Category Archives: Artificial Intelligence

TORONTO, Feb. 14, 2022 /CNW/ - The Canadian Life and Health Insurance Association (CLHIA) is pleased to announce the launch of an industry initiative to pool claims data and use advanced artificial intelligence tools to enhance the detection and investigation of benefits fraud.

Every insurer in Canada has their own internal analytics to detect fraud within their book of business. This new initiative, led by the CLHIA and its technology provider Shift Technology will deploy advanced AI to analyze industry-wide anonymized claim data. By identifying patterns across millions of records, the program is enhancing the effectiveness of benefits fraud investigations across the industry.

We expect that the initiative will expand in scope over the coming years to include even more industry data.

"Fraudsters are taking increasingly sophisticated steps to avoid detection," said Stephen Frank, CLHIA's President and CEO. "This technology will give insurers the edge they need to identify patterns and connect the dots across a huge pool of claims data over time, leading to more investigations and prosecutions."

"The capability for individual insurers to identify potential fraud has already proven incredibly beneficial," explained Jeremy Jawish, CEO and co-founder of Shift Technology. "Through the work Shift Technology is doing with the CLHIA, we are expanding that benefit across all member organizations, and providing a valuable fraud fighting solution to the industry at large."

Insurers paid out nearly $27 billion in supplementary health claims in 2020. Employers and insurers lose what is estimated to be millions of dollars each year to fraudulent group health benefits claims. The costs of fraud are felt by insurers, employers and employees and put the sustainability of group benefits plans at risk.

About CLHIAThe CLHIA is a voluntary association whose member companies account for 99 per cent of Canada's life and health insurance business. These insurers provide financial security products including life insurance, annuities (including RRSPs, RRIFs and pensions) and supplementary health insurance to over 29 million Canadians. They hold over $1 trillion in assets in Canada and employ more than 158,000 Canadians. For more information, visit http://www.clhia.ca.

About Shift TechnologyShift Technology delivers the only AI-native decision automation and optimization solutions built specifically for the global insurance industry. Addressing several critical processes across the insurance policy lifecycle, the Shift Insurance Suite helps insurers achieve faster, more accurate claims and policy resolutions. Shift has analyzed billions of insurance transactions to date and was presented Frost & Sullivan's 2020 Global Claims Solutions for Insurance Market Leadership Award. For more information, visit http://www.shift-technology.com.

SOURCE Canadian Life and Health Insurance Association Inc.

For further information: Kevin Dorse, Assistant Vice President, Strategic Communications and Public Affairs, CLHIA, (613) 691-6001, [emailprotected]; Rob Morton, Corporate Communications, Shift Technology, 617-416-9216, [emailprotected]

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Life and health insurers to use advanced artificial intelligence to reduce benefits fraud - Canada NewsWire

The Vector Institute has teamed up with Telus Corp., Deloitte and Roche Canada to help health care professionals learn more about the symptoms of long COVID.Nathan Denette/The Canadian Press

A Toronto tech institute is using artificial intelligence and social media to track and determine which long-COVID symptoms are most prevalent.

The Vector Institute, an artificial intelligence organization based at the MaRS tech hub in Toronto, has teamed up with telecommunications company Telus Corp., consulting firm Deloitte and diagnostics and pharmaceuticals business Roche Canada to help health care professionals learn more about the symptoms that people with a long-lasting form of COVID experience.

They built an artificial intelligence framework that used machine learning to locate and process 460,000 Twitter posts from people with long COVID defined by the Canadian government as people who show symptoms of COVID-19 for weeks or months after their initial recovery.

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The framework parsed through tweets to determine which are first-person accounts about long COVID and then tallied up the symptoms described. It found fatigue, pain, brain fog, anxiety and headaches were the most common symptoms and that many with long COVID experienced several symptoms at once.

Replicating that research without AI would have taken a huge amount of hours worked and staff members, who would have had to manually locate hundreds of thousands of social-media posts or people and siphon out those without long-COVID or first-person accounts and count symptoms.

AI is very good at taking large sets of large amounts of data to find patterns, said Cameron Schuler, Vectors chief commercialization officer and vice-president of industry innovation. Its for stuff that is way too big for any human to actually be able to hold this in their brain.

The framework speeds up the research process around a virus that is quickly evolving and still associated with so many unknowns.

So far, long COVID isnt well understood. Theres no uniform way to diagnose it nor a single treatment to ease or cure it. Information is key to giving patients better outcomes and ensuring hospitals arent overwhelmed in the coming years.

A survey conducted in May, 2021, of 1,048 Canadians with long COVID, also known as post-COVID syndrome, found more than 100 symptoms or difficulties with everyday activities.

COVID-19 can affect people for the long haul and theyre getting the short shrift

Canada should lead the effort to help COVID long-haulers

About 80 per cent of adults surveyed by Viral Neuro Exploration, COVID Long Haulers Support Group Canada and Neurological Health Charities Canada reported one or more symptoms between four and 12 weeks after they were first infected.

Sixty per cent reported one or more symptoms in the long term. The symptoms were so severe that about 10 per cent are unable to return to work in the long term.

Researchers and those behind the technology are hopeful it will quickly contribute to the worlds fight against long COVID, but are already imagining ways they can advance the framework even further or apply it to other situations.

This is a novel kind of tool, said Dr. Angela Cheung, a senior physician scientist at the University Health Network, who is running two large studies on long COVID.

Im not aware of anyone else having done this and so I think it really may be quite useful going forward in health research.

Researchers say preliminary uses of the framework show it can help uncover patterns related to symptom frequencies, co-occurrence and distribution over time.

It could also be applied to other health events such as emerging infections or rare diseases or the effects of booster shots on infection.

Sign up for the Coronavirus Update newsletter to read the days essential coronavirus news, features and explainers written by Globe reporters and editors.

This content appears as provided to The Globe by the originating wire service. It has not been edited by Globe staff.

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Toronto tech institute tracking long COVID with artificial intelligence, social media - The Globe and Mail

REHOVOT, Israel and HEIDELBERG, Germany, Feb. 13, 2022 /PRNewswire/ --AION Labs, a first-of-its-kind innovation lab spearheading the adoption of AI technologies and computational science to solve therapeutic challenges, and German independent research institute BioMedX, announced today the launch of the third global call for application to identify biomedical scientists and inventors to form a new startup at AION Labs' headquarters in Rehovot, Israel.

The chosen AION Labs startup team will be sponsored by several industry-leading partners and supported by the Israel Innovation Authority (IIA) and Digital Israel office. The sponsors of this call for application are AstraZeneca, Israel Biotech Fund, Merck, Pfizer and Teva Pharmaceuticals, with close support from Amazon Web Services (AWS).

Antibody treatments continue to be the standard of care for several disease areas and have emerged as cornerstone therapies during the current pandemic. However, despite being primary treatment modalities for over two decades, the cycle times for the discovery and optimization of therapeutic antibodies can still span several years. In order to achieve developable antibody therapeutics exhibiting target-specific binding, stability and scalability, several biophysical parameters need to be streamlined. The use of artificial intelligence (AI) has the potential to broaden the explored sequence space, accelerate the selection of fully optimized antibodies, and shorten overall lead discovery times by successfully predicting relevant parameters.

AION Labs is inviting computational biologists, bioinformatics and cheminformatics scientists, AI researchers, and antibody or protein engineers at academic and industry research labs worldwide to propose the development of a next-generation computational platform to optimize antibodies for targeted therapies with enhanced properties, including developability or manufacturability, stability, aggregation, immunogenicity, pharmacokinetics and tissue distribution. The ultimate solution is an AI platform that receives sequences of binders and generates novel variants with optimized IgG sequences, biophysical and targeting properties. The goal of the AI algorithm is to make an existing antibody a better drug while reducing design iterations, optimization of cycle times and lowering attrition rates. The AION Labs pharma partners involved in this project will provide a wealth of data for model training and their expertise in setting specifications and evaluating the outcome. Original ideas that go far beyond the current state-of-the-art are being encouraged.

"AION Labs is eager to tackle yet another pharmaceutical R&D challenge," said Dr. Yair Benita, CTO of AION Labs. "We're anticipating another strong round of applications, and look forward to working together with the chosen startup to develop a cutting-edge solution to substantially improve the design and optimization of antibodies for targeted therapies."

As part of the online application procedure, interested candidates are requested to submit a competitive project proposal. After a preliminary short-listing round, candidates will be invited to a five-day innovation boot camp in Rehovot. With the support of experienced mentors from the pharma, tech and VC industries, the winning team of scientists will be trained and guided during a fully-funded incubation period of up to four years towards becoming an independent startup.

Further details about this call for application can be found on the AION Labs website: http://www.aionlabs.com. Interested candidates are invited to apply via the BioMed X Career Space at https://career.bio.mx/call/2022-AIL-C03 before April 10, 2022.

Sign up here to join us for an informative webinar to learn more about AION Labs and this challenge on March 10, 2022 at 11 AM EST: https://us02web.zoom.us/webinar/register/WN_Qu788xk8SfycAm9vaTCpZg

About AION LabsAION Labs is a first-of-its-kind alliance of AstraZeneca, Merck, Pfizer, Teva, the Israel Biotech Fund and Amazon Web Services (AWS) that have come together with one clear mission: to create and adopt groundbreaking new AI technologies that will transform the process of drug discovery and development in order to contribute to the health and well-being of all people world-wide.

AION Labs is a unique venture hub where brilliant innovators and scientist-founders convene from around the world to solve the biggest R&D challenges guided by years of accumulated know-how, data and experience in pharma. The lab leverages its partners' wealth of knowledge and a new multidisciplinary mindset with the ingenuity, agility and innovative power of Israel's start-up ecosystem, to develop strong companies with clear long-term strategies, that will pave the way to the future of healthcare. AION Labs cultivates innovation from within; its unique venture creation process bridges the gap between outstanding academic research in the field of AI and the biggest R&D needs in the discovery and development of new medicines for the benefit of patients.

For more information, visit aionlabs.com

About BioMed XBioMed X is an independent research institute located on the campus of the University of Heidelberg in Germany, with a world-wide network of partner locations. Together with our partners, we identify big biomedical research challenges and provide creative solutions by combining global crowdsourcing with local incubation of the world's brightest early-career research talents. Each of the highly diverse research teams at BioMed X has access to state-of-the-art research infrastructure and is continuously guided by experienced mentors from academia and industry. At BioMed X, we combine the best of two worlds academia and industry and enable breakthrough innovation by making biomedical research more efficient, more agile, and more fun.

For more information, visit bio.mx

Media Contact:Lior FeiginFINN Partners for AION Labs[emailprotected]@LiorFeigin+1 929 588 2016+972 54 282 4503

Logo - https://mma.prnewswire.com/media/1708278/AION_Labs_Logo.jpg

SOURCE AION Labs

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AION Labs, Powered by BioMed X, Launches Third Global Call for Application: Artificial Intelligence for Design and Optimization of Antibodies for...

Balcan Nina Professor ninamf@cs.cmu.edu Dannenberg Roger Professor Emeritus rbd@cs.cmu.edu Datta Anupam Associate Professor, CSD, ECE danupam@andrew.cmu.edu Erdmann Michael Professor, Computer Science and Robotics me@cs.cmu.edu Fahlman Scott Research Faculty Emeritus sef@cs.cmu.edu Faloutsos Christos Professor christos@cs.cmu.edu Gupta Anupam Professor anupamg@cs.cmu.edu Hodgins Jessica Allen Newell University Professor of Computer Science And Robotics jkh@cs.cmu.edu Jia Zhihao Assistant Professor zhihaoj2@andrew.cmu.edu Lee Tai-Sing Professor tai@cs.cmu.edu Mason Matthew Professor, Computer Science and Robotics matt.mason@cs.cmu.edu Maxion Roy Research Professor maxion@cs.cmu.edu Mitchell Tom Affiliated Faculty, E. Fredkin University Professor, Faculty Founders University Professor mitchell@cs.cmu.edu O'Toole Matthew Assistant Professor motoole2@andrew.cmu.edu Pollard Nancy Professor nsp@cs.cmu.edu Reddy Raj Moza Bint Nasser University Professor; Affiliated Faculty reddy@cs.cmu.edu Rosenfeld Roni Affiliated Faculty; Department Head, Machine Learning Department; Professor roni@cs.cmu.edu Rudnicky Alexander Emeritus Faculty ar28@andrew.cmu.edu Sandholm Tuomas Angel Jordan University Professor of Computer Science sandholm@cs.cmu.edu Shah Nihar Assistant Professor nihars@cs.cmu.edu Simmons Reid Research Professor reids@cs.cmu.edu Sleator Daniel Professor sleator@cs.cmu.edu Touretzky David Research Professor dst@cs.cmu.edu Veloso Manuela M. Professor Emeritus veloso@cmu.edu Wactlar Howard Research Professor Emeritus hdw@cs.cmu.edu Woodruff David Associate Professor dwoodruf@cs.cmu.edu Xing Eric P. Professor epxing@cs.cmu.edu

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Artificial Intelligence | Carnegie Mellon University ...

Residential Real Estate in the United States is in aggregate a $45 trillion space. As such, reliable, intelligent, timely, accurate, and usable data in real estate is vital not only to this vertical but to the entire economy." - Malcolm Cannon

LOS ANGELES (PRWEB) February 05, 2022

Reliance on multiple data points, from historic prices and trends to property ownership data, is nothing new. However, the data that we use to make our housing choices have become increasingly sophisticated. Quantariums valuation models provide property valuations through a self-learning AI engine. Quantariums advances in Computer Vision (CV) technology is allowing AVM to transcend historical constraints; understanding their journey and the impact of AI is critical to the future of real estate.

Quantarium is a leading producer and purveyor of value-added data in residential real estate. Their data and analytics scientists and experts illustrated great innovation in their approach to building the industrys leading RE data lake, so it is a team-wide validation to receive this distinction. When asked what the potential of this space is, Cannon explained: Residential Real Estate in the United States is in aggregate a $45 trillion space. In addition, homes are the most vital and valuable of most families investments/assets. As such, reliable, intelligent, timely, accurate, and usable data in real estate is vital not only to this vertical but to the entire economy. PropTech, as the area has been called, is a booming industry and were happy to not only be in the space but also to enhance the stories of other players.

Quantariums product and customer journey is an interesting look at how artificial intelligence plays a role in real estate. They started as an idea and ended up building the leading Automated Valuation Model, AVM, that still breaks the tape in the AVM race. They leveraged their knowledge and experience to build a Data and Search platform for real estate, powered by the most comprehensive data lake in the industry. By emphasizing the platform aspect, they could power any company that leverages residential real estate data, from mortgage companies to banks to insurance.

There is a lot of talk in the data industry about technology versus people, from the benefits of consistency and efficiency to the reduction of bias in housing prices and access. When Cannon was asked about this, he answered that Quantarium has never been purely technocratic, despite the fact that it is an AI company: We, like anyone, would affirm the unquestionable importance of human wisdom in all parts of society, including this vital area of the economy. Often, the debate is cast in binary terms, which we reject. Increasingly, it would appear the industry will benefit from, and work towards, an equipoise in understanding both the methods and instruments through which human wisdom and tasking are best applied and those areas in which machine learning technologies can be best advantaged we see the growing confidence in the category of hybrid valuation products as evidence of this.

Cannon understood the importance of implementing this knowledge in this specific area of technology, which is why Quantariums Translucent A.I. is embedded in their Valuation Services Platform (QSVP) as an asset for human-assisted and enhanced technologies. It was intentionally constructed this way. Quantariums translucent A.I.-based technologies provide an auditable line of sight to offer the optimal platform for professional human input integration, as needed, towards maximum possible results. In fact, this should be the expectation; most computerized airplanes and spacecraft still provide manual controls for takeoff and landing, for instance. In other areas such as AVMs Quantariums advances in Computer Vision (CV) technology, AVM is now transcending historical constraints. These AVMs are crossing the threshold of not being able to understand property condition or see inside the structure to account for value adjustments other than assuming a static coefficient of average condition for the year built. Moreover, the fidelity of Quantariums CV can detect and eliminate occupants photos and other iconography that may be humanly interpreted to indicate racial, or ethnic identities that could consciously, or unconsciously introduce unwarranted subjectivity in the valuation, or QC process.

This being said, it was important to Cannon to elaborate on the challenges of AI in addressing bias. At the end of the day, the challenge for AI to assist in addressing bias will need to be understood. Certain axioms remain, particularly about any technology being only as effective as the data it relies upon. Quantarium remains ever cognizant of this. Consequently, it understands that models rely on trailing data and that data unavoidably, at this juncture, has suffused within, whatever prior human biases contributed to skewed valuations. With this understanding in hand, Quantarium refrains from making any announcements regarding AVMs eliminating bias in valuation it is disingenuous and the challenge to eradicate this will take more than just technology. Can it be an asset in addressing the challenge? Absolutely and Quantarium is making significant R&D investments, with a focus on assisting the industry in addressing the challenge.

Certainly, evidence for racial bias in real estate exists. But while technology is often considered a neutral tool, the data it relies on has bias built into it. So real estate data providers have a lot more work to do to identify and eradicate bias, and AI is an important part of that process. Quantarium has already devoted R&D resources to this area.

Cannon is now looking towards the future and thinking about his vision for Quantarium in 2022. Like with all companies, Quantarium has had to adjust to the realities of a Covid economy and the impact that has wrought on the global business climate. Despite this, they are able to focus on strong tailwinds, such as Machine Learning, Computer Vision (CV), and Data Innovation. These tailwinds are in demand as they play a larger part in the future of both the commercial markets and the regulatory environment. Moreover, Quantarium has made a mark in several areas of this space and as a result are fortunate to have a set of relationships with incredible experts across the industry, including in the newly burgeoning PropTech space. Cannon explains, then, that their vision is to provide our quickly expanding customer adoptions with innovation and rapid deployments of proficiency technologies, spanning from our new Computer Vision APIs and Valuation services, through to our Smart Market Explorer within the Quantarium TerraVerse to help our customers navigate the industry challenges ahead. More on that soon!

Along with other digital communities, data communities play a crucial role in bringing down barriers by providing use cases, datasets, and professional opinions from data experts all in one place. Find useful data and learn how to use the data you have. Whether you are a data leader, involved in data analytics, or a decision-maker needing to show growth, the DataHunters community is here to help you power your decision-making with the right data.

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How Artificial Intelligence is Transforming the Real Estate Space - PR Web

In todays world which is ruled by digitisation, technology is powering journalism. Artificial intelligence involved jobs accounts to around 10-12% of the total functionalities in the industry.

Machine-Written Articles: AI can help human reporters in more complex work, such as long-form articles, in-depth analyses and investigative journalism. AI-written articles are currently limited to simple and formulaic topics including stock market results, sports game scores, etc.

Transcribing Audio and Video Interviews: AI can save reporters valuable time by transcribing audio and video interviews. It converts audio data into text so that journalists can focus on deriving insights rather than transcribing audio or video interviews.

Flagging Alerts: AI can examine large databases and send journalists alerts as soon as a trend or anomaly emerges from big data. It can provide content producers and publishers tools to identify fake news and lessen their impact on their readership.

Powering Journalistic Processes: AI systems can improve journalistic processes and workflows. It can help organizations streamline their distributed processes for gleaning information, contacting sources and backend operations like dealing with the advertisers.

Controls bias:Bias is a global issue and news media have no escape from it. However, AI assists in reducing the subjective interpretation of the data of the human as its machine learning algorithms are trained to consider accuracy.

Robot as news reporters:Despite invading manufacturing, healthcare and marketing industry, robots are now in newsrooms as well. Chinas state-run Xinhua News Agency made a breakthrough by deploying a robot as English AI Anchor.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Artificial Intelligence in Journalism: Where is Media Headed? - Analytics Insight

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Global Wearable Artificial Intelligence (AI) Market Projected To Reach About To $42.34 Billion by end of 2026 | Exclusive Report by Esticast Research ...

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Artificial Intelligence (AI) In Drug Discovery Market Global Analysis 2021-2028: IBM Corporation, Microsoft, Atomwise Inc., Deep Genomics, Cloud...

Industry leaders around the world are using artificial intelligence to enhance their business with marketing technology. Whether its analyzing consumer interests and data, guiding sales decisions and social media campaigns or other applications, artificial intelligence is changing the way we understand marketing in many industries. Lets talk about the latest ways that businesses can utilize these powerful tools to achieve their marketing goals.

Technology changes every day. A lot can change over several years, especially intrending artificial intelligence technologies. The same goes for AI in marketing applications. Understanding the basic ideas behind applications of AI in marketing solutions can generate unique ideas that can break new ground in various industries.

AI can help automate projects to make businesses more efficient. According to Accenture, the productivity of businesses can be improved by 40 percent when utilizing AI. This not only can save time and money but can enable your company to focus their efforts on providing quality experiences for customers rather than spending too much time moving things from one spreadsheet to another.

AI can also help minimize errors in marketing processes. Artificial intelligence can complete specialized tasks with greater efficiency than humans can so long as supervision and guidance are involved. Often in cases where AI fails to provide the right results, human error was involved in setting up the AI program with appropriate data or it was used in a way that was not intended.

Because AI can dramatically speed up the process of marketing campaigns, reduce costs, and improve efficiency, artificial intelligence is much more likely to result in an increased return on investment (ROI).

Artificial intelligence is a strong tool when used alongside high-quality data. Many companies have had positive results in the real world when combining their market research data with artificial intelligence. This enables them to do all sorts of things. A big part of this trending use case is target group segmentation. AI is far quicker and more efficient at performing this task than humans are.

By investigating their target audiences more deeply, businesses can make more personalized offers to them that they are more likely to accept.

When we examine how this looks up close, we can get a better understanding of how it works. A nationwide department store can take a look at the data theyve collected on their customers and narrow down their search to those interested in food. Using artificial intelligence, we can identify customers that have a strong preference for organic foods. By quickly using AI to analyze the habits and preferences of these consumers, campaigns can be tailored toward them with greater efficiency to improve sales.

Target group segmentation is one of the keystone elements of personalizing a marketing campaign, but there are many other ways that artificial intelligence can help businesses personalize experiences for their audiences and customers. According to Salesforce, 76 percent of customers want businesses to have a clear understanding of their personal expectations.

One way that businesses do this with AI is to use predictive marketing analytics. By having AI analyze data of past events, it can reasonably and accurately infer how performance will look in the future based on a variety of factors. More importantly, analyzing what users like most can be useful when looking to suggest products to them.

For example, Amazon is the champion of this strategy. When browsing on their site, Amazons artificial intelligence knows about what you have bought in the past. Based on this, it can suggest products to you in your feed. It also knows what other users like you are interested in, meaning that they can provide suggestions based on that activity. This results in very personalized suggestions that can lead to higher conversions.

Spotify also takes advantage of this to make more effective music suggestions for you. It also uses this data to invest in artists to create new music that will be generally liked by a wider audience on a broader scale.

However, most personalization methods with AI tend to start from the top-down and personalize to the individual instead of an entire group. The more that the system can understand the individual user, the more likely that conversions can be made. Every user has variations that differentiate them from the larger group, so no group marketing campaign will ever be as effective as a campaign that targets specific individuals and their own interests.

The ability to use artificial intelligence to predict the success of marketing campaigns and to better personalize experiences for users is a powerful technological trend that will continue for years to come. Adaptation to include this tool in your arsenal is critical for relevancy at scale.

One of the most difficult challenges of the onset of the 2020 COVID-19 pandemic was a surge in sales of various products by stockpilers. Shortages of toilet paper became a notorious meme on the Internet as stores struggled to maintain stock in the face of the buying panic. Eventually, stock would be controlled by buying limitations. However, there was an important lesson to be learned here: demand forecasting and dynamic pricing could have prevented a great deal of this struggle.

Earlier we established that artificial intelligence is a powerful tool for analyzing past data in order to predict future activity. The same principle can be applied here. Its possible that AI can be used to analyze consumer interests, world events, and other sources to determine if there will be a rise in demand for certain products.

Using the pandemic as an example, BlueDot is a program that already can analyze the likelihood of a disease spreading across the world. If worldwide or nationwide emergencies can be predicted in this manner, stores can automatically begin ordering more products like toilet paper, medicine, and more. Not only can this help maintain stock and improve sales for stores, but it can also help the public better manage the disaster and lead to a swifter recovery.

This can also be used to dynamically and automatically raise prices. This can be used to better control stock during times of high demand and panic buying, naturally dissuading customers from bulk buying beyond reasonable amounts, as well as optimizing revenue for your business.

Dynamic pricing and demand forecasting for every business is unique. From the types of items that you carry to the types of consumers that you are serving, a custom solution made by your team or by an external vendor may be the best option for creating a system that can accomplish your goals.

Providing unique and engaging content can be challenging. While AI can automatically generate content, it often can be more trouble than its worth.

Although this technology is improving and can be very effective in some contexts, a more widely accessible and reliable possibility is for AI to offer intelligent suggestions to human writers. AI-guided suggestions for writers form the basis of features in applications like Grammarly, Microsoft Editor, Google Docs, Microsoft Word, Yoast, SEMRush, and more.

Adobe Premiere Pro uses AI for a variety of purposes, such as automatically matching colors and managing sound mixing against voiceovers. Whats great about content creation is that humans can create unique and interesting content that AI cannot, but AI can help us augment our talents to improve the quality of the final product.

AI can also help us with image generation and manipulation tasks:

All this can be done with the help of generative adversarial networks (GANs) that learn the structure of the complex real-world data examples and generate similar synthetic examples.

Does it mean that robots can replace designers? Absolutely not. The power ofAI in designis mostly about optimization and speed. Designers armed with AI tools can work faster and more effectively.

One particular avenue of AI in content creation comes from its role in marketing campaigns via email. eBay is a particularly good example of AI email marketing, utilizing a third-party service called Phrasee andnatural language processingto improve email open rates by 15.8 percent and improve clicks by 31.2 percent.

This technology is used to optimize the subject text and headline copy automatically to find the most effective variation to use with eBays audience. The AI-generated portions of the emails are attentive to the tone of voice to maximize their success.

Aside from the fact that natural language processing is improving as years go by, AI-based email marketing at its most basic can be automated with a series of A/B testing. However, the more demographic data and natural language processing that can be incorporated into the project, the better the results. Advanced artificial intelligence algorithms can improve the dynamic optimization of email marketing greatly, as seen in eBays case.

Ultimately, the future of AIs role in marketing technologies will be determined by imagination and innovation. Combining different technologies together can result in businesses outcompeting other leading players in the market for years. At the bare minimum, understanding whats already in use is important for bringing your company up to speed to remain relevant and competitive in the market.

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Artificial Intelligence in Marketing: Boost the Growth in 2022 - IoT For All

Even as the worlds leading militaries race to adopt artificial intelligence in anticipation of future great power war, security forces in one of the worlds most conflict-prone regions are opting for a more measured approach. In Africa, AI is gradually making its way into technologies such as advanced surveillance systems and combat drones, which are being deployed to fight organized crime, extremist groups, and violent insurgencies. Though the long-term potential for AI to impact military operations in Africa is undeniable, AIs impact on organized violence has so far been limited. These limits reflect both the novelty and constraints of existing AI-enabled technology.

Artificial intelligence and armed conflict in Africa

Artificial intelligence (AI), at its most basic, leverages computing power to simulate the behavior of humans that requires intelligence. Artificial intelligence is not a military technology like a gun or a tank. It is rather, as the University of Pennsylvanias Michael Horowitz argues, a general-purpose technology with a multitude of applications, like the internal combustion engine, electricity, or the internet. And as AI applications proliferate to military uses, it threatens to change the nature of warfare. According to the ICRC, AI and machine-learning systems could have profound implications for the role of humans in armed conflict, especially in relation to: increasing autonomy of weapon systems and other unmanned systems; new forms of cyber and information warfare; and, more broadly, the nature of decision-making.

In at least two respects, AI is already affecting the dynamics of armed conflict and violence in Africa. First, AI-driven surveillance and smart policing platforms are being used to respond to attacks by violent extremist groups and organized criminal networks. Second, the development of AI-powered drones is beginning to influence combat operations and battlefield tactics.

AI is perhaps most widely used in Africa in areas with high levels of violence to increase the capabilities and coordination of law enforcement and domestic security services. For instance, fourteen African countries deploy AI-driven surveillance and smart-policing platforms, which typically rely on deep neural networks for image classification and a range of machine learning models for predictive analytics. In Nairobi, Chinese tech giant Huawei has helped build an advanced surveillance system, and in Johannesburg automated license plate readers have enabled authorities to track violent, organized criminals with suspected ties to the Islamic State. Although such systems have significant limitations (more on this below), they are proliferating across Africa.

AI-driven systems are also being deployed to fight organized crime. At Liwonde National Park in Malawi, park rangers use EarthRanger software, developed by the late Microsoft co-founder, Paul Allen, to combat poaching using artificial intelligence and predictive analytics. The software detects patterns in poaching that the rangers might overlook, such as upticks in poaching during holidays and government paydays. A small, motion-activated poacher cam relies on an algorithm to distinguish between humans and animals and has contributed to at least one arrest. Its not difficult to imagine how such a system might be repurposed for counterinsurgency or armed conflict, with AI-enabled surveillance and monitoring systems deployed to detect and deter armed insurgents.

In addition to the growing use of AI within surveillance systems across Africa, AI has also been integrated into weapon systems. Most prominently, lethal autonomous weapons systems use real-time sensor data coupled with AI and machine learning algorithms to select and engage targets without further intervention by a human operator. Depending on how that definition is interpreted, the first use of a lethal autonomous weapon system in combat may have taken place on African soil in March 2020. That month, logistics units belonging to the armed forces of the Libyan warlord Khalifa Haftar came under attack by Turkish-made STM Kargu-2 drones as they fled Tripoli. According to a United Nations report, the Kargu-2 represented a lethal autonomous weapons system because it had been programmed to attack targets without requiring data connectivity between the operator and munition. Although other experts have instead classified the Kargu-2 as a loitering munition, its use in combat in northern Africa nonetheless points to a future where AI-enabled weapons are increasingly deployed in armed conflicts in the region.

Indeed, despite global calls for a ban on similar weapons, the proliferation of systems like the Kargu-2 is likely only beginning. Relatively low costs, tactical advantages, and the emergence of multiple suppliers have led to a booming market for low-and-mid tier combat drones currently being dominated by players including Israel, China, Turkey, and South Africa. Such drones, particularly Turkeys Bakratyar TB2, have been acquired and used by well over a dozen African countries.

While the current generation of drones by and large do not have AI-driven autonomous capabilities that are publicly acknowledged, the same cannot be said for the next generation, which are even less costly, more attritable, and use AI-assisted swarming technology to make themselves harder to defend against. In February, the South Africa-based Paramount Group announced the launch of its N-RAVEN UAV system, which it bills as a family of autonomous, multi-mission aerial vehicles featuring next-generation swarm technologies. The N-RAVEN will be able to swarm in units of up to twenty and is designed for technology transfer and portable manufacture within partner countries. These features are likely to be attractive to African militaries.

AIs limits, downsides, and risks

Though AI may continue to play an increasing role in the organizational strategies, intelligence-gathering capabilities, and battlefield tactics of armed actors in Africa and elsewhere, it is important to put these contributions in a broader perspective. AI cannot address the fundamental drivers of armed conflict, particularly the complex insurgencies common in Africa. African states and militaries may overinvest in AI, neglecting its risks and externalities, as well as the ways in which AI-driven capabilities may be mitigated or exploited by armed non-state actors.

AI is unlikely to have a transformative impact on the outbreak, duration, or mitigation of armed conflict in Africa, whose incidence has doubled over the past decade. Despite claims by its makers, there is little hard evidence linking the deployment of AI-powered smart cities with decreases in violence, including in Nairobi, where crime incidents have remained virtually unchanged since 2014, when the citys AI-driven systems first went online. The same is true of poaching. During the COVID-19 pandemic, fewer tourists and struggling local economies have fueled significant increases, overwhelming any progress that has resulted from governments adopting cutting-edge technology.

This is because, in the first place, armed conflict is a human endeavor, with many factors that influence its outcomes. Even the staunchest defenders of AI-driven solutions, such as Huawei Southern Africa Public Affairs Director David Lane, admit that they cannot address the underlying causes of insecurity such as unemployment or inequality: Ultimately, preventing crime requires addressing these causes in a very local way. No AI algorithm can prevent poverty or political exclusion, disputes over land or national resources, or political leaders from making chauvinistic appeals to group identity. Likewise, the central problems with Africas militariesendemic corruption, human rights abuses, loyalties to specific leaders and groups rather than institutions and citizens, and a proclivity for ill-timed seizures of powerare not problems that artificial intelligence alone can solve.

In the second place, the aspects of armed conflict that AI seems most likely to disruptremote intelligence-gathering capabilities and air powerare technologies that enable armies to keep enemies at arms-length and win in conventional, pitched battles. AIs utility in fighting insurgencies, in which non-state armed actors conduct guerilla attacks and seek to blend in and draw support from the population, is more questionable. To win in insurgencies requires a sustained on the ground presence to maintain order and govern contested territory. States cannot hope to prevail in such conflicts by relying on technology that effectively removes them from the fight.

Finally, the use of AI to fight modern armed conflict remains at a nascent stage. To date, the prevailing available evidence has documented how state actors are adopting AI to fight conflict, and not how armed non-state actors are responding. Nevertheless, states will not be alone in seeking to leverage autonomous weapons. Former African service members speculate that it is only a matter of time before before the deployment of swarms or clusters of offensive drones by non-state actors in Africa, given their accessibility, low costs, and existing use in surveillance and smuggling. Rights activists have raised the alarm about the potential for small, cheap, swarming slaughterbots, that use freely available AI and facial recognition systems to commit mass acts of terror. This particular scenario is controversial, but according to American Universitys Audrey Kurth Cronin, it is both technologically feasible and consistent with classic patterns of diffusion.

The AI armed conflict evolution

These downsides and risks suggest the continued diffusion of AI is unlikely to result in the revolutionary changes to armed conflict suggested by some of its more ardent proponents and backers. Rather, modern AI is perhaps best viewed as continuing and perhaps accelerating long-standing technological trends that have enhanced sensing capabilities and digitized and automated the operations and tactics of armed actors everywhere.

For all its complexity, AI is first and foremost a digital technology, its impact dependent on and difficult to disentangle from a technical triad of data, algorithms, and computing power. The impact of AI-powered surveillance platforms, from the EarthRanger software used at Liwonde to Huawei-supplied smart policing platforms, isnt just a result of machine-learning algorithms that enable human-like reasoning capabilities, but also on the ability to store, collect, process collate and manage vast quantities of data. Likewise, as pointed out by analysts such as Kelsey Atherton, the Kargu 2 used in Libya can be classified as an autonomous loitering munition such as Israels Harpy drone. The main difference between the Kargu 2 and the Harpy, which was first manufactured in 1989, is where the former uses AI-driven image recognition, the latter uses electro-optical sensors to detect and hone in on enemy radar emissions.

The diffusion of AI across Africa, like the broader diffusion of digital technology, is likely to be diverse and uneven. Africa remains the worlds least digitized region. Internet penetration rates are low and likely to remain so in many of the most conflict-prone countries. In Somalia, South Sudan, Ethiopia, the Democratic Republic of Congo, and much of the Lake Chad Basin, internet penetration is below 20%. AI is unlikely to have much of an impact on conflict in regions where citizens leave little in the way of a digital footprint, and non-state armed groups control territory beyond the easy reach of the state.

Taken together, these developments suggest that AI will cause a steady evolution in armed conflict in Africa and elsewhere, rather than revolutionize it. Digitization and the widespread adoption of autonomous weapons platforms may extend the eyes and lengthen the fists of state armies. Non-state actors will adopt these technologies themselves and come up with clever ways to exploit or negate them. Artificial intelligence will be used in combination with equally influential, but less flashy inventions such as the AK-47, the nonstandard tactical vehicle, and the IED to enable new tactics that take advantage or exploit trends towards better sensing capabilities and increased mobility.

Incrementally and in concert with other emerging technologies, AI is transforming the tools and tactics of warfare. Nevertheless, experience from Africa suggests that humans will remain the main actors in the drama of modern armed conflict.

Nathaniel Allen is an assistant professor with the Africa Center for Strategic Studies at National Defense University and a Council on Foreign Relations term member. Marian Ify Okpali is a researcher on cyber policy and an academic specialist at the Africa Center for Strategic Studies at National Defense University. The opinions expressed in this article are those of the authors.

Microsoft provides financial support to the Brookings Institution, a nonprofit organization devoted to rigorous, independent, in-depth public policy research.

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