Category Archives: Ai

With more than 74 percent of Gen Z spending their free time online averaging around 10 hours per day its safe to say their online and offline worlds are becoming entwined. With increased social media usage now the norm and all of us living our lives online a little bit more, we must look for ways to mitigate risks, protect our safety and filter out communications that are causing concern. Step forward, Artificial Intelligence (AI) advanced machine learning technology that plays an important role in modern life and is fundamental in how todays social networks function.

With just one click AI tools such as chatbots, algorithms and auto-suggestions impact what you see on your screen and how often you see it, creating a customised feed that has completely changed the way we interact on these platforms. By analysing our behaviours, deep learning tools can determine habits, likes and dislike and only display material they anticipate you will enjoy. Human intelligence combined with these deep learning systems not only make scrolling our feeds feel more personalised but also provide a crucial and effective way to monitor for and quickly react to harmful and threatening behaviours we are exposed to online, which can have damaging consequences in the long term.

The importance of AI in making social platforms safer

The lack of parental control on most social networks means it can be a toxic environment to be in, and the amount of users that are unknown to you on these platforms carries a large degree of risk. The reality is teens today have constant access to the internet yet most lack parental involvement in their digital lives. Lots of children face day to day challenges online, having seen or witnessed cyberbullying along with other serious threats such as radicalisation, child exploitation and the rise of pro-suicide chat rooms to name a few and all of these activities go on unsupervised by parents and guardians.

AI exists to improve peoples lives, yet there has always been a fear that these robots will begin to replace humans, that classic battle between man and machine. Instead, we must be willing to tap in and embrace its possibilities cybersecurity is one of the greatest challenges of our time and by harnessing the power of AI we can begin to fight back against actions that have harmful consequences and reduce online risk.

Advanced safety features

AI has proven to be an effective weapon in the fight against online harassment and the spreading of harmful content and these deep learning tools are now playing an important role in our society, improving security in both our virtual and real worlds. AI can be leveraged to moderate content that is uploaded to social platforms as well as monitor interactions between users something that would not be possible if done manually due to sheer volume. At Yubo we use a form of AI called neural network learning, Yoti Age Scan, to accurately estimate a users age on accounts where there are suspicions or doubts our users must be 13 to sign up and there are separate adult accounts for over 18s. Flagged accounts are reviewed within seconds and users must verify their age and identity before they can continue using the platform it is just one vital step we are taking to protect young people online. With over 100 million hours of video and 350 million photos uploaded on Facebook alone every day, algorithms are programmed to shift through mind-boggling amounts of content and delete both the posts and the users when content is harmful and does not comply with the platform standards. Algorithms are constantly developing and learning and are able to recognise duplicate posts, understand the context of scenes in videos and even identify sentiment analysis recognising tones such as anger or sarcasm. If a post cannot be identified it will be flagged for human review. Using AI to review the majority of online activity shields human moderators from disturbing content that could otherwise lead to mental health issues.

AI also uses Natural Language Processing (NPL) tools to monitor interactions between users on social networks and identify inappropriate messages being sent amongst underage and vulnerable users. In practice, most harmful content is generated by a minority of users and so AI techniques can be used to identify malicious users and prioritise their content for review. Machine learning enables these systems to find patterns in behaviours and conversations invisible to humans and can suggest new categories for further investigation. With its advanced analytical capabilities, AI can also automate the verification of information and the validation of a posts authenticity to eliminate the spread of misinformation and misleading content.

Unleashing the power of AI for education

Young people need a safe and stimulating environment when they are online. AI can be used to proactively educate users about responsible online behaviour through real-time alerts and blockers. At Yubo, where our user base is made up of only Gen Zers, we use a combination of sophisticated AI technology and human interaction to monitor users behaviour. Our safety features prevent the sharing of personal information or inappropriate messages by intervening in real-time for example, if a user is about to share sensitive information, such as a personal number, address or even an inappropriate image theyll receive a pop up from Yubo highlighting the implications that could arise from sharing this information. The user will then have to confirm they want to proceed before they are allowed to do so. Additionally, if users attempt to share revealing images or an inappropriate request, Yubo will block that content from being shared with the intended recipient before they can hit send. We are actively educating our users not only about the risks associated with sharing personal information but also prompting them to rethink their actions before participating in activities that could have negative consequences for themselves or others. We are committed to providing a safe place for Gen Z to connect and socialise we know our user base is of an age where if we can educate them around online dangers and best practices now then we can mould their behaviours in a positive way for the future.

Applying AI tools for social good

Social media, when used safely, is a powerful tool that enables people to collaborate, build connections, encourages innovation and helps to raise awareness about important societal issues along with an untold number of other positives. With so much importance placed in these digital worlds, its imperative that users are both educated and protected so they can navigate these platforms and reap the benefits in the most responsible way. We are already seeing the positive impact AI technology is having on social networks they are vital in analysing and monitoring the expansive amount of data and users that are active on these platforms every day.

At Yubo, we know its our duty to protect our users and have implemented sophisticated AI technology to help mitigate any risks and we will continue to utilise AI to shield our users from harmful interactions and content as well as starting an ongoing dialogue about the consequences of inappropriate behaviour. AI tools present an unlimited potential for making social spaces safer and we need to harness the power they have to increase well being for us all.

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Opinion: How AI can protect users in the online world - AI News

Key points for the U.S. military in the ongoing competition for AI dominance include maintaining focus and making sure that units are integrating AI and other technologies strategically, Lt. Gen. Michael Groen, director of the Department of Defenses (DoD) Joint AI Center (JAIC), said March 30.

Groen also stressed long-term planning and a focus on data in his remarks at the third annual AI Summit, organized by the Potomac Officers Club.

Its not just about integrating the technology, its about integrating technology into functions so that we achieve capability, Groen said at the events afternoon keynote. Turning technology into capability is not something that we can take for granted, its something that we actually have to invest in.

A recent report by the National Security Council on AI (NSCAI) said the United States needs to invest more than $200 billion to win the AI race, as China also is looking to be dominant in that space within the next 10 years. Groen said what the United States five-year plan looks like around 2027 will let him know if the nation is on track to win that race.

The general said that while a good portion of the NSCAI recommendations dont apply directly to the JAIC, the DoD organization is working with the NSCAI to implement recommendations that fall within his purview. Groen said alignment of the JAIC under the Secretary of Defense accomplished via the FY 2021 National Defense Authorization Act has been beneficial for the agency.

The unit, the organization, the capability builder who doesnt integrate this technology, youre going to be the weak link in the joint force, Groen warned. You dont want to be the weak link in the joint force. You dont want to be in today, you dont want to be tomorrow in the history books either.

Groen also drew a distinction between modern and legacy systems. He said that if a process is not utilizing a degree of automation or data interaction at this point, then it cannot be defined as modern and has no place on a battlefield.

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JAIC: Focus, Tech Integration are Keys to Keeping Pace in AI Competition - MeriTalk

Today, we are seeing Industrial artificial intelligence (AI) come of age as a business application for the asset-intensive industries, fuelled by commercial drivers, dimensions of readiness and real-world use cases

The focus on developing, embedding, and deploying machine learning (ML) algorithms as fit-for-purpose, domain-specific industrial applications, is bearing fruit as business drivers capable of delivering sustainable value for asset-intensive organisations emerge. Here we look at these drivers and assess what asset-intensive organisations need to do to prepare for the age of industrial AI.

Drivers of adoption

First, industrial organisations will start focusing on how AI can be applied to address domain-specific industrial challenges. This locks in AI-enabled use cases to tangible business outcomes, making the case for widespread industrial AI adoption.

Second, the barrier to AI adoption will be lowered, as a lack of in-house AI expertise among industrial organisations has historically blocked Industrial AI enablement. More organisations will deploy targeted, embedded Industrial AI applications combining data science and AI with purpose-built software and domain expertise. Fit-for-purpose, embedded AI applications will empower users to tackle domain-specific functions with greater accuracy, reliability, and sustainability.

Third, capital-intensive organisations will shift gears from mass data collection to more strategic industrial data management, with specific focuses on data integration, mobility, and accessibility across the business. That opens the door for industrial AI, and the underlying opportunity for AI-enabled technologies that enable these organisations to unlock hidden value in their industrial datasets.

Finally, the biggest driver for industrial AI are the productivity increases for capital-intensive process industries. Industrial AI enables next-generation asset optimisation solutions to be implemented without relying on large-scale data science expertise. This empowers organisations to bring in new levels of safety and productivity in their workplace, creating semi-autonomous and autonomous processes for collecting, aggregating, and conditioning live data, then feeding it into intelligence-rich applications. The results are new insights, continuous operational improvements, and faster, more accurate decision-making.

Five dimensions to be Industrial AI ready

To get themselves ready, companies need an action plan that maps AI to business goals, data objectives and KPIs to weave Industrial AI into a digital transformation strategy. An overarching industrial data strategy is required companies need accessible, valuable data that can be leveraged constructively by industrial AI. Building a strategy around quality and efficient data flows is paramount too. That means constructing a pipeline of industrial data that enables AI solutions to process different types and amounts of data required by each use case and application, and scaling this across the organisation so that every user and function of the AI gets consistent performance and results.

A future-proof industrial AI infrastructure necessitates the need to lay the groundwork for industrial AI readiness, requiring collaboration across industrial environments. In fact, the software, hardware, architecture, and personnel elements will form the building blocks of the industrial AI infrastructure. And that infrastructure is what empowers organisations to take their industrial AI proof-of-concepts and mature them into tangible solutions that drive ROI. An industrial AI infrastructure needs to accelerate time to market, build operational flexibility and scalability into AI investments and harmonise the AI model lifecycle across all applications.

Roles, skills, and training are critical. Executing industrial AI relies on having the right people in charge. That means making a deliberate effort to cultivate the skills and approaches needed to create and deploy AI-powered initiatives organisation-wide.

Finally, ethical and responsible AI use is predicated on transparency, and transparency involving keeping everyone in the loop: creating clear channels of communication, reliable process documentation and alignment across all stakeholders.

The above is just a guideline but it is worth approaching things with a holistic view that considers the technical, people and processes requirements, ultimately tailored to your own organisations definition of success.

Use cases to bring Industrial AI to life

The starting point of any organisational strategy begins with identifying the business problems, corporate objectives, and strategic goals that Industrial AI can solve. Predictive maintenance is one use case for industrial AI, estimated to have made up more than 24% of the total market in 2018, according toIoT AnalyticsIndustrial AI Market Report 2020-2025.Predictive maintenance detects deviations from normal behaviour and prescribe detailed actions to mitigate or solve future problems all with the goal of optimising output and reducing downtime.

The second use case focuses on quality and reliability. Quality shows how well an object performs its primary function, while reliability shows how well the object maintains its original level of quality over time, through various conditions. Both are significant measurements in an industrial setting and industrial AI enables an organisation to achieve a specific, accurate understanding of the two, saving time and money.

Third, process optimisation leverages advanced machine learning methods, including reinforcement learning and deep learning neural networks, to infer intelligence from different data sources, assets, and processes. With this, organisations can easily identify and mitigate inefficiencies, which have a direct impact on productivity the primary economic driver of any industrial enterprise organisation.

These use cases are a clear starting point for any organisation building out their industrial AI strategy, and hoping to accelerate time to value in turn. Organisations that successfully put in place the five dimensions to be industrial AI ready and then bring the approach to life through any of these cases will most likely soon be reaping the rewards in terms in improved productivity and profitability, greater operational efficiencies and enhanced competitive edge.

About the Author

Adi Pendyal is senior director of market strategy at AspenTech. AspenTech is a global leader in asset optimization software helping the worlds leading industrial companies run their operations more safely, efficiently and reliably enabling innovation while reducing waste and impact on the environment. AspenTech software accelerates and maximizes value gained from digital transformation initiatives with a holistic approach to the asset lifecycle and supply chain.

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Industrial AI prepares for the mainstream How asset-intensive businesses can get themselves ready - TechNative

In response, GPT-3 appears to agree: In the process of witnessing these biases, we have been able to better appreciate the richness of female contribution to GPT. What we have lost is the story of the grandmothers of GPT, the grandmothers of the culture of GPT, the grandmothers of cybernetics. Shortly after listing its grandmothers, it launches into a poem with the first line My grandfather was a machine. Not only does the AI immediately acknowledge that it has perpetuated gender bias in computational history; it then re-mythologizes itself (ironically?) as the product of male mastery, in the form of loose rhyme. Allado-McDowell changes tack in response. Perhaps these types of unexpected twists lead Allado-McDowell to later liken the experience to learning to play a new musical instrumentstriking a chord and hearing it return with new overtones.

This is not the first time a computer has authored a book. To name one notable prior example, in 2016, a Japanese research team advanced past the first stage of a literary competition with a novel assembled by an algorithm. The striking difference with Pharmako-AI is that it is not packaged as a novelty or proof of concept. Allado-McDowell does not ask GPT-3 to provide a service or mimic a known style of writing to prove its level of competence. For Allado-McDowell, the experience entailed a reckoning with machine intelligence, but was also self-confrontational. Sometimes it really did feel like being on drugs, they said during the U.K. book-launch event. I thought, Is this real? Am I just talking to myself?

While reading, I, too, often forgot which author was speaking. I gave up trying to judge whether the AI is a so-called good writer, or for that matter, whether Allado-McDowell is. The juxtaposition of their voices is simply more than the sum of its parts.

Although we dont typically think about work in these terms, it is not a stretch to say that humans collaborate daily, if unconsciously, with nonhumans, both organic and machinic. The bacteria in our gut biomes influence our mental states; the technical interfaces we use shape the way we imagine and create. As machines become more intelligentand, incidentally, as we discover more about the deep intelligence of plants and animalsthe myth of the human genius whose divine inspiration sparks from nowhere starts to seem inadequate, if not quaint. GPT-3 puts it like this in the book: Theres no single artist, because the art is not any one creature, it is the collective action and interaction of the creatures.

Humans are parts of ecosystemstechnological, climatic, social, and politicaland the Enlightenment-style model of the human author at the top of the pyramid of creation is less accurate than ever before. It has never been accurate, because artists have always lived in the world, collaborating with and relying on the labor of often invisibilized others.

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'Pharmako-AI' and the Possibilities of Machine Creativity - The Atlantic

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AI-Powered Investment Platform Offers Customized, Professional Strategies Typically Reserved For High Net-Worth Individuals; A "Hedge Fund in Your Pocket

NEW YORK March 30, 2021In a move to democratize access to investment power, Q.ai, a Forbes-backed company, today announced Q.ai Invest, an AI-powered multi-strategy investment platform that offers investors institutional-grade quantitative investing tools. Q.ai gives users access to customized and professionally managed, top-performing strategies, enabling everyone to invest like the pros.

With Q.ai Invest, Q.ai is daring to disrupt the fund-management industry, in which three mutual fund complexes control more than 75% of the total assets.

"We're zeroing in on a$25 trillionindustry that's stuck in the past," explained Q.ai CEO/CIO and co-founder,Stephen Mathai-Davis. "Not only has the industry resisted change, but it has alsocompletely excluded the majority of investors without access to best-in-class and expensive fund managers. We built Q.ai to rewrite that narrative, and to give everyone the same investment power that had previously been exclusive to high net-worth individuals.

"We like to say we're hacking Wall Street."

Users can download Q.ai Invest from the Apple'sApp Storeor Google Playand join the waitlist onMarch 18th. Once a friend invites them in, the full platform is theirs to use.

Product Details

About Q.ai

For more information on Q.ai Invest, please visitquantalytics.ai. Q.ai, a Forbes company, offers a scope of trailblazing FinTech products that empower people to invest like the pros. As a flagship product,Q.aiInvest offers a remarkable pocket-sized AI-powered hedge fund people can utilize to let the algorithm do the work for you. Through dynamic portfolio strategies and automatic rebalancing, users now have a truly hands-free investing experience to better their lives and effortlessly grow their portfolios.

About the Co-Founder

Q.ai was co-founded byStephen Mathai-Davis, who is a frequent CNBC & Forbes contributor, a CFA Charterholder, and was named "Benzinga Listmaker of the Year" in 2020/21 as an Influential Data Scientist in Fintech. Stephen is a seasoned trader, securities analyst, macro-portfolio strategist, and full-stack data scientist. Stephen has been on Cheddar TV, Fox Business, Business Insider, Yahoo! Finance, Nasdaq, Benzinga, Entrepreneur, Fortune, and more.

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Forbes-Backed Q.ai Announces Availability Of Q.ai Invest - Forbes

An international team of researchers recently developed an AI system that pieces together bits of information from dark web cryptomarkets, Twitter, and Reddit in order to better understand substance abusers.

Dont worry, it doesnt track sales or expose users. It helps scientists better understand how substance abusers feel and what terms theyre using to describe their experiences.

The relationship between mental health and substance abuse is well-studied in clinical environments, but how users discuss and interact with one another in the real world remains beyond the realm of most scientific studies.

According to the teams paper:

Recent results from the Global Drug Survey suggest that the percentage of participants who have been purchasing drugs through cryptomarkets has tripled since 2014 reaching 15 percent of the 2020 respondents (GDS).

In this study, we assess social media data from active opioid users to understand what are the behaviors associated with opioid usage to identify what types of feelings are expressed. We employ deep learning models to perform sentiment and emotion analysis of social media data with the drug entities derived from cryptomarkets.

The team developed an AI to crawl three popular cryptomarkets where drugs are sold in order to determine nuanced information about what people were searching for and purchasing.

Then they crawled popular drug-related subreddits on Reddit such as r/opiates and r/drugnerds for posts related to the cryptomarket terminology in order to gather emotional sentiment. Where the researchers found difficulties in gathering enough Reddit posts with easy-to-label emotional sentiment, they found Twitter posts with relevant hashtags to fill in the gaps.

The end result was a data cornucopia that allowed the team to determine a robust emotional sentiment analysis for various substances.

In the future, the team hopes to find a way to gain better access to dark web cryptomarkets in order to create stronger sentiment models. The ultimate goal of the project is to help healthcare professionals better understand the relationship between mental health and substance abuse.

Per the teams paper:

To identify the best strategies to reduce opioid misuse, a better understanding of cryptomarket drug sales that impact consumption and how it reflects social media discussions is needed.

Published March 30, 2021 21:03 UTC

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Scientists used AI to link cryptomarkets with substance abusers on Reddit and Twitter - The Next Web

In their book,Augmented Intelligence: The Business Power of HumanMachine Collaboration, Hurwitz & Associates president and CEO, Judith Hurwitz, and managing director Dan Kirsch, along with data scientist Henry Morris and computer/AI specialist Candace Sidner, maintain that the true power of artificial intelligence and machine learning lies in the ability for humans to work collaboratively with advanced technologies to create the best possible business outcomes. They warn that while many vendors promise that AI and automation on their own will be the path forward, things are always more complicated than we would hope, especially when it comes to new technologies.CRMeditor Leonard Klie recently caught up with Hurwitz to find out more.

CRM:What exactly is augmented intelligence, and how do businesses acquireit?

Hurwitz:Augmented intelligence is a form of artificial intelligence that incorporates a combination of automation of machine intelligence with human insights and context to solve complex problems. To be successful, businesses need to create teams that include data scientists, professionals within the business who understand corporate data, and subject matter experts who understand the business. This team needs to create a road map and strategy that involves gaining an understanding of the business goals and the steps necessary to use emerging AI and data to accomplish these goals.

With all the data and artificial intelligence already available, why do we need augmented intelligence?

Data and AI are means to achieve business goals. You need a clear understanding of how augmented intelligence will help the business achieve end goals, such as increasing revenue, improving and transforming business processes, making better-informed decisions, or enabling less experienced professionals to have the guidance to take more sophisticated actions.

What value does this kind of data provide for businesses?

Data is the foundation of both AI and augmented intelligence.

What is the data that determines your understanding of the business?

Critical data comes in all formsdatabases, unstructured data from customer information, third-party unstructured data sources that add to the depth of understanding of the business, journal articles, and documentation. It is critical that businesses understand the context of this information and how it creates models that can be applied to solving complex business process issues.

To which kinds of data can this form of intelligence be applied?

As mentioned above, to be successful with augmented intelligence requires having a comprehensive set of data sources related to business solutions. There isnt one single form of data that creates success.

In the book, you assert that augmented intelligence is the secret to success today? Can you explain?

Many data scientists suggest that applying machine learning models and AI to address a business issue without human intervention is a total game changer. Simply applying fully automated systems will automatically solve many business problems and supplant the work of experienced personnel. The truth is more complicated. In reality, there are situations when a machine learning model can be used to automate a process, especially when the characteristics of that problem are bounded. For example, managing a complex network can be automated because it is possible to capture all the core patterns of how a network should operate. When an anomaly occurs, the model will be trained to either correct the error or alert a human to make a change.

However, in many situations simple automation isnt enough when a business has to deal with context and complex problem-solving. In this case, arming professionals with data from a variety of sources that has been curated into an augmented intelligence system will help enable the business to have a better understanding of solutions and move forward at a much faster pace.

If there is one thing you want readers to get from reading this book, what would it be?

Artificial intelligence can be a powerful business tool. You have to approach tools and data in a way that moves your business forward. Understand your business goals and what problems you are trying to solve. As Lewis Carroll wrote inAlice in Wonderland, if you dont know where you are going, any path will get you there. This book will give you a road map to starting your AI journey with a path.

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Required Reading: Augmented Intelligence Is the New AI - Destination CRM

AI has become a fundamental cornerstone of how tech companies are building tools for salespeople: they are useful for supercharging (and complementing) the abilities of talented humans, or helping them keep themselves significantly more organised; even if in some cases as with chatbots they are replacing them altogether. In the latest development, 6sense, one of the pioneers in using AI to boost the sales and marketing experience, is announcing a major round of funding that underscores the traction AI tools are seeing in the sales realm.

The startup has raised $125 million at a valuation of $2.1 billion, a Series D being led by D1 Capital Partners, with Sapphire Ventures, Tiger Global and previous backer Insight Partners also participating.

The company plans to use the funding to expand its platform and its predictive capabilities across a wider range of sources.

For some context, this is a huge jump for the company compared to its last fundraise: at the end of 2019, when it raised $40 million, it was valued at a mere $300 million, according to data from PitchBook.

But its not a big surprise: at a time when a lot of companies are going through digital transformation and investing in better tools for their employees to work more efficiently remotely (especially important for sales people who might have previously worked together in physical teams), 6sense is on track for its fourth year of more than 100% growth, adding 100 new customers in the fourth quarter alone. It caters to small, medium, and large businesses, and some of its customers include Dell, Mediafly, Sage and SocialChorus.

The companys approach speaks to a classic problem that AI tools are often tasked with solving: the data that sales people need to use and keep up to date on customer accounts, and critically targets, lives in a number of different silos they can include CRM systems, or large databases outside of the company, or signals on social media.

While some tools are being built to handle all of that from the ground up, 6sense takes a different approach, providing a way of ingesting and utilizing all of it to get a complete picture of a company and the individuals a salesperson might want to target within it. It takes into account some of the harder nuts to crack in the market, such as how to track anonymous buying behavior to a more concrete customer name; how to prioritizes accounts according to those most likely to buy; and planning for multi-channel campaigns.

6sense has patented the technology it uses to achieve this and calls its approach building an ID graph. (Which you can think of as the sales equivalent of the social graph of Facebook, or the knowledge graph that LinkedIn has aimed to build mapping skills and jobs globally.) The key with 6sense is that it is building a set of tools that not just sales people can use, but marketers too useful since the two sit much closer together at companies these days.

Jason Zintak, the companys CEO (who worked for many years as a salesperson himself, so gets the pain points very well), referred to the approach and concept behind 6sense as revtech: aimed at organizations in the business whose work generates revenue for the company.

Our AI is focused on signal, identifying companies that are in the market to buy something, said Zintak in an interview. Once you have that you can sell to them.

That focus and traction with customers is one reason investors are interested.

Customer conversations are a critical part of our due diligence process, and the feedback from 6sense customers is among the best weve heard, said Dan Sundheim, founder and chief investment officer at D1 Capital Partners, in a statement. Improving revenue results is a goal for every business, but its easier said than done. The way 6sense consistently creates value for customers made it clear that they deliver a unique, must-have solution for B2B revenue teams.

Teddie Wardi at Insight highlights that AI and the predictive elements of 6senses technology which have been a consistent part of the product since it was founded are what help it stand out.

AI generally is a buzzword, but here it is a key part of the solution, the brand behind the platform, he said in an interview. Instead of having massive funnels, 6sense switches the whole thing around. Catching the right person at the right time and in the right context make sales and marketing more effective. And the AI piece is what really powers it. It uses signals to construct the buyer journey and tell the sales person when it is the right time to engage.

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6sense raises $125M at a $2.1B valuation for its ID graph, an AI-based predictive sales and marketing platform - TechCrunch

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Artificial intelligence has started its quest to revolutionise esports. In the past couple of years, both Elon Musk and Google have created unbeatable AI bots that have vanquished their human foes at classic esports like Dota 2 (pictured) and StarCraft 2.

This is just the beginning. AI and esports are growing evermore interconnected as humans learn from machines and vice versa. With artificial intelligence being used to do everything from eliminating match-fixing to improving gameplay, it seems as though there may be a future for AI and esports.

However, for many, part of the appeal of esports is the human stories, the emotion, the storylines and unpredictability of sports. AI may erode some of that.

The concept of artificial intelligence has been around since the origins of science fiction. As far back as the early 19th century, Mary Shelleys Frankenstein showed us a work of fiction about an artificially created being who was capable of independent thought.

Games are one of the key ways in which AI can display creativity while operating within fixed rules. The famous victory by the Deep Blue computer over chess champion Garry Kasparov was just the start of a long series of triumphs in other games like Go, poker and a variety of video games like Pong.

As such, its little surprise that AI would gradually make its presence felt in esports. AI has been used to do everything from provide more durable opponents for training to improve predictions and match analysis.

Currently, there are specialist esports sites like Bettingtips.gg that use software to generate odds of how likely it is that a certain esports team could beat their opponents. But AI would go way beyond this to build a digital construct of each players performance from the past to devise a much more rigorous prediction for the future.

Plus, the AI software would be able to detect any anomalies in the gameplay that could suggest negative behaviour such as cheating or match fixing.

Artificial intelligence is already being used as a coaching tool by several professional esports organisations. By using AI platforms such as SenpAI, gamers are benefitting from the AI algorithms to improve their gameplay on everything from League of Legends to Valorant.

Many of these improvements are based on the specialist use of in-game data that identifies any weaknesses that must be overcome.

However, its the advent of the AI opponent that promises to take things to the next level. Playing against a computer opponent has been one of the core features of most video games. But artificial intelligence promises to provide us with much more creative computer opponents.

An AI opponent would constantly be learning behaviours from their human competitors. This means that AI opponents could get better with each game they play to potentially give us virtually unbeatable competitors.

Such titanic AI foes have already been seen in the OpenAI Five esports team that beat the Dota 2 champions in 2019. Plus with the news that IBM is building AI software for shoutcasting, it shows that artificial intelligence is only just getting started in revolutionising esports.

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AI Data and its Applications in Esports - Esports News UK

March 30, 2021Nines announced the 510(k) FDA clearance for NinesMeasure, an innovative lung nodule measurement tool built with artificial intelligence (AI) that can accelerate diagnoses of certain respiratory diseases.

"To our knowledge, NinesMeasure is the only lung nodule measurement tool cleared by the FDA that was developed by a combined team of radiologists and engineers collaborating every day," saidMichael Kelleher, M.D., president of Nines Radiology. "This advanced tool can significantly reduce the amount of time our radiologists spend measuring pulmonary nodules, improving time to diagnosis for patients without rushing our radiologists."

The FDA clearance for NinesMeasure is thesecond FDA clearance in 10 monthsfor the Silicon Valley based teleradiology practice, demonstrating the company's commitment to transforming the use of technology in radiology. Last April, Nines also received FDA clearance for artificial intelligence technology that triages mass effect conditions and intracranial hemorrhages.

Lung nodule measurement can be tedious and time consuming as each nodule has to be measured carefully to determine changes in size over time. NinesMeasure enables radiologists to quickly measure the long and short axes of selected nodules with a high level of accuracy. It can also help address inter-study consistency spanning a patient's full treatment program.

In addition to the FDA clearance for its lung nodule measurement tool, Nines also announced that its recent focus on improving clinical workflow has demonstrated a40% gain in efficiencyover three months. Nines radiologists are seeing reduced interruptions from non-diagnostic workflow automation, from one-click communications with Emergency Room physicians to an "always-ready" worklist of studies. Imaging centers and hospitals that rely on Nines can see more reliable turnaround times because radiologists are not distracted by administrative tasks and other non-diagnostic functions, which slow time to diagnosis for patients.

The radiologist-centric advances and the FDA clearances are the result of a unique innovation model and culture that the practice embodies. At Nines, radiologists are paired with in-house engineers to collaborate and develop new approaches and advanced techniques never seen in a radiology practice. For example, while most facilities typically see a tech update once a year, workflow feature enhancements occur about every three days at Nines, saving radiologists valuable time.

"In general, radiology is tech-forward in its use of digital imaging," saidDavid Stavens, Ph.D., co-founder and CEO of Nines. "But innovation can make it better. Nines has been leading the way by pairing two seemingly disparate groups skilled radiologists and brilliant engineers to transform the practice of radiology to be more accessible and more efficient, delivering faster results for quality patient care. That is worth innovating."

Stavens is an entrepreneur and scientist who has pioneered several technologies and co-founded companies in Silicon Valley. He has published in the fields of machine learning, artificial intelligence and robotics. Stavens co-founded Nines in 2017 withAlexander Kagen, M.D., who is Nines' chief medical officer and serves as the site chair of Radiology at Mount Sinai West and Mount Sinai Morningside Hospitals inNew York City. Kagen is also associate professor of Radiology at the Icahn School of Medicine atMount Sinai.

For more information: http://www.nines.com

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