentations from leading public and private companies in the biotechnology, specialty pharmaceuticals, medical technology, and diagnostics sectors.

At all three events, both CEO Kristen Fortney, PhD, and CFO Dov Goldstein, MD, MBA will be available to meet with qualified institutional, private equity, and venture capital investors. Investors seeking to participate in these events should contact media@bioagelabs.com.

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BioAge Labs to Participate in Truist Symposium on AI-driven Drug Discovery, Multiple Upcoming Biotech Investment Conferences - Business Wire

People doing computer science degrees, of any sort, need ethical training, says one of the designers.

When Katrina Ingram talks to non-experts about artificial intelligence, she often has to clear up one big misconceptioncall it the Teminator fallacy.

Hollywoods done a fantastic job of creating this idea that AI is almost human-like and sentient, she says. And, not to mention, often with an evil agenda.

The so-called narrow AI at work in the world today is, frankly, more boring than Hollywoods killer robots. It takes the form of algorithms that decide which of your email messages are spam or sift through job applicants to choose promising candidates. These systems typically use huge datasets to learn how to make choices, and over time improve their performance by incorporating more data. But as these systems become more sophisticatedand as more businesses, governments and other organizations rely on AI for more and more tasksthe ethical quandaries surrounding the technology are piling up. A lot of AI systems are being used to make predictions about people, and that can cause harm in all kinds of ways, says Ms. Ingram.

As founder and CEO of Edmonton-based company Ethically Aligned AI, Ms. Ingram spends much of her time consulting with organizations on how to foreground ethical considerations when designing and deploying artificial intelligence. Thats why shes helped design Canadas first university microcredential focused on AI ethics a four-course certificate program offered by Athabasca University.

According to Ms. Ingram, this kind of training should be foundational for any professional working in digital systems. Yet the Athabasca program is the first of its kind in Canada, and one of relatively few worldwide.

People doing computer science degrees, of any sort, need ethical training, she said. Thats one audience that needs to be served the other big audience is people who are already working professionally, all the people working in companies, designing these systems, they need training too, and the microcredential program is flexible, not too time-consuming and works for them.

The courses were co-designed with Trystan Goetze, a philosopher and ethicist currently completing his postdoctoral fellowship in computer ethics at Harvard University.

The technology has gotten ahead of the policy thinking, and its gotten ahead of the humanistic thinking on the subject, says Dr. Goetze. Computers are not like other kinds of technology, where ethical issues that come up are very narrow in scope. We dont talk about automobile ethics, for example. But we do talk about computer or AI ethics, because this technology can be applied in almost any aspect of society, business, you name it.

What kinds of ethical issues are at stake? Bias is a major one, said Dr. Goetze. AI can reaffirm and even exacerbate existing prejudices. Consider an AI system tasked with choosing promising job applicants, which bases its decisions on a dataset of previous hires. If historic hiring practices were flawed or biased against particular candidates, those biases will be integrated into the AI as well.

Another consideration is how data used by AI systems is collected i.e., is it scraped from social media profiles or other online sources in a consensual and privacy-compliant way? And then theres robo-ethics, including concerns around misuse of facial recognition technology, or safety issues caused by the testing of AI-powered autonomous vehicles on public streets.

Besides academic thinkers, the courses will include interviews and contributions from activists, professionals and business leaders, who can bring different facets of the subject to light.

[These issues are] nothing new for technologists, says Dr. Goetze. But for a business leader this could be completely new information.

Beyond the content of the courses, Ms. Ingram and Dr. Goetze are both pleased with another aspect of them: they look good.

Athabasca has put a great deal of creative effort into these courses, Dr. Goetze said. Theyre visually designed in a beautiful way, with video and animations. Its not something that looks like it was slapped together in haste during the last lockdown, which I think is a testament to the fact that when we have the time and resources, we can produce online education experiences that are truly special.

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New microcredential focuses on the importance of AI ethics University Affairs - University Affairs