Daily Archives: January 1, 2021

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

While 84 percent of global business organizations believe that AI can give them a competitive advantage and help recover faster from COVID-19, fewer than 23 percent of these firms have implemented this type of technology within their organization to date.

NEW YORK (PRWEB) December 31, 2020

Mediaplanet today announces the launch of this Decembers edition of Business Artificial Intelligence. This campaign spotlights how artificial intelligence (AI) is making it possible for businesses to address specific challenges during the ongoing pandemic.

Over the past nine months, the COVID-19 pandemic has brought multiple changes to our lives, including our working lives. While 84 percent of global business organizations believe that AI can give them a competitive advantage and help recover faster from COVID-19, fewer than 23 percent of these firms have implemented this type of technology within their organization to date.

To address this discrepancy, this campaign serves as an educational guidebook for the modern IT and business decision maker. Bringing together leading associations, thought leaders, and influencers from Americas core economic sectors who look to improve readers understanding of what AI is best suited to accomplish and how to deploy it for the most effective post-COVID-19 recovery of their organization. AI-driven technologies are empowering organizations to better navigate these unprecedented times and more effectively increase efficiencies, augment human labor, improve customer experiences, grow their bottom line, and provide firms with a clear advantage over competitors.

The print component of Business Artificial Intelligence is distributed within todays edition of USA TODAY, with a circulation of approximately 200,000 copies and an estimated readership of 600,000. The digital component is distributed nationally, through a vast social media strategy, and across a network of top news sites and partner outlets. To explore the digital version of the campaign, visit: https://www.futureofbusinessandtech.com/campaign/business-ai/

This campaign was made possible with the support of NICE, Steve Wozniak, Sprinklr, Kevin O'Leary, SOCAP International, Customer Experience Professionals Association, North American Association of Sales Engineers, Conversica, Khoros, GEP, Blue Prism, American Society of Mechanical Egineers, Kirk Borne, Institute of Business Forecasting, Association of Supply Chain Management, Council of Supply Chain Management Professionals, Reverse Logistics Institute, Michelle Meyer, Jim Tompkins, IEEE Computation Intelligence Society, Institute for Digital Transformation, Ai4All, Marketing AI Institute, AA-ISP, National Retail Federation, Steve Escaravage, Booz Allen Hamilton and GEP.

About Mediaplanet Mediaplanet specializes in the creation of content marketing campaigns covering a variety of industries. We tell meaningful stories that educate our audience and position our clients as solution providers. Our unique ability to pair the right leaders with the right readers, through the right platforms, has made Mediaplanet a global content marketing powerhouse. Our award-winning stories have won the hearts of countless readers while serving as a valuable platform for brands and their missions. Just call us storytellers with a purpose. Please visit http://www.mediaplanet.com for more on who we are and what we do.

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Mediaplanet Outlines the Potential of Artificial Intelligence in Economic Recovery in New Business AI Campaign - PR Web

Artificial Intelligence

As we wrap up the year, Ill start by pointing out something obvious to anyone who reads my column: Ive rarely mentioned the Covid-19 pandemic. While it has had a major impact on many areas of society, I dont feel that it has changed the artificial intelligence (AI) and machine learning (ML) markets in a significant way. At most, it has accelerated the adoption of the technology, but it hasnt done much. While I hope everyone remains as safe as possible, it is not something in my coverage area. My best wishes to all readers remain.

At the end of 2019, my similar column mentioned retail uses of vision and chatbots being more integrated into other applications during 2020. That happened and chatbots have now become a must have in most customer interaction interfaces. That has meant a more minimal coverage this year. Retail use of AI vision continues to expand, but in the opposite way of the acceleration mentioned in the first paragraph. The pandemic caused slowdown in retail means spending has slowed while survival is at stake. I expect to see that pick up again in the second half of 2021, as the vaccines become more widespread and consumer confidence improves.

What was exciting about 2020 was the clear evidence of AI tools moving out of the standard worlds of core enterprise data analysis, marketing, retail vision and facial recognition. Two areas discussed in multiple columns were in infrastructure.

The first of those areas is in the cable industry. The large ISPs are focused on better analyzing and optimizing their networks. Smaller companies have come along and are helping the large firms by beginning to use AI at the edge, enhancing modems and routers to better analyze where problems are originating in households. That will improve the experience both for households and the internet providers.

The second was a bundle of segments around facilities. AI is being used to enhance everything from planning through construction and maintenance. One of the more intriguing aspects was the use vision and ML to help businesses and government analyze physical structures in the real world. In the construction arena, AI vision and analysis are helping to better manage and schedule projects.

Discussions with a few vendors shows that artificial intelligence is also working its way into different areas of the sales process. This is a slower change, only adding a bit more accuracy to sales systems in enterprise sales. That will continue, but the real advances I see are coming from the more commoditized markets. Earlier this month, I described the complexity of the commodity channels and sales policies. In this area, I see more visible and rapid adoption of AI to better optimize the channels, while enterprise sales will see a more steady, gradual adoption that is still important.

When looking to 2020 and the future, I still see governments moving slowly. Thats not a surprise. The USA, plenty of other nations, and the EU, have been putting out policy statements, but thats really all there is. A few statements by people in Congress have shown an increasing interest in the subject, and the Bipartisan Policy Center is looking at it, but still having an Inside the Beltway view of the issue.

Departments are beginning to look at the issue. As healthcare has been an early adopter of AI, especially in radiology, the FDA has begun to look at the issue. A number companies mentioned that the FDA is looking at how to adapt policies to manage changing algorithms, as usual the organization itself isnt forthcoming. Talking to anyone in the FDA involved in defining the process was not possible, all that was sent to me was vague statements. Seeing how that evolves in the coming year will be interesting.

On the other hand, NGOs are also busy pushing out opinions, policy statements and books. In 2020, Ive discussed information from the Brookings Institution and the World Economic Forum (WEF). Brookings has some interesting things going on and Ill continue to watch the evolution of their AI understanding and views. The WEF, being a business group, has almost completely avoided mentioning governments at all. Theyve put out general statements about business ethics with AI, but the regulatory environment will be changing. I dont expect to see anything significant next year, but I would hope theres momentum building for action in 2022.

I have constantly returned to the refrain that AI is not a panacea, its a tool. One thing preventing wider adoption of the tool is that development software is moving more slowly than I hoped. There have been some user interface changes, with the basics of a graphical user interface (GUI) being layered on some aspects of the development cycle, but theres still a long way to go.

There needs to be a change similar to that between Third Generation programming languages to Fourth Generation languages, where coding was minimized, and graphical tools made it easier for less technical personnel to build applications supporting their business needs.

One trend I see helping that is that the importance of data privacy has filtered down to the developers. Data cleansing has usually been, sadly, an afterthought. As it becomes more important, and ML uses larger bodies of data, the need to quickly manage that data will drive UI changes. That will hopefully bubble up from managing privacy, to analyzing features for selection, to higher level management of the development process.

Frameworks, such as TensorFlow, as still the main tool for developing ML engines. That requires both more time and more money, as they are complex and require more knowledge and a higher price tag for programmers. While schools are working to meet the demand, theres also a need to increase the supply by providing more abstract tools that allow more people to leverage the power and promise of AI/ML.

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The Obligatory Artificial Intelligence Year End Article, 2020 Edition - Forbes

Artificial Intelligence (AI) is influencing the future of virtually every industry and each person on the planet. Artificial intelligence has been set up as the primary driver of growing technologies, for example, robotics, big data, and the Internet of Things (IoT). Moving into 2021, Artificial Intelligence will keep on going about as a principle technological pioneer for years to come.

Artificial intelligence is a highlight of the coming new normal in our entire lives. Going ahead, AI will be the intelligent core of robotic, automated, and contactless procedures that will shield us all from future outbreaks.

As indicated by the first published World Intellectual Property Organization WIPO report monitoring the advancement of technologies through the analysis of information on innovation activities, we can see drifts in patenting of Artificial Intelligence (AI) innovations, the top players in AI from industry and academia, and the geological dispersion of AI-related patent protection and scientific publications. The first of a series of WIPO reports on AI and patent analysis was published in 2019 and remains significant in issues of AI patterns.

In 2021, the grittiest of organizations will push AI to new boondocks, for example, holographic meetings for telecommunication and on-demand, personalised manufacturing. They will gamify vital planning, incorporate simulations in the meeting room and move into intelligent edge experiences.

Combined with this, fortunate laggards will utilize no-code automated machine learning to execute five, 50, or 500 AI use cases quicker, leaving behind their rivals with proficient, entrenched data science teams that take a customary, code-first way to deal with machine learning.

According to Rohan Amin, the Chief Information Officer at Chase, In 2021, we will see more refined uses of machine learning and artificial intelligence across industries, including financial services. There will be more noteworthy incorporation of AI/ML models and abilities into numerous business operations and processes to drive improved insights and better serve clients.

Further, Authenticated AI is very crucial. In 2021, companies will execute facial recognition for solid authentication in a developing range of internal and customer-facing applications. By a similar token, business will progressively neglect to utilize the innovation to surmise personality, gender, race, and other characteristics that may be sensitive from a bias, privacy, and surveillance viewpoint. To the degree that organizations consolidate facial recognition in picture/video auto-tagging, query-by-image, and other such applications, it might be after broad review by legal counsel. The administrative affectability of this innovation and the legitimate dangers will just develop for the foreseeable future.

As per Forresters forecast, the year 2021 will feature the great, the terrible, and the ugly of artificial data, which comes in two structures: Synthetic information that permits clients to make data sets for training AI, and fake information that does the inverse; it annoys training data to purposely lose AI.

Organizations are confronting increasing pressure from consumer interest groups and controllers to give a data lineage for AI. This incorporates data review trails to guarantee consistency and moral utilization of AI. In 2021, Blockchain and AI will begin uniting all the more genuinely to foster data provenance, integrity, and usage.

According to Flavio Bonomi, the Board Advisor to Lynx Software Technologies, 2021 is where AI will get embedded into existing devices and make certain operations quicker and more precise as standard. Sensors would now be able to recognize any of the five senses (indeed, including smell) and we will see AI progressively applied to those. Examples incorporate the capacity to identify vibrations or unordinary noises in a plant that ensures maintenance is performed on hardware before malfunctioning. Not as attractive or as clear as a self-driving vehicle, yet pragmatic and with a measurable ROI.

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Artificial Intelligence in 2021: Endless Opportunities and Growth - Analytics Insight

What better time than now to unveil what to look out for in the world of AI and technology in 2021.

By Prithwis De

The year 2020 will be marked as an unprecedented year in history due to the adverse impact of coronavirus worldwide. This pandemic has started bringing extraordinary changes in some key areas. The trends of faster drug development, effective remote care, efficient supply chain, etc, will continue into 2021. Drone technology is already playing a vital role in delivering food and other essentials alongside relief activities.

With Covid-19 came a new concept of the Internet of Behaviour within organisations to track human behaviour in the work environment and trace any slack in maintaining guidelines. Now on, organisations are set to capture and combine behaviour-related data from different sources and use it. We can assertively say it will affect the way organisations interact with people, going forward. Students are experiencing distance learning, taking examinations under remotely-monitored and proctored surveillance systems through identity verification and authentication in real time.

All these will have a high impact on technology, which will shape our outlook in the future. Businesses around the globe are taking the giant leap to become tech-savvy with quantum computing, artificial intelligence (AI), cybersecurity, etc. AI and cloud computing are alluring us all towards an environment of efficiency, security, optimisation and confidence. What better time than now to unveil what to look out for in the world of AI and technology in 2021.

What 2020 has paved the way for is quantum computing. Now, be prepared to adapt to a hybrid computing approach (conventional cum quantum computing) to problem-solving. This paradigm shift in computing will result in the emergence of implausible ways to solve existing business problems and ideate new opportunities. Its effects will be visible on our ability to perform better in diverse areasfinancial forecasting, weather predictions, drug and vaccine development, blood-protein analysis, supply chain planning and optimisation, etc. Quantum Computing as a Service (QCaaS) will be a natural choice for organisations to plug into the experiments as we advance. Forward-thinking businesses are excited to take the quantum leap, but the transition is still in a nascent stage. This new year will be a crucial stepping stone towards the future of things to change in the following years.

Cloud providers such as Amazon (AWS), Microsoft (Azure) and Google will continue to hog the limelight as the AI tool providers for most companies leaning towards real-time experiments in their business processes in the months to follow. Efficiency, security and customisation are the advantages for which serverless and hybrid cloud computing are gaining firm ground with big enterprises. It will continue to do so in 2021.

Going forward, the aim is to make the black box of AI transparent with explainable AI. The lack of clarity hampers our ability to trust AI yet. Automated machine learning (AutoML), another crucial area, is likely to be very popular in the near future. One more trend that caught on like wildfire in 2020 is Machine Learning Operations (MLOps). It provides organisations visibility of their models and has become an efficient tool to steer clear of duplicated efforts in AI. Most of the companies have been graduating from AI experimentations and pilot projects to implementation. This endeavour is bound to grow further and enable AI experts to have more control over their work from end-to-end now onwards.

Cybersecurity will gain prime importance in 2021 and beyond as there is no doubt that hacking and cybercrime prevention are priorities for all businesses with sensitive data becoming easily accessible with advanced phishing tools. Advanced prediction algorithms, along with AI, will play a decisive role in the future to prevent such breaches in data security.

AI and the Internet of Things along with edge computing, which is data processing nearer the source closer to the device at the edge of the network, will usher in a new era for actionable insights from the vast amount of data. The in-memory-accelerated-real-time AI will be needed, particularly when 5G has started creating new opportunities for disruption.

In 2020, there was a dip in overall funding as the pandemic had badly impacted the investment sector due to a reduction in activity. Some of the technology start-ups are still unable to cope up with the challenges created due to Covid-19 and the consequent worsening economic conditions. According to NASSCOM, around 40% of Indian start-ups were forced to stop their operations. In 2021, mergers and acquisitions of start-ups are expected. The larger companies are likely to target smaller companies, specialised mainly in niche and innovative areas such as drug development, cybersecurity, AI chips, cloud computing, MLOps, etc.

The businesses in 2021 and beyond will develop into efficient workplaces for everybody who believes in the power of technology. It is important to bear in mind that all trends are not necessarily independent of each other, but rather form the support base of the other as well as work in tandem with human intervention. So, are the hybrid trends and solutions here to stay for the next few years for the smooth running of various organisations? Only time will tell. But the need for AI and newer technology adoption and modernisation increases manifold.

The author is an analytics and AI professional, based in London, working in a big IT company. Views are personal

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Excerpt from:

Tech trends in 2021: How artificial intelligence and technology will reshape businesses - The Financial Express

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings.

When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious narrow AI applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019).

Read the original:

Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next? - Journal of Petroleum Technology