Category Archives: Artificial Intelligence

VANCOUVER, British Columbia, Jan. 06, 2021 (GLOBE NEWSWIRE) -- WPD Pharmaceuticals Inc. (CSE: WBIO)(FSE: 8SV1) (the Company or WPD) a clinical-stage pharmaceutical company, is pleased to announce that it has engaged Image Analysis Group (IAG), a leading medical imaging company, to provide important critical imaging services during the Berubicin phase 2 clinical trials which are scheduled to start in February 2021.

IAG has deep expertise in partnering with global biotech companies to provide centralized reading and analysis of patient responses in real time. IAGs scientific and clinical imaging expertise in the field ofglioblastomamultiforme (GBM), coupled with IAGs proprietary Artificial Intelligence (AI) powered platform DYNAMIKA will allow WPD and its partners to review efficacy assessments, objective responses, and to thoroughly explore Berubicins effect in patients with GBM.

Advanced cancer therapies often lead to pseudo-progression, a local tissue reaction resulting from immune cell infiltration, causing inflammation, tumor necrosis and oedema which are often misinterpreted as tumor growth on traditional MRIs. Under the arrangement, IAG will utilize its advanced AI-driven methodologies that provide reliable early efficacy readouts. Pseudo-progression is difficult to distinguish from disease progression using routine clinical MRI assessments, and having IAGs advanced technology will be crucial for early detection and progression.

Mariusz Olejniczak, CEO of WPD commented, We are very pleased to partner with IAG to collaborate on the upcoming Berubicin trials and other studies including WPD101, if possible in the future. IAG has a strong track record of working with leading biotech companies to provide critical imaging collection, analysis, and assessment which is a very important part of our Berubicin phase 2 trials. We look forward to working with them leading up to and in preparation of the commencement of these Trials.

Olga Kubassova, CEO of IAG commented, We are pleased that IAGs unique focus on the use of quantitative imaging and AI will help accelerate WPDs development programs while reducing R&D costs, timelines, and uncertainties. This work will support the field of advanced cancer drug development as a whole and provide much needed treatment options to patients with glioblastoma.

Diana Dupont-Roettger, Chief Scientific Alliance Officer of IAG commented, The integration of advanced imaging and AI driven image analysis will prevent a false response assessment in patients experiencing pseudo-progression and thereby enhance the efficacy read out of Berubicin, avoid early patient drop-out and save costs. IAG is excited to collaborate with WPD and to support the clinical development with optimal imaging trial design, efficient imaging data management and response assessment.

About Image Analysis Group

Image Analysis Group (IAG) is a unique clinical development partner to life sciences companies. IAG broadly leverages its proprietary image analysis methodologies, power of our cloud platform DYNAMIKA, years of experience in AI and Machine Learning as well as bespoke co-development business models to ensure higher probability for promising therapeutics to reach the patients. IAGs independent Bio-Partnering division fuses risk-sharing business models and agile culture to accelerate novel drug development.

About WPD Pharmaceuticals

WPD is a biotechnology research and development company with a focus on oncology and virology, namely research and development of medicinal products involving biological compounds and small molecules. WPD has licensed in certain countries 10 novel drug candidates with 4 that are in clinical development stage. These drug candidates were researched at medical institutions, and WPD currently has ongoing collaborations with Wake Forest University and leading hospitals and academic centers in Poland.

WPD has entered into license agreements with Wake Forest University Health Sciences and sublicense agreements with Moleculin Biotech, Inc. and CNS Pharmaceuticals, Inc., respectively, each of which grant WPD an exclusive, royalty-bearing sublicense to certain technologies of the licensor. Such agreements provide WPD with certain research, development, manufacturing and sales rights, among other things. The sublicense territory from CNS Pharmaceuticals and Moleculin Biotech includes for most compounds 30 countries in Europe and Asia, including Russia.

On Behalf of the Board

Mariusz Olejniczak

Mariusz Olejniczak CEO, WDP Pharmaceuticals

Contact:

Investor Relations Email: investors@wpdpharmaceuticals.com Tel: 604-428-7050 Web: http://www.wpdpharmaceuticals.com

Cautionary Statements:

Neither the Canadian Securities Exchange nor the Investment Industry Regulatory Organization of Canada accepts responsibility for the adequacy or accuracy of this release.

This press release contains forward-looking statements. Forward-looking statements are statements that contemplate activities, events or developments that the Company anticipates will or may occur in the future. Forward-looking statements in this press release include that IAGs technology will enhance our analysis of the effects of Berubicin, that we may partner with IAG regarding our other compounds and that WPDs drugs could be developed into novel treatments for cancer. These forward-looking statements reflect the Companys current expectations based on information currently available to management and are subject to a number of risks and uncertainties that may cause outcomes to differ materially from those projected. Factors which may prevent the forward looking statement from being realized is that the IAG technology may not provide the benefits expected and we may not engage them further; competitors or others may successfully challenge a granted patent and the patent could be rendered void; that we are unable to raise sufficient funding for our research; that we may not meet the requirements to receive the grants awarded; that our drugs dont provide positive treatment, or if they do, the side effects are damaging; competitors may develop better or cheaper drugs; and we may be unable to obtain regulatory approval for any drugs we develop. Readers should refer to the risk disclosure included from time-to-time in the documents the Company files on SEDAR, available at http://www.sedar.com. Although the Company believes that the assumptions inherent in these forward-looking statements are reasonable, they are not guarantees of future performance and, accordingly, they should not be relied upon and there can be no assurance that any of them will prove to be accurate. Finally, these forward-looking statements are made as of the date of this press release and the Company assumes no obligation to update them except as required by applicable law.

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WPD Pharmaceuticals to Collaborate With IAG to Deploy Artificial Intelligence and Quantitative Imaging to Assess the Effects of Berubicin -...

One of the best-performing IPOs of 2020 was SaaS company C3.ai Inc (NASDAQ: AI). The stock priced at $42, above its initial range of $30 to $33, and closed at $161 in their first week of trading.

Growth Ahead: C3.ai is called a disruptive enterprise software vendor by Wedbush analyst Daniel Ives.

It's "the LeBron of AI setting up for massive growth," said Ives,who initiated the company with an Outperform rating and $200 price target.

C3.ai has a subscription model that could drive 30% revenue growth annually and strong gross margins, said KeyBanc analyst Michael Turits.

The analyst has a Sector Weight rating on shares of C3.ai based on valuation.

"AI is the new internet for the enterprises," said Piper Sandler analyst Arvind Ramnani, who rates the stock Overweight with a $166 price target.

Ramnani said the company's revenue and gross margins are supported by strong gross retention and partnerships that will drive sales.

Related Link: IPO Outlook For The Week: Airbnb, DoorDash Lead Highly Anticipated Week

Partnerships: The company's partnerships with Microsoft Corporation (NASDAQ: MSFT) and Baker Hughes (NASDAQ: BKR) area positive for Ives.

Turits mentioned a partnership with Baker Hughes and said it has a minimum value of $450 million for a five-year contract. Raytheon (NYSE: RTX)and Adobe (NYSE: ADBE) are were also mentioned.

"C3 has established lighthouse go-to-market and development partnerships with leaders in industries like energy (Baker Hughes), defense (Raytheon), financial services (FIS) and horizontal partnerships like CRM (Microsoft/Adobe)," the analyst said.

Analysts On C3.ai's Valuation: Wedbush Ives' price targetcomes from a valuation of 27x EV/revenue for fiscal year 2026.

Turits is cautious on C3.ai shares due to valuation. The analyst said three customers equal 44% of the company's revenue and has a relatively small number of customers.

"Despite its large, dynamic market opportunity, we view AI as fully valued at 82.2x EV/revenue (CY21E)," he said.

C3.ai is valued at 35x 2023 estimated revenue of $325 million by Ramnani.

Morgan Stanley analyst SanjitSingh initiatedshares with an Underweight rating and $100 price target. The analyst said there is "compelling growth ahead," but success is priced in.

AI Price Action: Shares of C3.ai were trading down 13.65% at $120.11 at last check Monday.

View More Analyst Ratings for AI View the Latest Analyst Ratings

2021 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

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Is C3.ai The 'LeBron' Of Artificial Intelligence? 4 Analyst Takes On Recent IPO - Benzinga

Artificial Intelligence or AI, which appears to be the newest and most exciting frontier in science and engineering, attempts to emulate humans in performing complex tasks by means of learning, adapting and automation. Alan Turing, who is widely considered to be the father of artificial intelligence, presented his ground-breaking Turing Test in 1950 setting a benchmark for AI. In order to be intelligent, a system needs to have certain capabilitiesincluding natural language processing, knowledge representation, automated reasoning, machine learning, computer vision, and robotics. It was proposed that these six technology branches would collectively form an AI and surprisingly, all six components remain relevant today even after 70 years.

Today we are at the crossroads of the Fourth Industrial Revolution and AI is gradually occupying the centre stage of this unprecedented transformation. It is no wonder that all the tech giants are now involved in a relentless race to surpass each other in acquiring superior AI capabilities, while their non-tech clients and partners are heavily embracing AI to sharpen their competitive edges. Harvard Business Review in one of its recent editions suggested that companies should look at AI through the lens of business capabilities rather than a mere collection of technologies. Thus, AI should be integrated as one of the core components of any complex business process instead of being categorised as a supporting tool. Specifically, AI can cater to three important business needs: process automation, extraction of cognitive insights, and cognitive engagement with clients and employees. Interestingly, AI applications are no longer restricted to corporations, but is gradually encompassing a variety of domains, from farming to manufacturing, hospitality to healthcare, public services to professional services, judiciary to defence and others. Innumerable examples of successful AI applications can be cited to demonstrate the and ubiquity of AI and its farreaching and profound impacts.

It is now obvious that AI has started to fundamentally disrupt the way we live and work. This will continue to happen in the foreseeable future with even greater speed, intensity and coverage. As AI penetrates every sphere of human life, it is imperative for us to formulate a roadmap to see the opportunities and identify its likely threats. Like any other technology, AI is neither an unmixed blessing nor a silver bullet for all our problems. Rather, AI poses some genuine concerns that must be addressed with appropriate strategies.

Unlike the preceding automation boom, AI is not only taking over the routine back-office roles or bluecollar jobs but also threatening to pervade the cognitive domain that has traditionally been under human control for thousands of years. Now the question arises, what approach is suitable for a developing country like Bangladesh? Should we turn a blind eye to AI? We can't afford to ignored it because the powerful AI-wave generated on both sides of the Atlantic is transcending continents and is not far from reaching Bangladesh. As a matter of fact, an AI-driven world is no more a matter of choice but an irreversible destiny that we are all heading towards. Nevertheless, we should not blindly follow other nations in adopting AI applications. Instead, we should frame policies and strategies which are commensurate with our own needs, necessities and priorities. For instance, we have a large young population and as such, we wouldn't want a big chunk of the available jobs to be taken over by AI. Hence, we should choose those problems for applying AI at the first phase that we haven't been able to solve with the traditional approaches and age-old systems. For instance, we are now facing serious problems of credit scam and cyber heist in our financial sector, corruption and inefficiency in public service delivery process, rampant violence against women, frequent violation of traffic rules and road accidents, wastage and pilferage in the utility delivery system and so on. Along with fixing some of these pressing problems of our time, AI can also be chosen to perform those tasks which involve a lot of risks and/or very high levels of precision such as firefighting, ship breaking, handling industrial chemical, predicting natural disasters, diagnosis of critical diseases and so on.

We must revisit our education system and reinvigorate it with soft skills such as critical thinking, creative problem-solving, leadership, collaboration and teamwork, social responsibilities, and ethics along with emphasis on science, technology, engineering and mathematics (STEM). In this way, we'll be able to convert our young population into skilled manpower, a critical input for realising our AI ambitions. Besides, we should also attempt to develop capability to serve the overseas markets, where AI is gaining ground very fast. Thus, we can diversify our heavily imbalanced export basket and realise our aspiration for economic prosperity. Countries like Taiwan and South Korea have leapfrogged as a developed nation through harnessing the semiconductor revolution, while countries like India and Philippines have enormously benefited from the business process outsourcing boom. Today, another revolution is beckoning that Bangladesh can attempt to exploit for its economic prosperity. If this train is missed, our path to prosperity might be harder and longer.

No matter what technology we opt to develop or which algorithm we choose to apply, fostering social welfare and upholding humanity through the sensible use of technology should be the ultimate goal. Again, no technological development should be allowed to widen the gap between the rich and the poor, or create any form of disparity or injustice. Therefore, a carefully crafted policy should be adopted and implemented to secure our future in the AI-driven global order.

Md Abdul Bari is a civil servant who is currently pursuing his Master's degree at the University of Queensland, Australia.

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Bangladesh in the age of Artificial Intelligence - The Daily Star

In order to solve social problems, Indian start-ups are growing and developing AI solutions in education, health, financial services, and other fields.

Artificial Intelligence (AI) is bringing a dramatic shift in the world of technology where it can be applied for more productivity and success in order to simplify the system. From just your cell phone to the diagnosis of diseases, AI is now being used in many fields, offering high-performance and precise device operation with quality. In every area and not just technology, it has proven to be a path-breaking technology.

As the fastest growing economy with the worlds second-largest population, India has a big stake in the AI revolution. The leading technology institutions in the country, such as IITs, NITs, and IIITs, have the ability to be the cradle of AI researchers and start-ups. In order to solve social problems, Indian start-ups are growing and developing AI solutions in education, health, financial services, and other fields.

For instance, the Deccan Article notes that the first Community Centre for Artificial Intelligence was launched in Hyderabad. They also mentioned that The HexArt Institute is an initiative of the Hexagon Capability Center India (HCCI), the greatest product development center of the technology major Hexagon AB. It recognizes this institution as an initiative of timely societal responsibility. The center will train over 350 students in several batches per year.

Some of the AIs main prospects in India are:

Digital assistants to be used by several highly advanced organizations to communicate with customers, saving the need for human resources.

Together with other innovations, organizations can use AI to make machines take decisions faster than an individual and perform actions faster.

In almost every area, AI powers several inventions that will help humans overcome the majority of complex issues.

Trade and Development agreement to operate together to leverage the power of cutting-edge technology to improve and expand trade, such as AI and blockchain.

Additionally, companies like Google, Microsoft, Amazon are trying to achieve the governments needs of cloud computing and machine learning. Private companies will rush to win big contracts, add to the stream of funds to create innovative technology, and establish new AI and data scientific startups as the Indian government pushes for digital transformation and introduces more AI initiatives.

However, some of AIs major adoption challenges are:

India has a comparatively small number of researchers in the field of machine learning and research production.

India has very little local awareness of the latest knowledge that is being generated by others each day.

Given the existing and potential possibilities, Indian businesses have been reluctant to accept AI.

Despite the number of available standard packages, India does not have sufficient qualified personnel to apply machine learning to its own challenges and data.

In its capacity to handle challenges, current AI strategies are minimal, and they will have to develop to deal with the complexity of life in India.

In general, Indias digital footprint has seen tremendous growth. The government is also moving different programs toward the objective of technical infrastructure. Different agencies and artificial intelligence institutes including sectors are developing policy structures and programs that instill such skills. With a little more drive towards resources and frameworks that boost its development, the Indian artificial intelligence market, which is still regarded as emerging, can certainly take a leap.

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Artificial Intelligence: Growth and Development in India - Analytics Insight

AN artificial intelligence app will enable people to talk to dead relatives using Alexa.

The ghost bot can generate 3D images of a person and mimic their personality.

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It uses recordings of them to copy their style, diction, tone, voice and intent to make what they say appear to be genuine.

Details from their social media accounts and letters could be mixed in to add to the effect.

App users could keep a loved-ones memory alive with catch-up chats.

It is believed the technology could be used to interact with historical figures or dead celebrities.

The patent states: Social data may be used to create or modify a special index ... to train a chat bot to converse and interact in the personality of a specific person.

It says a 2D or 3D model of a person could be generated using images, depth information, and/or video.

Microsoft has applied for a patent for the app, which could operate through smart devices like Amazon Alexa or Google Nest.

Some experts, however, consider the idea terrifying and have questioned the ethics behind the app.

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It comes after rapper Kanye West, 43, gave wife Kim Kardashian a hologram of her late dad Robert for her 40th birthday.

Lawyer Robert, who died of cancer in 2003 aged 59, was brought back to virtual life as a surprise.

In a clip online, the hologram is heard to state that Kim is all grown up and Kanye is a genius.

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Artificial intelligence app will use Alexa to enable people to talk to dead relatives - The Sun

Scale AI CEO Alexandr Wang doesnt need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.

The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.

In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scales visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.

Scale AIs customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.

Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.

Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.

Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.

A lot of the innovations that have happened within the self-driving industry, were starting to see trickle out throughout a lot of other robotics problems, Wang said. And so its been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.

The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.

Were finally getting to the point where we can help with computational AI, which has been this thing thats been pitched for forever, he said.

That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.

There was this big reckoning, Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.

If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, How do we start building for that?, according to Wang.

The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.

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How artificial intelligence will be used in 2021 - TechCrunch

The year 2020 was long and treacherous, but the biggest bright spot for me was the official launch of Neural. Thats our AI sub-brand here at TNW and the section youre reading this article in.

More specifically, Neural is me (Tristan Greene), Thomas Macaulay, Ivan Mehta, and the contributors and colleagues who help us put out fresh, original, exciting content in the world of machine learning every day.

It was a tough year to be a reporter but Thomas and Ivan managed to exceed our expectations at every turn with incredible insight and consistent excellence. With that in mind, Im proud to present some of my favorite articles from Tom and Ivan this year.

Between the two of them they covered some of the biggest events, breakthroughs, and stories in the world of machine learning and artificial intelligence. But, more importantly, they provided keen insight and analysis that you wont find anywhere else. And they also adhered to our biggest principal here at Neural: we cover AI for humans (not robots, businesses, or governments).

So, if youll indulge me, heres an Editors Choice list of just a few of the many articles my team published this year.

But first, heres my contribution:

Stories pictured above here and here.

And those are just a small sample of the wonderful work weve put out here at Neural. Check back in with us in 2021 where well continue to bring you news, analysis, and trusted opinions on the world of machine learning and its impact on humans.

Published December 29, 2020 22:00 UTC

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Heres what happened in the world of artificial intelligence in 2020 - The Next Web

In my previous column, I looked at the problem of artificial intelligences forcing hardware to consume too much power, which could lead to an unsustainable spike in demand at data centers in this country by 2025. To test out their appetites for more power, I employed several advanced artificial intelligences, and also their close cousins machine learning, cognitive computing, deep learning and advanced expert system technology. For that column, I only measured how much power they consumed, but my original intention was to actually test them out to show some innovative things the technology was accomplishing. I am circling back to that effort now.

For many years we have been reporting on the technology of artificial intelligence, about how its being built out and made more efficient, or how it can be paired with other technologies like quantum computing to become even more accurate. At the same time, the government has been keenly focused on AI ethics, ensuring that our newly created smart programs and machines dont go rogue or make mistakes that could get people hurt. The Defense Department now follows five ethical principles when using AI, while the intelligence community has its own artificial intelligence ethics guidelines.

There is still a lot of learning to do, but at this point we have pretty much covered the basics in terms of building out smart AIs and related technologies, power consumption issues aside. That is why we are starting to see a lot of interesting reports on projects that make use of AI, like figuring out which fishing boats out in the ocean are using forced labor or planning how we can safely get people to Mars.

These are some of the most interesting AIs that I have collected over the past few months, what they do and how well they perform.

COBOL Colleague

The federal government invested big in the COBOL programming language back in the day. It was designed for business, finance and administrative systems within both private companies and government organizations. Today, however, its not actively used for any new projects, having been replaced by more efficient languages. Most COBOL programming today is used to maintain existing systems written in the language that cant easily be replaced or recoded. The problem is that nobody is learning how to code in COBOL anymore, and most programmers that already know it have retired.

That is where Colorado-based startup Phase Change Software and their COBOL Colleague AI comes into play. Instead of trying to teach COBOL to modern programmers, it scans existing programs written in the language for vulnerabilities and problems, and zeros in on exactly what lines need to be fixed.

There is certainly a skills shortage, however, the real problem is that the knowledge of the application is disappearing, said Steve Brothers, COO of Phase Change Software.

Deploying an AI to look at code is almost like hiring a skilled human programmer. In the case of COBOL Colleague, it wont make changes to the code on its own but will show where any changes are needed. Then skilled programmers, even if they are not totally familiar with COBOL, can make the necessary fixes.

ToxMod

ToxMod is an interesting artificial intelligence made by Modulate Inc, a company that specializes in innovative AI. ToxMod is designed to regulate live comments in voice chat rooms and is able to distinguish subtle differences between, say, someone using an explicative in frustration, and someone using it as an attack or as part of a hateful tirade. To give ToxMod a real workout, its being deployed in the ultimate toxic environment, the chat rooms of video games.

Since ToxMod can differentiate something like honest frustration expressed in a toxic way from malicious intent, it can also advise matchmaking or reputation algorithms to improve the player experience, said Carter Huffman, Co-Founder and CTO of Modulate. Additionally, each games private ToxMod instances learn over time about their communitys specifics, on top of ToxMods universal core algorithms which evolve and improve automatically behind the scenes.

ToxMod can listen to and understand emotions, volume, inflections and other factors to determine if speech should be flagged. If hateful speech is detected, site moderators are alerted along with an audio clip to back up the AIs claim. This will let moderators check the AIs work while identifying bad actors and preemptively resolving a problem before it grows into something more serious.

The Test

This last one is mostly just for fun, but I was so impressed that I felt like I needed to include it here. The Test is, on the surface, a series of three games available for less than $2 each on the Steam gaming platform. The games are kind of bizarre in nature. Players sit in front of a demonic-like figure at his desk and are asked a series of very personal questions. The questions consist of typical personality type questions like If you found money on the street and knew who it belonged to, would you return it? But there are also a series of very strange scenarios and off-the-wall questions like if you were starving at home would you eat your pets, if pink is a prettier color than red, or if would you stop a zombie apocalypse if you could.

Behind the scenes, developer Randumb Studios is likely using an expert system as opposed to a true AI to track results and prepare advice for players. The questions were so strange that I really didnt think much of it, though I did try and answer honestly. But the final results, especially for the second game, really floored me.

It told me that I was working too hard and that I needed to take some time to recharge my batteries because I was not doing anyone any good if I was spread so thin that nobody was getting my best. It advised me to allocate two units of personal time for every one unit I spent working for others, which would be a key to both my happiness and success moving forward.

The strange thing is that I was really thinking about this exact same thing over the past two weeks, especially with the holidays approaching. I was worried about burnout and keeping a good work and life balance, and had spent quite a few evenings contemplating that exact topic. But I never told the game this, and dont see how its bizarre questions led it to that conclusion.

I guess that is the magic of expert systems. Its why those little 20-questions toys that ask you yes and no questions can always guess the song or the movie star that you are thinking about. But the leap that The Test made with me was a lot bigger than that. I still dont know how they did it, but am very impressed with the results.

The really amazing thing is that we are really just scratching the surface about what AI can do. In the near future, even the projects highlighted here will seem trivial compared to what is possible.

John Breeden II is an award-winning journalist and reviewer with over 20 years of experience covering technology. He is the CEO of the Tech Writers Bureau, a group that creates technological thought leadership content for organizations of all sizes. Twitter: @LabGuys

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Artificial Intelligence Begins to Realize Its Potential - Nextgov

C3.ai (NYSE:AI) was one of the hottest tech IPOs of 2020. The enterprise artificial intelligence company priced its IPO at $42 a share on Dec. 8, but the stock opened at $100 the following day and subsequently surged to about $140.

C3.ai raised $651 million in its IPO, and it now has a market cap of about $13.4 billion, or 85 times its fiscal 2020 revenue. That frothy valuation indicates investors are still thrilled about C3.ai's growth prospects -- but the bulls are ignoring some obvious weaknesses, and pricing too much growth into this high-flying stock.

C3.ai's founder and CEO is Thomas Siebel, who previously co-founded Siebel Systems, the enterprise software company Oracle (NYSE:ORCL) acquired for $5.85 billion in2006.

Image source: Getty Images.

Siebel founded C3.ai in 2009. The company initially offered its cloud-based AI tools to energy companies, but it now serves a wide range of organizations across the commercial, industrial, and government sectors.

C3.ai's top customers include the machinery maker Caterpillar, the oil and gas services giant Baker Hughes (NYSE:BKR), and the European energy company Engie (OTC:ENGIY). It notably generated 36% of its revenue from Baker Hughes and Engie in fiscal 2020, which ended in April.

These organizations all use C3.ai's software to streamline their operations, cut costs, and make data-driven decisions. Its software helps Caterpillar optimize its inventories, Baker Hughes streamline its maintenance routines, and Engie modernize its energy infrastructure.

C3.ai expands via a "lighthouse" strategy, in which it secures a top "lighthouse" customer in a sector to attract its industry peers. These lighthouse customers include 3M, Royal Dutch Shell, and the U.S. Air Force.

C3.ai generated 86% of its revenue from subscriptions and the rest from professional services last year. Its revenue rose 88% in 2018, 48% in 2019, and another 71% to $157 million in fiscal 2020. But in the first quarter of 2021, its revenue only rose 16% year over year to $40.5 million as COVID-19 disruptions throttled its growth.

Image source: Getty Images.

C3.ai says it generates "uncommonly high" contract values, thanks to the "high-value outcomes" its AI tools produce. As a result, its average contract was worth $12.1 million in fiscal 2020, which the company calls a "high-water mark for the applications software industry."

C3.ai tries to grow its revenue per customer with a "land and expand" strategy, wherein it locks in customers with a smaller contract, then signs them onto additional contracts. Its initial contract is worth about $13 million, but it believes it can boost that figure to $39 million via additional contracts. Its average contract lasts for about three years.

But like many other cloud service companies, C3.ai is unprofitable. Its net losses widened over the past three years, and it ended 2020 with a net loss of $69.4 million -- compared to a loss of $33.3 million in 2019. It generated a slim profit of $150,000 in the first quarter of 2021, due to lower operating costs during the pandemic, but it probably won't stay in the black for the rest of the year.

C3.ai's customer concentration is a major risk, and it could still face competition from public cloud leaders like Amazon (NASDAQ:AMZN) Web Services (AWS) and Microsoft (NASDAQ:MSFT) Azure, even though it classifies these tech giants as technological partners.

C3.ai's AI services run on top of AWS, Azure, and other cloud platforms -- but AWS and Azure also offer their own integrated AI services. C3.ai claims its services are cheaper, more efficient, and more customizable than those integrated AI solutions, but Amazon and Microsoft could still develop new AI services to compete against C3.ai in the future.

C3.ai has a promising business model, and it could have plenty of room to grow. It estimates the total addressable market for AI tools will grow from $174 billion in 2020 to $271 billion in 2024 -- and its "land and expand" strategy could boost the average values of its contracts as that market grows.

Unfortunately, C3.ai's stock is simply too hot to handle at 85 times last year's sales. Even if it doubles its revenue this year, it would still be pricier than other bubbly tech stocks like Palantir and JFrog -- which both trade at roughly 30 times next year's sales.

I'd consider buying C3.ai's stock if a market crash cuts its price in half, but there's far too much optimism baked in at these prices. The market's near-term momentum might carry it slightly higher, but I'm not interested in paying the wrong price for the right company.

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Here's Why This Hot Artificial Intelligence IPO Stock Isn't Worth Buying - Motley Fool

The usage of recommendation engines is growin in consumer services. Be it Spotify, Netflix, or Amazon; brands are leveraging artificial intelligence-based recommendation systems to provide more personalized services and enhance user experience. Apps like Google Maps and UBER also rely on AI to provide accurate directions and estimated travel time, respectively. So, it is obvious that many of us depend on AI to make numerous daily life decisions. However, will artificial intelligence-based recommendations pass the litmus test when fared against human recommendations, against the backdrop of concerns against it?

A person or machine makes a recommendation after learning their preferences. Basically, after filtering through varied information, suggestions are made by tailoring data to users interests, preferences, or behavioral history on an item.

There are several instances when artificial intelligence has been alleged as biased when making a suggestion or recommendation. For instance, few years ago, Reuters reported that the e-commerce giant Amazon.com Incssecret AI recruiting tool showed bias against women. The software penalized applicants who attended all-womens colleges, as well as any resumes that contained the word womens. Bias has also been observed in facial recognition algorithms that tend to misidentify people due to their gender or race. These biases may have existed because of the presence of bias in the training dataset or faulty programming. This also brings us to another major concern regarding artificial intelligence, i.e. the black box problem.

Though it is argued that AI-based decisions tend to be logical and adheres specified set of rules, we arent sure of how those decisions are made. Fortunately, to counter this, researchers have proposed Explainable AI (XAI), fine-tuning, unmasking AI, and more. Addressing the black box issue is important to understand the cause of the mistake or bias, or decision made by artificial intelligence models, boost transparency, and tweak it later. Recently researchers at Duke University proposed a method that targets reasoning process behind the AI predictions and recommendations.

When pitted against recommendations by humans, AI need not necessarily always have a win-win situation. It is true that data-driven recommendationsare always preferred; however, the preferences to accept humans and artificial intelligence based recommendations differ with respect to situation and use case. It all stems from the word-of-machine effect.

Recently, an article on When Do We Trust AIs Recommendations More Than Peoples? by University of Virginias Darden Business School Professor Luca Cian and Boston Universitys Questrom School of Business Professor Chiara Longoni, was published in the Harvard Business Review. In the article, they explained this phenomena asa widespread belief that AI systems are more competent than humans in dispensing advice when utilitarian qualities are desired and are less competent when the hedonic qualities are desired.

The article authors clarify that, it doesnt imply that artificial intelligence is competent than humans at assessing and evaluating hedonic attributes nor are humans in the case of utilitarian attributes. As per their experiment results, suppose someone is focused on utilitarian and functional qualities, from a marketers perspective, the word of a machine is more effective than the word of human recommenders. For someone focused on experiential and sensory qualities, human recommenders are more effective.

Out of one of the 10 studies by Cian and Longoni, one study involved recruiting 144 participants from the University of Virginia campus and informing them about testing chocolate-cake recipes for a local bakery. During the experiment, the participants were offered two options: one cake created with ingredients selected by an AI chocolatier and one created with ingredients selected by a human chocolatier. Both cakes were identical in appearance and ingredients. Participants were asked to eat the cakes and rate them on the basis of two experiential/sensory features (indulgent taste and aroma, pleasantness to the senses) and two utilitarian/functional attributes (beneficial chemical properties and healthiness). It was observed that while participants found the AI recommended cake less tasty than human recommended one, yet it was healthier than the other.

Longoni and Cian also assert that consumers will embrace artificial intelligence recommendations if they believe a human was part of the recommendation process.

The human brain has an edge over AI for its cognitive skills. It acquires knowledge and improves reasoning by learning from experience, abstract concepts, several cognitive processes, and more, its ability to manipulate ones environment. Whereas, artificial intelligence models try to mimic human intelligence by following certain program rules and continuous self-learning (machine learning). Regardless of their learning method, both are capable of giving good and bad recommendations. Second, people nowadays are slowly starting to trust AI. Independent surveys have found that people may opt for AI for higher flexibility and control. Respondents believe that the relationship between humans and AI and trust will likely improve in the future, given that AI proves itself safe, and transparent.

As recommendations become an effective marketing tool, developers and marketers have to be careful in leveraging artificial intelligence algorithms. They can program the AI system to identify what the customer is actually looking for, before making any suggestion. As AI becomes more tangible than ever, its ability to offer recommendations that are unique and personal in nature will increase too. It is true that currently, AI lacks quick thinking, creativity and other attributes associated with human intelligence, but with innovations around the clock, who knows what AI will be capable of in the future. At the same time, humans and AI can live in a symbiotic or collaborative relation to avail of each others benefits.

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

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Recommendations by Artificial Intelligence vs Humans: Who will win? - Analytics Insight