Daily Archives: February 5, 2022

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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Artificial Intelligence Creeps on to the African Battlefield - Brookings Institution

If I were to ask someone why they chose a career in information technology, I doubt they would respond withI love data entry!,I could debug code all day long!, orHandling tickets is so much fun, Id do it even if I didnt get paid for it.

Fortunately, AI can help. Here are the top three ways AI can help automate manual IT tasks, thereby freeing up precious resources and benefiting your teams, businesses, and customers.

Grace Murray Hopper was a Navy rear admiral and computer programming pioneer who worked on the Mark II computer at Harvard in the 1940s. On September 9, 1947, Hopper traced an error with the Mark II to of all things a dead moth in the relay. The insects remains were taped in the teams logbook with the caption, First actual case of a bug being found.

While Hopper and her team werent the first to use the term bug to describe a system glitch, they certainly helped popularize it. Of course, software bugs are decidedly unpopular. IT departments and software engineers have all felt the pain of toiling over lines of code trying to reproduce and locate problems.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders:Cheat sheet: AI glossary. ]

To be as good as human engineers, an AI tool would need to possess levels of reasoning and creativity it simply hasnt yet reached. But AI can still be tremendously effective in exception and anomaly detection. You train it on normal usage and it detects when something is off.

Another advantage AI has over humans is its pattern detection. Lets say a system is crashing at the same time every week or after memory usage hits a certain level. An AI tool could easily connect the dots. AI can learn which behaviors of your developers and which code patterns that are checked into your repo are correlated to bugs. This can be used to notify developers that they have done something that is likely to break and ask them to check again.

If you had a moth infestation in your home, you could certainly go around swatting them one by one. But wouldnt it be a lot easier to discover where they hide and put out traps?

The adage an ounce of prevention is worth a pound of cure is as true in IT as it is in medicine. Monitoring operations and taking proactive action instead of just reacting to problems as they arise can prevent unexpected downtime and expensive failures.

CIOs and IT professionals are familiar with the value of preventative maintenance to some degree, whether its installing software updates or creating backups. That kind of maintenance is done after a certain amount of time has elapsed or usage has been logged. Its like eating vegetables or getting exercise theyre sound practices for a company.

[ Read also:4 Robotic Process Automation (RPA) trends to watch in 2022.]

Predictive maintenance, on the other hand, is individualized and custom-tailored. It monitors the equipment and its environment, performs tests, and receives equipment feedback to generate individualized predictions. Its like having a blood test show that youre pre-diabetic and in response, you design a low-sugar diet.

People may be uncomfortable with the idea of machines watching them all day. But with AI-enabled predictive maintenance, you watch the machines with other machines.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

Dealing with IT tickets can feel like playing a perpetual game of Whack-A-Mole, but with all of the exhaustion and none of the fun carnival music and prizes.

As we all know, some incidents are worth your attention and others arent at all. And without a proper way to triage incidents, IT departments become overwhelmed. Enter intelligent filters. Theyve been around for years in search engines and email inboxes, distinguishing between good and bad, important and unimportant. For IT departments, they can distinguish between real incidents and noise.

More on artificial intelligence

Using AI techniques like case-based reasoning can help decide which solution to explore first or what additional information to request from a customer to make a diagnosis quickly and accurately. Case-based reasoning systems learn from success and failure, apply sophisticated probabilistic reasoning to identify promising solutions, and create a valuable knowledge base.

With intelligent filters and case-based reasoning, IT managers can better allocate resources for incidents that require human intervention.

While there are numerous existing AI applications that help IT departments and many more yet to be discovered debugging, predictive maintenance, and intelligent filtering are three applications of AI that are essential for any great IT department today.

As AI becomes increasingly integrated into our work, any organization that is not actively exploring automating its more manual IT tasks is wasting valuable financial and human capital and may eventually fall behind.

[ How does AI connect tohybrid cloud strategy? Get the free eBooks,Hybrid Cloud Strategy for DummiesandMulti-Cloud Portability for Dummies. ]

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Artificial intelligence (AI): 3 everyday IT tasks where automation fits - The Enterprisers Project

[90 Pages Report] Artificial Intelligence & Advanced Machine Learning Market Insights 2022 This report contains market size and forecasts of Artificial Intelligence & Advanced Machine Learning in China, including the following market information:

China Artificial Intelligence & Advanced Machine Learning Market Revenue, 2016-2021, 2022-2027, (USD millions)

China top five Artificial Intelligence & Advanced Machine Learning companies in 2020 (%)

The global Artificial Intelligence & Advanced Machine Learning market size is expected to growth from USD million in 2020 to USD million by 2027; it is expected to grow at a CAGR of % during 2021-2027.

The China Artificial Intelligence & Advanced Machine Learning market was valued at USD million in 2020 and is projected to reach USD million by 2027, at a CAGR of % during the forecast period.

The Research has surveyed the Artificial Intelligence & Advanced Machine Learning Companies and industry experts on this industry, involving the revenue, demand, product type, recent developments and plans, industry trends, drivers, challenges, obstacles, and potential risks.

Get a Sample PDF of report https://www.360researchreports.com/enquiry/request-sample/19613829

Leading key players of Artificial Intelligence & Advanced Machine Learning Market are

Artificial Intelligence & Advanced Machine Learning Market Type Segment Analysis (Market size available for years 2022-2027, Consumption Volume, Average Price, Revenue, Market Share and Trend 2015-2027): Smart Wallets, Voice-Assisted Banking

Regions that are expected to dominate the Artificial Intelligence & Advanced Machine Learning market are North America, Europe, Asia-Pacific, South America, Middle East and Africa and others

If you have any question on this report or if you are looking for any specific Segment, Application, Region or any other custom requirements, then Connect with an expert for customization of Report.

Get a Sample PDF of report https://www.360researchreports.com/enquiry/request-sample/19613829

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(New Report) Artificial Intelligence & Advanced Machine Learning Market In 2022 : The Increasing use in Insurance, Banking and Capital Markets is...

SUNNYVALE, Calif.--(BUSINESS WIRE)--Celestial AI, an AI-accelerator company with a proprietary hardware and software platform for machine learning chipsets, today announced a $56 million Series A investment led by Koch Disruptive Technologies (KDT) with participation from Temaseks Xora Innovation fund, The Engine, the venture firm spun out of MIT, Tyche Partners, Mercks corporate venture fund, M-Ventures, IMEC XPand, and venture capital investor in the Princeton University ecosystem, Fitz Gate. The new capital will be used for expanding the global engineering team, product development and strategic supplier engagements, including Broadcom, to build the companys Orion AI accelerator products. Celestial AIs mission is to fundamentally transform the way computing is done with a new processing system, based on their proprietary Photonic Fabric technology platform, that uses light for data movement both within chip and between chips.

Driven by advancements in data communications, robust silicon photonics technology and volume manufacturing ecosystems have been established. The industry is ripe for commercial implementation of Machine Learning (ML) and high-performance computing (HPC) solutions that leverage integrated silicon photonics for data movement. For AI computing applications, data movement is the dominant contributor to system power, and most leading competitive architectures are trading off moderate power reductions for increased system and software complexity. Celestial AIs Photonic Fabric enables optically addressable memory and compute (within chip and chip-to-chip) that decouples their technology from the limitations of electronics and slowdown of Moores Law. Their proprietary architecture enables elegant, low-complexity system software, allowing highly efficient mapping of data and compute without the need for complex optimizations. This software advantage extends to multi-chip exascale systems as the Photonic Fabric democratizes optical access to effectively limitless memory and compute. Celestial AIs Orion AI accelerator products serve an addressable market that is projected by Omida to exceed $70 billion in 2025.

We are addressing the problem of our time in computing efficient data movement, said Celestial AI founder and CEO David Lazovsky. Celestial AIs hybrid photonic-electronic platform allows us to leverage the complementary strengths of electronics for high-performance, high-precision computing and photonics for high-speed, low-power, high-bandwidth data movement. The result is transformational performance advantages relative to electronic-only systems. The ML application benefits extend beyond performance and low power to latency, user friendly software, and low total cost of ownership. Our competitive differentiation will increase with time, as AI model complexity increases, driving increased data movement.

Domain-specific architectures targeted to AI workloads can make up for some of the slowdown in CMOS advancements, but that approach also has its limits. By integrating photonics into accelerators for AI workloads, Celestial AI enables step-change advancements in AI computation. Chips and server systems are limited today by power budget (Thermal Design Power or TDP). The Celestial AI Photonic Fabric allows a redistribution of the fixed power budget from data movement to compute, providing sustainable and expanding performance advantages over all electronic-only solutions. Every Joule of energy saved on data movement can be spent on compute.

Photonics is poised to be the technology to usher in the next era of rapid growth in AI and high-performance computing, and we believe the Celestial AI team has the experience and vision to drive this industry transformation, said Isaac Sigron, Managing Director of KDT, and newly-appointed Celestial AI Board Member. It was Celestial AIs software advantages that ultimately drove our decision to lead this financing. Their system architecture enables unparalleled software simplicity, which translates to ease of use for customers and reduced time to market. Software is the pathway to revenue, and Celestial AIs solution changes the game in this large and rapidly expanding market.

Celestial AI has developed an architecture that uniquely scales across multi-chip systems, and greatly diminishes the development burden on AI teams bringing new applications to market. As the world moves to increasingly complex AI models, we believe that Celestial AIs competitive advantage will only grow over time, said Phil Inagaki, Managing Director at Xora Innovation.

ABOUT CELESTIAL AI

Celestial AI is an AI accelerator company with a proprietary hardware and software technology platform which enables the next generation of high-performance computing solutions. Celestial AIs mission is to fundamentally transform the way computing is done with their proprietary Photonic Fabric technology that uses light for data movement both within chip and between chips.

ABOUT KOCH DISRUPTIVE TECHNOLOGIES

Koch Disruptive Technologies (KDT) is a unique investment firm, focused on empowering founders to create a could-be world. KDT provides a flexible, multi-stage investment approach which includes both traditional venture and growth stages. We work with principled entrepreneurs who are building transformative companies, disrupting the status quo, and creating new platforms. KDT is a subsidiary of Koch Industries, one of the largest privately held companies in the world with $115 billion in revenue and operating in more than 70 countries. KDT helps its partners unlock their full potential by bringing Kochs capabilities and network to them, structuring unique capital solutions, and embracing a long-term, mutual benefit mindset. For more information, visit http://www.kochdisrupt.com.

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Celestial AI Raises $56 Million Series A to Disrupt the Artificial Intelligence Chipset Industry with Novel Photonic-Electronic Technology Platform -...

A web-based cohort study suggests that, if machine learning algorithms are provided with a sufficiently wide range of predictive data, they can be induced to analyse epidemiologic data and predict the probability of conception with a discrimination accuracy which exceeds earlier studies.

One focus for AI research has been in predicting the chance of pregnancy - with varying success. A study last year found an AI-based model outperformed clinicians in assessing embryo viability, while a poster from last years annual meeting of preliminary research into predicting embryo ploidy showed that the algorithm tended to classify embryos as aneuploid.(1,2)

Adding to this evidence base, a new large prospective study has now found that algorithms are able to forecast the probability of conception among couples trying to get pregnant if given a wide range of data on predictors of fecundability (defined as the per-cycle probability of conception).(3) Based on a study participation cohort of more than 4000 women, results showed an overall discrimination performance of around 70% for six different supervised machine-learning algorithms in distinguishing between women who were likely to conceive and those who were not.

It was an outcome which, the authors say, exceeds results from predictive models in previous studies and demonstrates that such models can be created with reasonable discrimination using self-reported data. They add that this is in the absence of more detailed medical information such as laboratory or imaging tests.

Earlier work in this area has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in sub-fertile populations but with limited power and using little or no data on lifestyle, environmental and sociodemographic factors. In contrast, a total of 163 predictors of fecundability were considered in this new study to anticipate the cumulative likelihood of pregnancy over six and 12 menstrual cycles.

The data were based on 4133 women from the ongoing Pregnancy Study Online (PRESTO), a web-based preconception cohort study which is analysing the impact of environmental and behavioural factors on fertility and pregnancy. Participants in the study were aged 2144 years, from the US or Canada, were not using fertility treatment, reported no more than one menstrual cycle of pregnancy attempt at study entry, and were actively trying to conceive at enrolment (20132019).

The female patients completed extensive questionnaires at enrolment (eg, marital status, reproductive and diet history, male partner characteristics, etc). Some of this information (eg, menstrual cycle dates) was updated via follow-up questionnaires completed bimonthly for 12 months, or until conception/cessation of pregnancy attempts or study withdrawal.

Next, the data were used to develop models to predict the probability of pregnancy. These were based on three time periods: pregnancy in fewer than 12 menstrual cycles (model I, n = 3195); pregnancy within six menstrual cycles (model II, n = 3476); and the average probability of pregnancy per menstrual cycle (model III, n = 4133). Additional models were also developed for women (n = 1957) who had never been pregnant but had no history of infertility: pregnancy in fewer than 12 menstrual cycles (model IV); pregnancy within six menstrual cycles (model V); and predicting fecundability (model VI). Six different supervised machine learning algorithms were then applied to each model to establish how each algorithm performed.

Results showed 86% of women in model I became pregnant and 69% in model II within the timeframes. For all six algorithms, the AUC (for prediction accuracy) was as follows: model I 68-70% (SD: 0.8%-1.9%); model II 65-66% (SD: 1.9%-2.6%); model III (63%); model IV 69.5% (SD: 1.4%); model V 65.6% (SD: 2.9); and model VI 60.2% concordant index.

Female age, female BMI and history of infertility were the predictors inversely associated with pregnancy in all models. The predictors positively associated with pregnancy in the first three models were having previously breastfed an infant and using multivitamins or folic acid supplements. Among the nulligravid women, the most important predictors were female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.

The authors conclude that the findings are especially relevant for couples planning a pregnancy and for clinicians caring for women coming off contraception to have a baby. However, they add that the models do need to be validated in external populations before they can become a counselling tool.

1. VerMilyea M, Hall J, Diakiw S, at al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Human doi: 10.1093/humrep/deaa0132. Aparicio Ruiz B, Bori L, Paya E, et al. Applying artificial intelligence for ploidy prediction: The concentration of IL-6 in spent culture medium, blastocyst morphological grade and embryo morphokinetics as variables under consideration. Human Reprod 2021; doi.org/10.1093/humrep/deab127.0663. Yland J, Wang T, Zad Z, et al. Predictive models of pregnancy based on data from a preconception cohort study. Human Reprod 2022; 1-13; doi.org/10.1093/humrep/deab280

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The algorithm will see you now: artificial intelligence in the prediction of pregnancy - ESHRE