Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.

Attendees were entertained and challenged as they explored the good and bad of computing, explained CSAIL Director Professor Daniela Rus, who organized the event with John Werner, an MIT fellow and managing director of Link Ventures; MIT sophomore Lucy Zhao; and grad student Jessica Karaguesian. As you listen to the talks today, Rus told the audience, consider how our world is made better by AI, and also our intrinsic responsibilities for ensuring that the technology is deployed for the greater good.

Rus mentioned some new capabilities that could be enabled by AI: an automated personal assistant that could monitor your sleep phases and wake you at the optimal time, as well as on-body sensors that monitor everything from your posture to your digestive system. Intelligent assistance can help empower and augment our lives. But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring, said Rus.

The next speaker, CSAIL principal investigator and professor of electrical engineering and computer science Manolis Kellis, started off by suggesting what sounded like an unattainable goal using AI to put an end to evolution as we know it. Looking at it from a computer science perspective, he said, what we call evolution is basically a brute force search. Youre just exploring all of the search space, creating billions of copies of every one of your programs, and just letting them fight against each other. This is just brutal. And its also completely slow. It took us billions of years to get here. Might it be possible, he asked, to speed up evolution and make it less messy?

The answer, Kellis said, is that we can do better, and that were already doing better: Were not killing people like Sparta used to, throwing the weaklings off the mountain. We are truly saving diversity.

Knowledge, moreover, is now being widely shared, passed on horizontally through accessible information sources, he noted, rather than vertically, from parent to offspring. I would like to argue that competition in the human species has been replaced by collaboration. Despite having a fixed cognitive hardware, we have software upgrades that are enabled by culture, by the 20 years that our children spend in school to fill their brains with everything that humanity has learned, regardless of which family came up with it. This is the secret of our great acceleration the fact that human advancement in recent centuries has vastly out-clipped evolutions sluggish pace.

The next step, Kellis said, is to harness insights about evolution in order to combat an individuals genetic susceptibility to disease. Our current approach is simply insufficient, he added. Were treating manifestations of disease, not the causes of disease. A key element in his labs ambitious strategy to transform medicine is to identify the causal pathways through which genetic predisposition manifests. Its only by understanding these pathways that we can truly manipulate disease causation and reverse the disease circuitry.

Kellis was followed by Aleksander Madry, MIT professor of electrical engineering and computer science and CSAIL principal investigator, who told the crowd, progress in AI is happening, and its happening fast. Computer programs can routinely beat humans in games like chess, poker, and Go. So should we be worried about AI surpassing humans?

Madry, for one, is not afraid or at least not yet. And some of that reassurance stems from research that has led him to the following conclusion: Despite its considerable success, AI, especially in the form of machine learning, is lazy. Think about being lazy as this kind of smart student who doesnt really want to study for an exam. Instead, what he does is just study all the past years exams and just look for patterns. Instead of trying to actually learn, he just tries to pass the test. And this is exactly the same way in which current AI is lazy.

A machine-learning model might recognize grazing sheep, for instance, simply by picking out pictures that have green grass in them. If a model is trained to identify fish from photos of anglers proudly displaying their catches, Madry explained, the model figures out that if theres a human holding something in the picture, I will just classify it as a fish. The consequences can be more serious for an AI model intended to pick out malignant tumors. If the model is trained on images containing rulers that indicate the size of tumors, the model may end up selecting only those photos that have rulers in them.

This leads to Madrys biggest concerns about AI in its present form. AI is beating us now, he noted. But the way it does it [involves] a little bit of cheating. He fears that we will apply AI in some way in which this mismatch between what the model actually does versus what we think it does will have some catastrophic consequences. People relying on AI, especially in potentially life-or-death situations, need to be much more mindful of its current limitations, Madry cautioned.

There were 10 speakers altogether, and the last to take the stage was MIT associate professor of electrical engineering and computer science and CSAIL principal investigator Marzyeh Ghassemi, who laid out her vision for how AI could best contribute to general health and well-being. But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

Its important to focus on the data, Ghassemi stressed, because these models are learning from us. Since our data is human-generated a neural network is learning how to practice from a doctor. But doctors are human, and humans make mistakes. And if a human makes a mistake, and we train an AI from that, the AI will, too. Garbage in, garbage out. But its not like the garbage is distributed equally.

She pointed out that many subgroups receive worse care from medical practitioners, and members of these subgroups die from certain conditions at disproportionately high rates. This is an area, Ghassemi said, where AI can actually help. This is something we can fix. Her group is developing machine-learning models that are robust, private, and fair. Whats holding them back is neither algorithms nor GPUs. Its data. Once we collect reliable data from diverse sources, Ghassemi added, we might start reaping the benefits that AI can bring to the realm of health care.

In addition to CSAIL speakers, there were talks from members across MITs Institute for Data,Systems, and Society; the MIT Mobility Initiative; the MIT Media Lab; and the SENSEableCity Lab.

The proceedings concluded on that hopeful note. Rus and Werner then thanked everyone for coming. Please continue to reflect about the good and bad of computing, Rus urged. And we look forward to seeing you back here in May for the next TEDxMIT event.

The exact theme of the spring 2022 gathering will have something to do with superpowers. But if Decembers mind-bending presentations were any indication the May offering is almost certain to give its attendees plenty to think about. And maybe provide the inspiration for a startup or two.

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The promise and pitfalls of artificial intelligence explored at TEDxMIT event - MIT News

Artificial intelligence (AI) technology has become a critical disruptor in almost every industry and banking is no exception. The introduction of AI in banking apps and services has made the sector more customer-centric and technologically relevant.

AI-based systems can help banks reduce costs by increasing productivity and making decisions based on information unfathomable to a human agent. Also, intelligent algorithms are able to spot anomalies and fraudulent information in a matter of seconds.

A report by Business Insider suggests that nearly 80% of banks are aware of the potential benefits that AI presents to their sector. Another report suggests that by 2023, banks are projected to save $447 billion by using AI apps.These numbers indicate that the banking and finance sector is swiftly moving towards AI to improve efficiency, service, productivity, and RoI and reduce costs.

In this article, we will find out the key applications of AI in the finance and banking sector and how this technology is redefining customer experience with its exceptional benefits.

Artificial intelligence technologies have become an integral part of the world we live in, and banks have started integrating these technologies into their products and services at scale to remain relevant.

Here are some major AI applications in the banking industry through which you can reap the numerous benefits of the technology. So, lets dive in!

Every day, huge quantities of digital transactions take place as users pay bills, withdraw money, deposit checks, and do a lot more via apps or online accounts. Thus, there is an increasing need for the banking sector to ramp up its cybersecurity and fraud detection efforts.

This is when artificial intelligence in banking comes to play. AI can help banks improve the security of online finance, track the loopholes in their systems, and minimize risks. AI along with machine learning can easily identify fraudulent activities and alert customers as well as banks.

For instance, Danske Bank, Denmarks largest bank, implemented a fraud detection algorithm to replace its old rules-based fraud detection system. This deep learning tool increased the banks fraud detection capability by 50% and reduced false positives by 60%. The system also automated a lot of crucial decisions while routing some cases to human analysts for further inspection.

AI can also help banks to manage cyber threats. In 2019, the financial sector accounted for 29% of all cyber attacks, making it the most-targeted industry. With the continuous monitoring capabilities of artificial intelligence in financial services, the banks can respond to potential cyberattacks before they affect employees, customers, or internal systems.

Undoubtedly, chatbots are one of the best examples of practical applications of artificial intelligence in banking. Once deployed, they can work 24*7, unlike humans who have fixed working hours.

Additionally, they keep on learning about the usage pattern of a particular customer. It helps them understand the requirements of a user in an efficient manner.

By integrating chatbots into banking apps, the banks can ensure that they are available for their customers round the clock. Moreover, by understanding customer behavior, chatbots are able to offer personalized customer support and recommend suitable financial services and products accordingly.

One of the best examples of AI chatbot in banking apps is Erica, a virtual assistant from the Bank of America. This AI chatbot can handle tasks like credit card debt reduction and card security updates. Erica managed over 50 million client requests in 2019.

Also Read: How much does it cost to develop a chatbot?

Banks have started incorporating AI-based systems to make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit history, credit scores, and customer references to determine the creditworthiness of an individual or company.

However, one cannot deny that these credit reporting systems are often riddled with errors, missing real-world transaction history, and misclassifying creditors.

An AI-based loan and credit system can look into the behavior and patterns of customers with limited credit history to determine their creditworthiness. Also, the system sends warnings to banks about specific behaviors that may increase the chances of default.

Artificial intelligence in financial services helps banks to process large volumes of data and predict the latest market trends, currencies, and stocks. Advanced machine learning techniques help evaluate market sentiments and suggest investment options.

AI for banking also suggests the best time to invest in stocks and warns when there is a potential risk. Due to its high data processing capacity, this emerging technology also helps speed up decision-making and makes trading convenient for both banks and their clients.

Banking and finance institutions record millions of transactions every single day. Since the volume of information generated is enormous, its collection and registration turn into an overwhelming task for employees. Structuring and recording such a huge amount of data without any error becomes impossible.

In such scenarios, AI-based innovative solutions can help in efficient data collection and analysis. This, in turn, improves the overall user experience. The information can also be used for detecting fraud or making credit decisions.

Customers are constantly looking for a better experience and convenience. For example, ATMs were a success because customers could avail essential services of depositing and withdrawing money even when banks were closed.

This level of convenience has only inspired more innovation. Customers can now open bank accounts from the comfort of their homes using their smartphones.

Integrating artificial intelligence in banking and finance services will further enhance consumer experience and increase the level of convenience for users. AI technology reduces the time taken to record Know Your Customer (KYC) information and eliminate errors. Additionally, new products and financial offers can be released on time.

Eligibility for cases such as applying for a personal loan or credit gets automated using AI, which means clients can eliminate the hassle of going through the entire process manually. In addition, AI-based software can reduce approval times for facilities such as loan disbursement.

AI banking also helps to accurately capture client information to set up accounts without any error, ensuring a smooth experience for the customers.

[Also Read: 5 ways Fintech industry is using AI to woo millennials]

External global factors such as currency fluctuations, natural disasters, or political unrest have serious impacts on banking and financial industries. During such volatile times, its crucial to take business decisions extra cautiously. AI-driven analytics can give a reasonably clear picture of what is to come and help you stay prepared and make timely decisions.

AI also helps find risky applications by evaluating the probability of a client failing to pay back a loan. It predicts this future behavior by analyzing past behavioral patterns and smartphone data.

Banking is one of the highly regulated sectors of the economy worldwide. Governments use their regulatory authority to ensure that banking customers are not using banks to perpetrate financial crimes and that banks have acceptable risk profiles to avoid large-scale defaults.

In most cases, banks maintain an internal compliance team to deal with these problems, but these processes take a lot more time and require huge investment when done manually. The compliance regulations are also subject to frequent change, and banks need to update their processes and workflows following these regulations constantly.

AI uses deep learning and NLP to read new compliance requirements for financial institutions and improve their decision-making process. Even though AI banking cant replace a compliance analyst, it can make their operations faster and efficient.

One of AIs most common use cases includes general-purpose semantic and natural language applications and broadly applied predictive analytics. AI can detect specific patterns and correlations in the data, which traditional technology could not previously detect.

These patterns could indicate untapped sales opportunities, cross-sell opportunities, or even metrics around operational data, leading to a direct revenue impact.

Robotic process automation (RPA) algorithms increase operational efficiency and accuracy and reduce costs by automating time-consuming repetitive tasks. This also allows users to focus on more complex processes requiring human involvement.

As of today, banking institutions successfully leverage RPA to boost transaction speed and increase efficiency. For example, JPMorgan Chases CoiN technology reviews documents and derives data from them much faster than humans can.

Now that we have seen how AI is used in banking, in this section, we will look into the steps that banks can take to adopt AI on a broad scale and evolve their processes while paying due attention to the four critical factors people, governance, process, and technology.

The AI implementation process starts with developing an enterprise-level AI strategy, keeping in mind the goals and values of the organization.

Its crucial to conduct internal market research to find gaps among the people and processes that AI technology can fill. Make sure that AI strategy complies with the industry standards and regulations. Banks can also evaluate the current international industry standards.

The final step in AI strategy formulation is to refine the internal practices and policies related to talent, data, infrastructure, and algorithms to provide clear directions and guidance for adopting AI across the banks various functional units.

The next step involves identifying the highest-value AI opportunities, aligning with the banks processes and strategies.

Banks must also evaluate the extent to which they need to implement AI banking solutions within their current or modified operational processes.

After identifying the potential AI and machine learning use cases in banking, the technology teams should run checks for testing feasibility. They must look into all aspects and identify the gaps for implementation. Based on their evaluation, they must select the most feasible cases.

The last step in the planning stage is to map out the AI talent. Banks require a number of experts, algorithm programmers, or data scientists to develop and implement AI solutions. If they lack in-house experts, they can outsource or collaborate with a technology provider.

After planning, the next step for banks is to execute. Before developing fully-fledged AI systems, they need to first build prototypes to understand the shortcomings of the technology. To test the prototypes, banks need to compile relevant data and feed it to the algorithm. The AI model trains and builds on this data; therefore, the data must be accurate.

Once the AI model is trained and ready, banks must test it to interpret the results. A trial like this will help the development team understand how the model will perform in the real world.

The last step is to deploy the trained model. Once deployed, production data starts pouring in. As more and more data starts coming in, banks can regularly improve and update the model.

The implementation of AI banking solutions requires continuous monitoring and calibration. Banks need to design a review cycle for monitoring and evaluating the functioning of the AI model comprehensively. This will, in turn, help banks in the management of cybersecurity threats and robust execution of operations.

The continuous flow of new data will affect the AI model at the operation stage. Therefore, banks should take appropriate measures to ensure the quality and fairness of the input data.

A few big banks have already started leveraging artificial intelligence technologies to improve their quality of service, detect fraud and cybersecurity threats, and enhance customer experience.

Here are a few real-world examples of banking institutions that have been utilizing AI to their full advantage.

JPMorgan Chase: Researchers at JPMorgan Chase have developed an early warning system using AI and deep learning techniques to detect malware, Trojans, and phishing campaigns. Researchers say it takes around 101 days for a Trojan to compromise company networks. The early warning system would provide ample warning before the actual attack takes place.

It can also send alerts to the banks cybersecurity team as hackers prepare to send malicious emails to employees to infect the network.

Capital One: Capital Ones Eno, the intelligent virtual assistant, is the best example of AI in personal banking. Besides Eno, Capital One is also using virtual card numbers to prevent credit card fraud. Meanwhile, they are working on computational creativity that trains computers to be creative and explainable.

Apart from commercial banks, a number of investment banks such as Goldman Sachs and Merrill Lynch have also integrated analytical AI-based tools in their routine operations. Many banks have also started utilizing Alphasense, an AI-based search engine, that uses natural language processing to discover market trends and analyze keyword searches.

Now that we have looked into the real-world examples of artificial intelligence in banking, lets dive into the challenges that exist for banks using this emerging technology.

The wide implementation of high-end technology like AI is not going to be without challenges. From the lack of credible and quality data to security issues, a number of challenges exist for the banks using AI technologies.

So, without further ado, lets take a look at them one-by-one:

To avoid calamities, banks should offer an appropriate level of explainability for all decisions and recommendations presented by AI models. Banks need to understand, validate, and explain how the model makes decisions.

As we can see AI and banking go hand-in-hand because of the multiple benefits that this technology offers. According to Forbes, 65% of senior financial management expects positive changes from the use of AI and machine learning in banking. Thus, all banking institutions must invest in AI solutions to offer novel experiences and excellent services to customers.

At Appinventiv, we work with banks and financial institutions on different custom AI and ML-based models that help in improving revenue, reducing costs, and mitigating risks in different departments.

In case you are also looking for AI development services, talk to our experts. We can help you create and implement a long-term AI in banking strategy and cater to your needs in the most tech-friendly and cost-effective manner.

Get in touch!

Sudeep Srivastava

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Key applications of artificial intelligence (AI) in banking and finance - Appinventiv

Artificial intelligence, or AI, is transforming the medical device industry today. As medical devices continue to incorporate artificial intelligence to perform or support medical applications, new regulations require AI-driven medical devices to comply with state-of-the-art requirements and provide objective evidence for repeatability and reliability. AI has the potential to improve patient outcomes as well as the productivity and efficiency of healthcare delivery. It can also improve the day-to-day lives of healthcare providers by allowing them to spend more time caring for patients, hence improving staff morale and retention. It may even accelerate the development of life-saving therapies. Simultaneously, concerns have been expressed about the influence AI may have on patients, practitioners and health systems, as well as its potential risks; ethical arguments have erupted about how AI and the data that supports it should be utilized.

Leading researchers and clinical faculty members presented 12 technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence within the next decade at the 2018 World Medical Innovation Forum on artificial intelligence, hosted by Partners Healthcare.

From real-time video from the interior of a refrigerator to automobiles that can identify when the driver is inattentive, smart gadgets are sweeping the consumer market. In the medical industry, smart gadgets are critical for monitoring patients in the ICU and elsewhere. Artificial intelligence can improve outcomes and minimize costs related to hospital-acquired diseases penalties by improving the capacity to predict deterioration, detect the development of sepsis, or detect the onset of complications.

Heart AttackWhen a section of the heart muscle doesnt get enough blood, it causes a heart attack, also known as a myocardial infarction. The longer the heart muscle goes without therapy to restore blood flow, the more damage it sustains. The most common cause of heart attack is coronary artery disease. A strong spasm, or abrupt contraction, of a coronary artery, which can block blood flow to the heart muscle, is a less common reason. Experts at the University of Oxford have used machine learning to create a fingerprint, or biomarker.

The fat radiomic profile reveals biological red flags in the blood arteries that feed blood to the heart, such as inflammation, scarring and vessel alterations, all of which are indicators of a potential heart attack. Another example is cardiovascular magnetic resonance (CMR): CMR is a scan that detects how much of a particular contrast agent the heart muscle picks up and evaluates blood flow to the heart; the stronger the blood flow, the less likely there will be blockages in the heart veins.

DiabetesUncontrolled diabetes causes diabetes mellitus, which can lead to multi-organ failure in individuals. As a result of improvements in machine learning and artificial intelligence, it is now possible to detect and diagnose diabetes in its early stages using an automated procedure that is more efficient than manual diagnosis. Image-based AI-assisted medical screening and diagnosis is presently in development. Diabetic retinopathy (DR), age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract, and retinal vein occlusion are among the disorders where this technique is now being applied in ophthalmology.

IDx-DR is the first AI algorithm authorized by the FDA for detecting DR in non-ophthalmic healthcare practitioners offices. The gadget is linked to a non-mydriatic retinal camera (Topcons TRC-NW400), which sends the pictures to a cloud-based server. Based on autonomous comparison with a huge collection of typical fundus photos, the server uses IDx-DR software and a deep-learning algorithm to discover retinal abnormalities compatible with DR. One of two outcomes is provided by the software: (1) Refer to an eyecare professional (ECP) if more than moderate DR is discovered; (2) If the results are negative for more than mild DR, rescreen in 12 months.

CancerOne of the most promising methods of cancer treatment is immunotherapy. Patients may be able to beat tough tumors by attacking them with the bodys immune system. Machine learning algorithms and their ability to synthesize extremely complex data may open up new avenues for tailoring drugs to a persons genetic composition.

Dermatology and OphthalmologyEvery year, the quality of cell phone cameras improves, and they can now create photos that can be analyzed by artificial intelligence systems. Two of the first specialties to gain from this trend are dermatology and ophthalmology. Researchers in the UK have even developed a gadget that analyzes photos of a childs face to detect developmental difficulties. The approach may detect discrete elements including a childs jawline, eye and nose placement, and other characteristics that could suggest a craniofacial aberration. The program can match everyday images to more than 90 illnesses, allowing doctors to make better-educated decisions.

Electronic health records are a gold mine of patient data, but doctors and engineers have battled to gather and analyze it in a way that is accurate, quick and reliable.

Due to data quality and integrity difficulties, as well as a tangle of data formats, structured and unstructured inputs, and missing data, understanding how to engage in meaningful risk stratification, predictive analytics and clinical decision support has been particularly difficult. From cellphones with step trackers to wearables that can detect a pulse around the clock, a significant amount of health-related data is generated on the road. Collecting and analyzing this information, as well as complementing it with the information provided by patients via apps and other home monitoring devices, can provide a unique perspective on individual and population health. Artificial intelligence will play a key role in deriving relevant insights from this massive and diverse data set.

The difficulty of interoperability and integration is one of the main traits that divide academic research from practical AI applications. The majority of research focuses on creating AI models that function with carefully vetted sets of health data. Data is complex, scattered, and difficult to access in real life. The absence of a sufficient data architecture is often the biggest impediment to integrating AI into current applications.

Machine learning and artificial intelligence research require high-quality reporting. The danger of bias and the possible value of prediction models can only be accurately appraised if all features of a diagnosis or prognosis model are fully and clearly reported. Machine learning studies should aim to follow best practice recommendations like the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), which is designed to help researchers report studies that develop, validate, or update a prediction model for diagnostic or prognostic purposes.

Artificial intelligence has great potential in medical science beyond what we can imagine, and the current applications are only the beginning. As briefly discussed, immunotherapy has significant promise in cancer treatment. As we know, cancer is a deadly disease that impacts vital parts of the body. Customizing diagnosis according to the patients genes is just outstanding. It is imminent that AI will help us in effectively diagnosing diseases, developing personalized medications for complex treatments, and much more.

In addition, many of the disease states discussed are a leading factor for a patients cause of death (i.e., heart attack). Not to forget the adverse events from diabetes are unimaginableincluding cardiovascular complications, kidney damage, eye damage. Retinal image analysis also helps diabetic patients, as it aids the doctor with fundus image analysis, which can more swiftly help determine the next stages in a patients therapy. Doctors would be able to attend to more patients who require treatment. Emerging healthcare technologies focus on minimizing eye specialist visits, lowering total treatment costs, and increasing the number of patients seen by each practitioner. AI can assist the healthcare provider in reaching his or her objective more effectively.

Although this technology helps in the healthcare industry, it should not be used to replace a clinician time. Advances in AI are bringing with them new possibilities for running and grading algorithms. But as stated, this is just the beginning era of Artificial Intelligence-Machine Learning in medical science. The more we focus on the improvement of data quality and automation in the analysis of medical data, the more the algorithms can assist us in identifying useful patterns patterns that can be used to make accurate, cost-effective judgments in complex procedures.

The Chinese government is investing heavily in the development of new technologies that leverage AI. This includes solutions to address the COVID-19 outbreak.

Companies developing technologies that integrate AI need to consider regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

The giant leap forward in virtual health is punctuating the need for reliable, clinically accurate technologies to advance how virtual medicine is delivered.

The application of AI in telehealth to allow doctors to make real-time, data-driven rich choices is a key component in generating a better patient experience and improved health outcomes.

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The Power of Artificial Intelligence in the Medical Field - MedTech Intelligence

Using finely tuned hardware, a specialized network, and large data storage, supercomputers have long been used for computationally intense projects that require large amounts of data processing. With the rise of artificial intelligence and machine learning, there is an increasing demand for these powerful computers and, as a result, processing power is rapidly increasing. As such, the growth of AI is inextricably linked to the growth in processing power of these high-performing devices.

Supercomputers arent new. The term appeared in the late 1920s and the CDC 6600 (released in 1964) is generally considered to be the first true supercomputer. Early supercomputers used only a few extremely powerful processors but, in the late 1990s, computer experts realized that stringing together thousands of off-the-shelf processors would yield the greatest processing power. Current state-of-the-art supercomputers have over 60,000 massively parallel processors to approach petaflop performance levels.

Mindful of the threats to security that are posed by supercomputers, a consortium of countries, including the United States, Germany, and South Korea, developed the Wassenaar Arrangement, which restricts the sale of, among other things, supercomputers that can be used for military purposes. Nonetheless, supercomputers can be found in most countries pursuing AI research.

As such, much of the development of AI is predicated on two pillars: technologies and human capital availability. Our prior reports for Brookings, How different countries view artificial intelligence and Analyzing artificial intelligence plans in 34 countries, detailed how countries are approaching national AI plans, and how to interpret those plans. In a follow-up piece, Winners and losers in the fulfillment of national artificial intelligence aspirations, we discussed how different countries were fulfilling their aspirations along technology-oriented and people-oriented dimensions. In our most recent post, The people dilemma: How human capital is driving or constraining the achievement of national AI strategies, we discussed the people dimension and so, in this piece, we will examine how each country is prepared to meet their AI objectives in the second pillarthe technology dimension.

In order to analyze each countrys technology preparedness, we assembled a country-level dataset containing: the number and size of supercomputers in each country, the amount of public and private spending on AI initiatives in each country, the number of AI startups in each country, and the number of AI patents and conference papers each countrys scholars produced. This resulted in ten distinct data elements.[1]

As with our previous analyses, we conducted a factor analysis to determine if any of the data elements were closely related. Closely related items can be mathematically combined into a composite factor, which aids in interpretation. In this factor analysis, two clear factors emerged. The first factor contained country ranks by theoretical peak computer performance, number of processing cores, number of supercomputers, and maximal LINPACK performance achieved; country ranks for the number of conference papers and journal papers; and the country rank for the number of patents. The second factor contained private and public investments in AI. One field, AI startups, was not closely associated with either factor and was dropped from further analysis.

It is clear that all of the fields in the first factor are either directly related to technology or its use in research. As a result, we name this factor Technology and Research. The second factor is solely focused on investments, and so we name this field Investments.

Figure 1 shows where a select group of countries sit along these sub-dimensions.

We interpret and name the quadrants as follows. The countries that are in the upper right-hand corner we dub Leaders; these have both a robust technology and research platform (factor one) and substantial public/private investments (factor two). Countries in the lower right quadrant we dub Technology Skilled. These countries have a strong current technology and research platform but are lacking strong public and private investments. Countries in the upper left quadrant we dub Funding Positioned, and are countries that have a strong funding stream but are behind in terms of technology and research. Finally, we dub the lower left quadrant Unprepared, which reflects countries that are both lacking in technology and research and are also lacking from a funding perspective.

The race for technology dominance is clearly a two-horse race between the U.S. (94th percentile for technology and research and 96th percentage for investment) and China (94th percentile for technology and research and 91st percentage for investments). While the U.S. holds a very slight lead overall, both countries are in the top three positions for every single one of our data elements. This is not surprising, as the size of the U.S. and Chinese economies (largest and second-largest respectively at $20 trillion and $15 trillion respectively) dwarf Japan, which is the third-largest economy ($4.9 trillion). As a result, we see no technology-centric hindrances for either country to continue to excel.

The United Kingdom (75th percentile in technology and research and 88th percentile in investments), France (75th percentile in technology and research and 81st percentile in investments), Japan (87th percentile in technology and research and 75th percentile in investments), and Germany (83rd percentile in technology and research and 68th percentile in investments) form a strong chase pack to the two leaders. Of the four countries, we view the United Kingdom as being in the strongest position to challenge the U.S. and China and this is based on their stronger investments in technology. We feel that these investments will allow them to close the gap more quickly than the other countries are capable of. However, we cannot ignore the fact that Japans economy is the largest of the four and this suggests that, if they decide to do so, they can quickly accelerate their efforts.

India (57th percentile in technology and research and 78th percentile in investments), Canada (68th percentile in technology and research and 60th percentile in investments), South Korea (71st percentile in technology and research and 60th percentile in investments), and Italy (71st percentile in technology and research and 60th percentile in investments) complete the Leaders quadrant. As with the United Kingdom, India is also well-positioned from a funding standpoint and should quickly separate itself from the other four countries.

Almost without exception, there is a strong relationship between the countrys economic size and its position in our quadrants. The U.S. is certainly leveraging its status as the worlds largest economy and is making dramatically larger technology investments than almost any other country; China is a close second. While we were concerned with the U.S. position from a people perspective, there are no similar concerns from a technology standpoint. America remains a world leader in digital innovation and supercomputers are no exception to that fact.

The uncomfortable reality for the U.S. is that its economic strength is very helpful to make the necessary technology infrastructure investments which are necessary but not sufficient to be successful in the pursuit of AI but this economic strength has little or no bearing on the other necessary element the ability to attract the necessary people to develop and implement its AI strategy. By contrast, China also has the economic strength for the necessary technology infrastructure investments but also has a sizeable population to provide the people power which is also necessary. In other words, China has both conditions necessary for AI success while the U.S. only has one of them. As such, China is currently in far better shape than the U.S. to achieve its AI goals and, without changes on the people front, the U.S. will fall increasingly far behind.

In our next post, we will exclusively focus on what the U.S. needs to do to improve its position and in our subsequent posts, we will examine different teaming strategies that leverage each countrys respective strengths.

[1]: These were: Rpeak (country rank by theoretical peak computer performance), Cores (country rank by number of processing cores), Count (country rank by number of supercomputers), Rmax (country rank by maximal LINPACK floating point calculation performance achieved), AI Startups (country rank for number of AI-based startups), Private Investment (country rate for private investments in AI), Public Investments (country rank for public investments in AI), AI Conference Papers (country rank for number of AI conference papers), AI Journal Papers (country rank for number of AI papers) and AI Patents (country rank for number of AI patents).

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How countries are leveraging computing power to achieve their national artificial intelligence strategies - Brookings Institution

DUBLIN--(BUSINESS WIRE)--The "Growth Opportunities for Global Artificial Intelligence in Automotive" report has been added to ResearchAndMarkets.com's offering.

This research service examines the role artificial intelligence (AI) will play in the transformation of the automotive space. AI is a key disruptive technology, wherein automakers are evolving into technology firms and expanding their service offerings beyond manufacturing vehicles.

Technology implementation has increased, and the post-pandemic situation appears to be positive for all stakeholders; however, automakers have yet to fully harness AI's potential in their service offerings. Although AI is in the nascent stage of development, OEMs are adopting it across the automotive value chain to improve manufacturing and to enhance customer experience, marketing, sales, and after-sales services.

This report examines use cases and business opportunity areas for various players in the automotive ecosystem, including OEMs, Tier I suppliers and technology service providers, and new entrants or start-ups. As the industry continues to evolve, AI capabilities will become the core of automotive solutions.

The study identifies key AI trends impacting the industry, including the convergence of connectivity, autonomous, sharing/subscription, and electrification (CASE); the increasing use of digital assistants; and the emergence of cloud and data analytics. Discussion covers the adoption of various AI automotive industry elements and lists companies to watch out for in this space.

Additionally, this report guides market participants on how to chart their strategic priorities, such as partnerships, acquisitions, and new capabilities built to capitalize on growth opportunities in the automotive AI space. In conclusion, top growth opportunities are mapped out for automotive OEMs, Tier I suppliers, and technology solution providers.

Key Topics Covered:

1. Strategic Imperatives

2. Growth Dynamics - Drivers, Restraints, and Opportunities

3. Global OEM AI Roadmap - Introduction

4. Features Offered in Automotive AI

5. Application Areas for Automotive AI

6. Globally Launched New AI Features

7. Major Global Automotive AI Suppliers

8. Developments in AI-linked Products for Automotive

9. Opportunities Landscape

10. Growth Opportunity Universe

For more information about this report visit https://www.researchandmarkets.com/r/3w0rbg

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Growth Opportunities for Global Artificial Intelligence in the Automotive Industry - ResearchAndMarkets.com - Business Wire

AI is being a fast-paced technology and ruling the world with its different applications

The role of AI has modified considerably from its preliminary creation on the threshold of an enterprise of their innovation labs to the modern-day while human beings are starting to recognize that it has the ability to convert businesses from the center out.

According to the reports, AI will be better than human beings in translating languages by 2024, promoting items by 2031, and conducting surgical procedures by 2053.

Meanwhile, lets see the changes that AI brings in 2022:

Machine Learning (ML) is an application of artificial intelligence that gives systems the capacity to automatically analyze and enhance from experience without being explicitly programmed. ML specializes in the development of computer applications that may access information and use them to analyze for themselves. ML is the idea that computer software can learn and adapt to new information without human intervention. ML keeps a computers integrated algorithms up to date and permits the system to pick out data and built predictions around them. ML is beneficial in maintaining an enormous quantity of data and may be implemented in quite a few areas, together with investment, lending, setting up news, fraud detection, and more.

AI in defense and security are absolutely unlimited.AI is frequently embedded into weapons and surveillance systems to enhance performance. It regularly enhances target recognition, flight simulation and training, and risk monitoring.

Most importantly, the vital and volatile jobs of securing the borders of the country may be delegated to artificially smart robots, unmanned aircraft, drones, UAVs, etc. This might lessen the threat of life for the soldiers at the borders and offer better surveillance measures through the use of advanced Facial Recognition Technologies.

The future of classrooms is virtual. Already, there are lots of courses on platforms that can be distinctly informative and can be accessed from anywhere, anytime. AI can automate the excursion of administrative responsibilities for instructors and educational institutions.

Educators spend a lot of time grading exams, assessing homework, and imparting important responses to their students. AI is permitting the automation of categories and processing of paperwork. The idea of schooling may be redefined from the comfort of the homes, customized in line with each students

2022 is the year robot delivery ultimately takes off. Drones will be used for medical applications in towns handing over samples and reagents from hospitals to laboratories quicker than automobiles could drive throughout town. Detecting and deactivating bombs, working in environments wherein people cant survive, producing items or additives repetitively.

Businesses are using AI to enhance the productiveness of their employees. The advantage of AI for enterprise is that it handles repetitive tasks throughout an organization simply so that employees can focus on creative solutions, complicated problem solving, and impactful work. The idea of the workplace may also be redefined through the arrival of technology. The future work may be distinctly flexible. The concept of Work From Home may be the brand new norm and virtual meetings and conferences will be the normal practice. This might cause, the industrial real estate areas to witness a drop in their demands.

Artificial intelligence (AI) and self-driving automobiles are regularly complementary subjects in technology. Car producers everywhere in the globe are using artificial intelligence. AI and machine learning are being applied in how automobiles are built and the way they function on the road. There is an electric-powered automobile in each segment scooter, motorcycle (Revolt), hatchback (Mahindra e2O), compact sedan (Tata Tigor), compact SUV (Tata Nexon), SUV (MG ZS EV), and several high-end top-class services from Mercedes-Benz, Jaguar, Audi, and BMW.

AI now using its fast-paced different technologies such as ITMS, ATCS, LMT, Law Enforcement & many more in Traffic Management. The power of AI that propels quite a few information evaluations of these systems, is likewise what powers the navigation systems of ride-hailing in addition to last-mile delivery operators. Deliveries or order fulfillments that needed to be not on time in the past, can now be improved notably as towns retain taking over such shrewd visitors structures.

The metaverse is an immersive, virtual reality (VR) world that you enter through carrying VR goggles. It holds massive promise for quite a lot of sectors from purchasing to enterprise and the arena of work.

The metaverse describes an imaginative and prescient of a related to 3D digital international, wherein actual and virtual worlds are incorporated the use of technology including virtual reality (VR) and augmented reality (AR). There are numerous metaverses already as an instance in digital gaming platforms like The Sandbox and virtual worlds like Decentraland. AI has the ability to parse massive volumes of information at lightning pace to generate insights and drive action. Users can either leverage AI for decision-making (that is the case for maximum agency applications), or link AI with automation for low contact processes.

AI additionally helps such things as computer imagination and prescient and simultaneous location and mapping technology, which assist machines to recognize our bodily environment.

At the 2018 World Medical Innovation Forum (WMIF) on artificial intelligence presented by Partners Healthcare, main researchers and clinical faculty participants showcased the twelve technologies and areas of the healthcare enterprise which might be maximum in all likelihood to look a major effect from artificial intelligence. AI has made specific contributions in anticancer drug improvement and remedy. It can offer critical insights and data that cant be discovered through human identity and customize remedies for each cancer patient. It is assumed that AI could be an effective riding force for human most cancer studies and remedy in the future. We trust that AI will carry profound modifications to scientific technology in the future. AI can control the usage of chemotherapy drugs and expect the tolerance of chemotherapy drugs so that it will optimize the chemotherapy regimen. AI can assist medical doctors to make accurate remedy choices, lessen needless surgeries, and assist oncologists to enhance sufferers cancer remedy plans.

In a recent study, researchers from NYU and NYU Abu Dhabi (NYUAD) report that theyve evolved a unique artificial intelligence (AI) device that achieves radiologist-level accuracy in figuring out breast cancer in ultrasound images.

The consensus amongst many specialists is that some of the professions might be definitely computerized. A group of senior-level tech executives who contain the Forbes Technology Council named 13, which include insurance underwriting, warehouse, and production jobs, consumer service, studies and information entry, long haul trucking, and a fairly disconcertingly wide class titled Any Tasks That Can Be Learned. Accountants, manufacturing unit employees, truckers, paralegals, and radiologists simply to name a few.

Ex: Chatbots, AI-writers (such as Ryte), AI-Translators, AI-coders, etc.

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10 Ways Artificial Intelligence will Change the World in 2022 - Analytics Insight

Opinions expressed by Entrepreneur contributors are their own.

Artificial intelligence has the power to transform your business. It can be used for everything from customer service to sales, and it's easy to implement as well.Artificial intelligence is already being used in some industries, but it's just starting to become mainstream in small business.

Related:What Every Entrepreneur Must Know AboutArtificial Intelligence

We're about to look at someof the best ways that AI can benefit you as an entrepreneur. By taking advantage of these ideas, you'll find success when it comes to your business.

Artificial intelligence is a computer system that can perform tasks that are usually considered too complicated for humans. It's currently the most advanced technology, and it's being used in many different industries.

AI systems are able to learn independently from theirsurroundings, which is what makes them so valuable for your business. AI programs are set up to solve problems by gathering information or performing tasks on their own. They're capable of analyzing vast amounts of data quickly and making decisions based on patterns they've learned over time.

AI is an important tool for entrepreneurs especially in the digital age. As artificial intelligence becomes more prevalent, it will help you grow your business exponentially. Here are threeways that AI can help you succeed:

1. Customer service

One of the best ways to use AI in your small business is customer service. AI has the ability to quickly identify what customers are looking for and solve their problems efficiently. If your customer service reps are struggling to keep up with all of your customers' requests, artificial intelligence might be the answer.

2. Predictive analytics

An additionalbenefit of artificial intelligence is predictive analytics. It helps you predict what will happen next so you can make smart decisions about how tostrategicallyrun your business. You can also use predictive analytics for sales or marketing purposes, which makes it a valuable tool for any entrepreneur's arsenal.

3. Automated data entry

Another way that AI can help you run your business more efficiently is with automated data entry. Uploading data into a spreadsheet is tedious work. Tasks like this are often forgotten or delayed until they're needed at a later date when deadlines are looming.With automated data entry, there's no need to worry about forgetting to upload any columns of information.

There are many different types of AI tools, so it's important to find the one that best suits you. For example, if you're starting a new business, there are programs specifically for startups. Orif your company does not have a strong internet presence, you can take advantage of AI-powered chatbots for customer service purposes. They would be able to answer questions and provide information about your company to potential customers who contact you through social media or email.

There are programs that might help with customer service or sales, while otherswill help with web development or marketing automation. There are plenty of AI solutions that can be customized to your needs. It's just a matter of finding the right one.

Related:The Complete Guide to AI for Businesses and How It's Making a Difference

According to a recent survey by TechRepublic, the majority of businesses use AI for customer service. Retail and banking were also largeusers of artificial intelligence.

There are many different ways that AI can help your business, and those industries are actually just scratching the surface.Artificial intelligence is great for customer service because it allows agents to quickly deal with customers' problems. It helps out with everything from chat bots to voice commands.

It's also incredibly beneficial for marketing and sales. For example, marketers rely on AI to understand their prospects better and identify new leads that match their needs. Sales professionals often use it as a way to create complex deals without having negotiations with individual customers.

Sowhat are you waiting for? These uses for artificial intelligence can help you to take your business to the next level.

No matter what industry youre in, AI can help you to be more productive and efficient. And if youre looking for a solution to a problem with your business, AI just might have the answer.

Related:5 Ways AI Will Change the Digital Marketing Game in 2022

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3 Entrepreneurial Uses of Artificial Intelligence That Will Change Your Business - Entrepreneur

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Prosus (OTCPK:PROSF) reports a significant amount of cash in hand to acquire new targets. The company is using artificial intelligence capabilities to offer more personalized recommendations to users. Besides, PROSF's food delivery is increasing its offering by delivering groceries. With double-digit revenue growth and growing free cash flow, PROSF's DCF model results in a valuation of $155 per share. In my view, the current market price is a joke, and I will be buying shares.

Prosus is a holding that is investing in high-growth markets like online food delivery, fintech, and classifieds. Management finds target markets that are growing at a double-digit rate. Most of the business segments operated by Prosus report sales growth between 32% and 145%:

Source: IR

I believe that most analysts will be expecting significant revenue growth in the coming years. Note that the company has reported 53% sales growth y/y, and management is targeting an internal rate of return close to 22%.

I cannot really say whether Prosus will deliver the same results in the coming years. However, I believe that it is quite likely that sales growth continues at a double-digit rate. This is also the belief of many investment analysts.

Source: IR

I am also quite optimistic about the future transaction executed by Prosus. Note that the company makes on average close to $2.6 billion via acquisitions and investments. Given the increase in the net asset value of the portfolio, I believe that management is quite skilled in the M&A market. It knows how to identify growth as well as to sell companies at a decent multiple. In the future, I would expect this know-how to enhance the portfolio growth:

Source: IR

As of September 30, 2021, Prosus reported $6 billion in cash and $7 billion in short-term investments. I believe that the company has sufficient liquidity and financial shape to acquire new targets or make new investments.

Management also reported $3.8 billion in goodwill, which I appreciate for two reasons. First, it means that management has expertise in the acquisition of targets. We could expect more acquisitions in the future. Second, if management has successfully calculated the operating synergies, we could be expecting revenue and FCF growth.

Source: Quarterly report

I am not concerned about the current amount of leverage. The company reports financial debt of close to $10.5 billion, but the net debt is negative:

Source: Quarterly Report

I believe that Prosus will make tons of money once the company's food delivery business segment increases its offering. If the company is successful in bringing groceries to clients, the target market will most likely significantly increase:

Online grocery presents a large growth opportunity, where structural category dynamics are attractive (high frequency and average order value) but online penetration is low compared to other ecommerce categories. We have seen a significant switch over the past year, with the market's transition to online accelerated by the pandemic.

We will continue to grow our core food-delivery markets and build adjacencies - local food-service brands, grocery and convenience delivery, and more. Source: Annual Report

Regarding the company's classifieds business segment, I am quite optimistic about the new artificial intelligence training offered by Prosus. Management noted in the annual report that the company's machine learning capabilities enhanced personalized recommendations to users. In my opinion, if investments in AI technology continue, and it is also used in other companies owned by Prosus, revenue growth could be substantial:

During the year, our ML models had a substantial impact on our search, lead qualification, and trust and safety initiatives. This has enabled us to advance further in becoming a smart, convenient and trusted way for people to make big and small life choices. Personalised recommendations using item2vec technology enable our products to make 'smart' alternative suggestions to our users. Accurate job recommendations, as well as online price valuation in the motors category, are prime examples of offering transparency and peace of mind to our customers. Finally, automated content moderation keeps our platforms safe and trusted. Source: Annual Report

I made several assumptions about the future revenue breakdown. In 2022, I believe that the most relevant business segments will be online classifieds with 31% of total revenue, food delivery with 29% of the total amount of sales, payments, and fintech. I will also assume 2022 sales of $7.5 billion:

My Figures

I studied the growth of each target market to design my first DCF model. I assumed that the classifieds business segment will grow at a CAGR of 29% because the e-commerce market is expected to grow at that rate:

Global E-Commerce Market 2021-2025 The analyst has been monitoring the e-commerce market and it is poised to grow by $ 10. 87 trillion during 2021-2025, progressing at a CAGR of almost 29% during the forecast period. Source: The Global E-Commerce Market

I also assumed that the food delivery would grow at a CAGR of 10%, and the fintech's growth would stay close to 27.5%:

The global online food delivery services market size is expected to grow from $115.07 billion in 2020 to $126.91 billion in 2021 at a compound annual growth rate (CAGR) of 10.3%. Source: Online Food Delivery Services Market Growth To Accelerate

The "Global FinTech Market, By Technology, By Service, By Application, By Region, Competition Forecast & Opportunities, 2026" report has been added to ResearchAndMarkets.com's offering. The Global FinTech Market was valued at USD7301.78 billion in 2020 and is projected to grow at a CAGR of 26.87% during the forecast period. Source: Global FinTech Market Report 2021: Market was Valued at (globenewswire.com)

My results include 2032 sales of close to $58.85 billion, and FCF margin of close to 23%. I believe that my figures are close to the expectations of other analysts out there:

My Figures

Using the previous sales figures, I assumed an EBITDA margin of 5%-10%, which I believe is conservative in the tech sectors in which Prosus invests. I also added depreciation and amortization of $350-$600 million, and capital expenditures worth $200-$320 million. Finally, the resulting FCF/Sales would stand at 0.5%-7.5%, and 2032 FCF would stay close to $4.25 billion:

My Figures

In this case, including some of the associates in which Prosus invested is very relevant. The company owns 28% of Tencent (OTCPK:TCEHY), and 25.7% of VK (OTC:MLRYY). In total, I believe that these two investments are worth $167.814 billion:

Ycharts

Source: Vk

I took a look at some of the competitors and their EV/EBITDA multiples. Peers trade at 10x-41x FCF. However, I believe that Prosus could trade at much more than its peers because it holds investments in companies that are very young. Their business history will most likely be longer than that of competitors, so using a large EV/FCF makes sense:

Putting everything together, I used a WACC of 5%, which implied a 2032 terminal value of $205 billion, and a net present value of $9.75 billion. If we sum the stakes in Tencent and VK and divide by 15 billion shares, the target price stands at $25:

Source: My Results

In the best-case scenario, I will be assuming that Prosus will buy and invest in impressively profitable companies. Notice that Prosus usually targets an internal rate of return of 20% for early-stage partnerships. So, in this case, I would be expecting many of such successful deals:

For early-stage partnerships in new or high-growth markets, the risk profile is clearly high. In these cases, we look for venture-style IRRs well over 20%, understanding that operational success will determine the outcome. Source: Annual Report

I would also expect mergers among the company's owned by Prosus. Think about it. Prosus has many companies operating in the same business segment. In my view, if management decides to merge some teams, shareholders may benefit from certain operating synergies. As a result, we could see an increase in the FCF margins:

Source: Prosus

I would also include an increase in the company's net income thanks to transactions, sales, and other disposals. The company sells and buys companies on a daily basis. I am not thinking out of the box by assuming that more transaction could be very beneficial for Prosus. In this regard, let me mention one example of a transaction executed by Prosus. It is the merger between OLX and OfferUp, which resulted in a net gain of $114.8 million.

In July 2020, OLX merged its US letgo business with OfferUp, two of America's most popular apps to buy and sell in the US. OLX contributed its US letgo business. The total consideration was US$360.0m, including cash of US$100.0m. On disposal of the US letgo business, the group recognised a gain of US$114.8m in 'Net gains on acquisitions and disposals'. Source: Annual Report

Under this particular and a bit unlikely case scenario, I assumed that the classifieds business segment would grow at 35% y/y. I also believe that with sufficient M&A transactions, food delivery sales could reach 15% sales growth, and the fintech sales growth could reach 27.5%. In sum, 2032 sales would be equal to $75-$85 billion:

My Figures

I assumed an EBITDA margin of 10%, which is lower than the EBITDA margin reported by most competitors. With this figure, I obtained 2032 EBITDA of $7.5-$8.5 billion.

Source: Ycharts

If we also assume an EBIT margin of 8.5%, conservative D&A, capex, and changes in working capital, 2032 FCF would stand at $5.75 million:

My Figures

With the previous financial figures, I would expect significant optimism about Prosus. The cost of equity would most likely decline, so the WACC would be lower than that in the previous case scenario. My results include an implied market capitalization of $470-$475 billion, and an implied share price of $30:

My Results

Prosus invests mostly in online platforms, which may suffer considerable damage from new data regulations. As a result, the company may have to use more resources to protect the data of employees and clients. In this case scenario, I would expect a significant decline in the company's FCF margins:

Most of our businesses are subject to extensive laws and regulations: legal or regulatory developments, including changes in tax laws, may have an adverse impact on our businesses. A number of new laws and regulations around consumer protection and privacy have been passed globally. Source: Annual Report

In the past, management found investment opportunities at beneficial EV/FCF multiples. The future may not be that sweet. If the company does not identify attractive opportunities for whatever reason, the IRR would most likely decline. As a result, traders may sell shares, which may lead to an increase in the cost of equity and the WACC. In sum, we may see a significant decline in the company's valuation:

We may not find investment opportunities that fit our strategy and deliver an expected return more than our cost of capital. Portfolio risk may prove to be higher than we assumed to accept, which could negatively impact IRR and lead to a decline in the valuation of Prosus and/or Naspers. Source: Annual Report

Another clear risk comes from miscalculations of the valuation. If management pays too much for its acquired companies, accountants may have to impair the goodwill accumulated. In this detrimental case scenario, we may see a reduction in the book value per share and the expected FCF.

Prosus has a considerable amount of cash to invest in new targets. That's not all. In my view, if management continues to invest heavily in machine learning and AI technology, more users will visit the company's classifieds domains. I would also expect significant revenue growth from the offerings proposed by the food delivery business segment. Finally, if management also decides to merge some of its targets, FCF margins would also increase. Under the best conditions, I obtained a target price of $155. Traders are buying shares at less than $30, so I will be a buyer too.

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Prosus: New Artificial Intelligence Capabilities And Cheap - Seeking Alpha

DUBLIN--(BUSINESS WIRE)--The "Global Mobile Artificial Intelligence (AI) Market (2021-2026) by Technology Node, Product, and Application and Geography, Competitive Analysis and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Mobile Artificial Intelligence Market is estimated to be USD 7.2 Bn in 2021 and is expected to reach USD 22.08 Bn by 2026, growing at a CAGR of 25.12%.

Artificial Intelligence and Machine Learning are considered a part of the day-to-day operations of huge enterprises in various disciplines.

The worldwide mobile artificial intelligence (AI) market is expected to rise significantly during the projected period. Factors such as growing demands for artificial intelligence (AI) in smartphones, rising need for AI-capable processors, and rising investment in AI technologies fuel the growth of the mobile artificial intelligence (AI) market. Artificial intelligence technology is widely employed in various ground-breaking applications in manufacturing, automotive, and video games.

AI technology is now used in the smartphone business, and it extends well beyond applications such as digital assistants. Several smartphone makers are striving to create AI capabilities that meet the expectations of their customers. For the worldwide mobile artificial intelligence (AI) industry, more investment in inexpensive AI processors and R&D dedicated to camera applications is an opportunity.

The high cost of artificial processors, on the other hand, is a key stumbling block to the worldwide mobile artificial intelligence (AI) market's development.

The market is segmented further based on Technology Node, Product, and Application and Geography.

Countries Studied

Competitive Quadrant

The report includes a Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Why buy this report?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/cqjyuu

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Global Mobile Artificial Intelligence Market (2021 to 2026) - by Technology Node, Product, and Application and Geography - ResearchAndMarkets.com -...