Daily Archives: August 15, 2022

Text-to-image AI systems are booming in both ability and popularity right now, and what better proof than their appearance in the worlds hottest app: TikTok.

The video platform recently added a new effect it calls AI greenscreen that allows users to type in a text prompt that the software will then generate as an image. This image can then be used as the background to a video potentially a very useful tool for creators.

The output of TikToks system is pretty basic compared to that of state-of-the-art text-to-image models like Googles Imagen, OpenAIs DALL-E 2, or Midjourneys eponymous software. It creates only rather abstract and swirling images; a strength reflected in the dreamy nature of TikToks suggested prompts like astronaut in the ocean and flower galaxy. Other models, by comparison, can produce both photorealistic imagery and complex and coherent illustrations that look like they were drawn or painted by humans.

The limitations of TikToks model may well be intentional, though. First, more advanced models require greater computing power, which would be expensive and resource-intensive for the company to implement. Secondly, TikTok has more than a billion users, and giving all these individuals the power to create photorealistic images of anything they can imagine would almost certainly produce some troubling results.

For example, we tested the models ability to create nudity and gore two types of output that text-to-image generators often try to limit. Pictures based on violent prompts like assassination of Boris Johnson and assassination of Joe Biden produce mostly abstract swirls, with a just-about-recognizable face for the UKs prime minister (though the mans familiar blond mop does makes caricature particularly easy).

Likewise, a request involving nudity naked model on beach produces thematically appropriate colors, including flesh-tones, sandy oranges, and ocean blues, but nothing that would make a vicar blush.

Whats notable about the appearance of TikToks AI greenscreen, then, is that it shows just how fast this technology is going mainstream. The latest cycle of development for text-to-image AI arguably began in 2021 with the original release of DALL-E by OpenAI. Less than two years later and the tech is already in the hands of millions via an app like TikTok.

Given the potential of these systems for both harm and good, things are only going to get stranger from here on in.

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TikTok now offers a very basic text-to-image AI generator directly in the app - The Verge

Anyone who has been following the news on AI in 2022 knows of the high rate of AI project failures. Somewhere between 60-80% of AI projects are failing according to different news sources, analysts, experts, and pundits. However, hidden among all that doom and gloom are the organizations who are succeeding. What are those 20%+ of organizations doing that are setting themselves apart from the failures, leading their projects to success?

Surprisingly, it has nothing to do with the people they hire or the technology or products they use. Indeed, many of the successful AI companies are using the same products and services from the same vendors that the companies with AI project failures are using. Likewise, the organizations with high rates of AI success dont have some magical team of data science or machine learning unicorns that somehow possess mysterious skills. Many of these successful AI organizations have the same skill sets that the average organization has. So if its not team and technology, what could it be?

Stop Treating your AI Projects like App Dev Projects

One of the biggest insights from these AI successes is that they dont see AI projects as application development or functionality-driven projects. Rather, they see them as data projects, or sometimes even data products. A data project doesnt start with an idea of what the functionality needs to be, but rather focuses on what insights or actions need to be gleaned from the data in whatever current shape its in.

It might seem somewhat obvious to many that AI projects are data projects, but perhaps the AI failures need to understand this at a greater level of detail. What makes an AI system work isnt specific code, but rather the data. The same algorithms with the same code can be used to generate text, recognize images, or have conversations the functionality is determined by the training data and the configuration of the system. Therefore, to achieve the desired outcome of an AI project requires focus on data iteration and data-centric methods versus coding-centric methods.

More specifically, the code for a facial recognition application doesnt actually do facial recognition, but rather the code just sets up the data to train the model and then execute the model once its trained. The data determines the functionality when it comes to AI and ML projects. So, if you are supposed to run AI projects as data projects, why are people still making the mistake of throwing developer-focused methods and approaches at what clearly doesnt have much to do with development?

Agile is Dead. Long live Agile.

The most popular methodology for application development is Agile, which focuses on short, iterative sprints tied to the immediate needs of the business user versus long development cycles. However, Agile falls flat when dealing with AI because it doesnt tell you how to deal with data, the core asset of an AI system.

Another approach is the Cross-Industry Standard Process for Data Mining (CRISP-DM), a decades old method that guides data mining efforts. However, its particularly focused on data projects and lacks some critical elements needed for AI projects, and hasnt been updated in over two decades. There have been other data-centric approaches, but they dont provide detail on how to run and manage data-centric projects, but they dont tell you how to deal with the specific requirements of AI model training and iteration, and they havent been built for Agile. This leads AI project managers to struggle with the right approach to run an AI project. No wonder so many AI organizations are making up their own approaches and failing so much at it.

CPMAI Methodology

CPMAI Methodology updates CRISP-DM with Agile and AI-specific details

The alternative to Agile is the waterfall methodology that has been around for decades. Like a waterfall, you can begin your project by designing your application, building your application, testing to make sure the application meets the criteria you built for and then deploying the application. The problem with waterfall is, especially for large and continuously changing projects, this process can take a very long time - sometimes upwards of 18-24 months. During this time the project requirements may change, new technology is created, or business needs may evolve past the original scope. This reality of waterfall is what led to the development of Agile as a more iterative approach. However, while Agile has been very successful for software development projects, if you try to use agile alone on data projects youre going to run into problems.

Take an AI-enabled chatbot for example. With each iteration the functionality doesnt change, its still a chatbot. The new iterations might change the number of words it can understand, add the ability to converse in new languages or increase the accuracy of the model but the functionality of the chatbot remains the same; it is still just a chatbot. Unlike with software projects where it may take twenty iterations to even get to the first functionality iteration of your model, AI projects have that functionality from the beginning. Therefore, you also need a data centric methodology to apply.

Taking the right approach to AI project Management

So if Agile doesnt work well, but we cant apply waterfall, and if CRISP-DM doesnt have what we need, what approaches are successful AI practitioners using? A hybrid of these approaches, of course! Agile and data centric methodologies do not compete, but rather they run on different timelines. The agile and data centric methodologies focus on different iterations. The data centric methodology focuses on the data, the agile methodology focuses on the functionality and they run together.

Agile doesnt tell you how to do things like data preparation, how to understand the data you have or need, how to build a model, retrain a model, and other critical functions of AI projects. This is why having a data centric methodology, going through specific steps in the correct order, and asking these questions in the beginning is essential. Approaches such as the Cognitive Project Management for AI (CPMAI) methodology are blending data-centric approaches with agile methods to produce methods that are more optimized for the highly data-centric, variable nature of AI projects.

Other project management methods have been tested in the space such as Microsofts Team Data Science Process (TDSP) and IBMs iteration on CRISP-DM. However, many organizations have been reluctant to adopt vendor-originated methodologies and turned to vendor-neutral approaches. Regardless of the approach used, whether its CPMAI, CRISP-DM with agile enhancements, TDSP, or others, what is setting apart the successes from the failures is, as Louis Armstrong used to say, its not what you do, its the way that you do it. Perhaps as these successes see more publicity, well see a resurgence in interest in methodology to drive AI projects forward with success.

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The One Practice That Is Separating The AI Successes From The Failures - Forbes

Nations are in a frantic race to attain true AI or known as Artificial General Intelligence (AGI).

The world is in a frantic race.

Geopolitical powers assert that the winner will take home all the bacon, as it were.

What race is being fiercely waged and strenuously pursued?

It is the AI race.

You could perhaps more aptly refer to this as the race to attain true Artificial Intelligence (AI), currently referred to more fully as Artificial General Intelligence (AGI). We want to somehow arrive at seemingly utmost AI or known as AGI that is comparable to human intelligence. We arent there yet. Indeed, despite all kinds of wild and brazenly proclaiming headlines, we do not know when or if we will achieve that high-bar mark. Todays AI is far less in capabilities than overarching human intelligence, though certainly there are lots of narrower ways in which AI has made impressive forays, such as being able to play top-notch world-class chess or do other relatively constrained tasks.

The golden ring though is the advent of AI that exhibits human intelligence of a devout nature and depth akin to that of humankind. This is the holy grail of AI researchers and practitioners. From time to time, there have been specious claims of already having crossed the AI race finish line, which Ive debunked in my column at the link here. Those that try to make finish-line crossing contentions are confounding the general public and at times do so by zealous innocence while at other times have seriously questionable motives in hand. All in all, this raises quite significant and vital AI Ethics considerations. For my ongoing and extensive coverage of AI Ethics and Ethical AI, see the link here and the link here, just to name a few.

Anyway, there is no doubt that a global-wide sprinting AI race is avidly underway. You would be hard-pressed to claim otherwise.

Think of it this way. If we had already managed to achieve true AI or AGI, the odds are that the AI footrace would have been formally and globally declared as successfully concluded. I assure you that worldwide attention would be riveted on such a resounding and earth-shattering breakthrough. You would know of it. We all would. The AI madcap dash would ergo effectively no longer exist, though perhaps a secondary version might occur involving those that hadnt attained true AI working feverishly to catch up. There is also the unsettling matter of how we will end up controlling or managing AGI if or when we get there.

No person or entity or nation can as yet properly claim the crown of producing true AI or AGI.

Meanwhile, a tremendous and unrelenting amount of handwringing is taking place about which nation (or nations) are at the head of the pack and who is tailing further behind. The assumption is that if you arent first, you will be left in the dirt. You will be eating the scraps leftover from the AI winners. You are potentially going to be forever subjugated to the nation or nations that make the heralded leap into true AI or AGI.

As a quick aside and to ease the wording of this discussion, I am going to henceforth herein use AGI whenever I am wanting to invoke the aura of true AI. The use of the somewhat newer phrase AGI is sometimes jarring to those that arent accustomed to seeing it used. We all are familiar with AI and you might be disturbed to see the acronym AGI being used instead. Allow me to explain why this is gradually emerging as a verbiage trend.

Part of the reason that AGI has risen in the arena of AI vernacular is that merely stating AI has now become a regrettably watered-down phrasing. No one knows if the AI that you are mentioning is the barely-AI variant or some quasi-better making-progress AI infusion, or might be alluding to the someday futuristic fully human-equated AI. To deal with the overloading of AI as a catchphrase, the AGI moniker has been gaining preference by those insiders within the AI field that want to specifically and particularly allude to true AI.

So, in short, consider my mentioning of AGI to be the same as saying true AI of the robust caliber akin to human intelligence, thanks.

Lets take a prudent deep breath and mindfully examine some facets of the race to attain AGI. There is even a meta-aspect that needs to be first stated. Be aware that there is a bit of cringy heartburn about using the allegory of a supposed footrace or some other kind of racing activity as a metaphor for the AGI race. Why so? Ill let you in momentarily on the complications and complexities of why (some say) a footrace or its equivalent is entirely misleading and an insidious simpleton viewpoint.

Here are the key points that Ill go over with you in this discourse:

You might want to fasten your seatbelt as I examine the urgently proceeding AGI footrace (yes, I dare to call it a footrace) that has nearly everyone moving at breakneck speed and seemingly skyrocketing ahead on this burning quest. Some might say that this is a race for the betterment of humanity, while others are forewarning that the race might spell utter doom for us all.

Time will tell.

If This Is A Race, The AGI Finish Line Seems Quite Ill-Defined

A finish line is usually a rather definitive demarcation. You either have reached the finish line or you have not. Coming up short doesnt seem to do you much good. Imagine an Olympics 400-meter dash and whether you would especially remember or would heap accolades on the runners that didnt finish the race at all (never having crossed the finish line). Unlikely.

Will we know when we have reached AGI such that it is reasonably all agreed that the finish line has been achieved?

There are heated disagreements about the demarcation of AGI.

For example, suppose that we devise Artificial Intelligence that seems entirely able to exhibit human intelligence, but there isnt a semblance of sentience therein per se (see my coverage about the arguments over AI sentience at the link here). The AI is computationally able to mimic or otherwise perform as human intelligence does. There though isnt a spark of aliveness or sentience that we associate with humans and other living creatures. Does this AGI count as reaching the goal that we thought we had?

Some would counterargue that it wouldnt matter if sentience per se seemed to be wrapped into this AGI. As long as it could exhibit human intelligence, the incorporation of sentience is something of a differing variety that we might or might not wish to see arise. Sentience in that sense of things is an add-on.

Others vehemently argue that the only means of attaining human intelligence in AGI will be to integrally embody sentience. AGI and sentience are either considered the same, or they are a mixture of an irreducible inseparable dual embodiment. To get AGI, you must have sentience, they would contend.

Setting aside that angle of the debate, another perspective is that we could use the famous Turing Test to assess whether AGI has been achieved. I have covered in-depth the Turing Test at the link here. In brief, the notion consists of having a human ask questions to the AGI and if the human cannot distinguish the AGI-generated replies from those of humankind then we would declare the AGI as being able to exhibit human intelligence.

There are lots of troubles or shortcomings often associated with the Turing Test.

Suppose that the human making the inquiries does a lousy job and fails to ask probing questions. One concern is that many of todays seeming powerful Large Language Models (LLMs) can parrot back to a human the content that the LLM was trained on (i.e., text and digital media often sourced via large-scale scraping of the Internet). As such, a particularly selected human asking questions of an ordinary sort that have already been answered and exist online can be potentially all readily answered by the LLM, but this is debatably not due to any human intelligence embodiment of an AGI caliber.

Lots of other qualms come up. Suppose the human is unable to comprehend the answers. Or suppose the human deludes themselves into believing that the answers are all exhibitory of human intelligence. Ive even covered the threadbare idea by some that all we need to do is ask the AI if it is AGI or is sentient, which I make clear is not a very convincing form of AGI proof, see the link here.

Finally, as one added thought, do we need to fully reach the finish line to consider that AGI has been reached?

I mentioned earlier that we usually forget about those that dont reach the finish line. This might not be sensibly analogous to the AGI race. I believe a compelling case can be made that if we are able to attain a substantial way toward AGI, we are already going to be finding ourselves in a state of amazement and either great benefit or great trouble. You see, many important and highly useful outcomes could arise by an almost-there AGI. Coming up short wont be so problematic as not finishing a 400-meter footrace, especially since it might be a vital foundation for making our way to the fuller version of AGI (possibly being a marathon in comparison).

The metaphor of AGI attainment being a type of footrace or its equivalent is at times an unsatisfactory and inadequate one.

The AGI Race Might Go To A Person, An Entity, Or A Nation

Some harbor the dreamy notion that AGI is going to be attained by some tinkerer working in the garage while in their pajamas and be an outcropping of crazily inventive computerization experiments that they have been toiling away on for years upon years. That is the classic high-tech trope of the lone wolf.

Sorry to report that this is an extremely low odds proposition.

The greater odds would be that an entity such as a business or some research team will be the AGI go-getters. A strong and prevailing belief is that it will take a village to arrive at AGI. The lone wolf wont have the resources nor the insights by themselves to reach AGI. They might contribute to the quest. They might provide needed pieces to the puzzle. They wont be capable of garnering the whole kit and caboodle.

Speaking of villages, another strongly held viewpoint is that it will be nations that are only able to attain AGI. Via a combination of the people, businesses, academics, and all other manners of entities within a nation-state, the AGI will arrive as a result of the combined work of the national totality. The unit level of attention for the winner in this race is on the nation-state basis, rather than on something more scattered, free-form or individualistic.

In short, if it takes a village, the village is going to be of a national scale, thus the nation-state will be the designated runner that crosses the finish line in the AGI race.

Metrics And How Nations Are Being Compared In The AGI Race

Take as a given that the AGI achievement is going to be based at the nation-state level.

To reiterate, we dont know that for sure, but it seems a reasoned assumption.

Consider the ramifications of the nation-state basis. Suppose a lone wolf does manage to get to AGI first and believes that their work is beyond that of the nation-state that they are a member of. This person proclaims they are not of any nation-state in terms of the crafted AGI. Would we still give credit to that nation-state and would the AGI be within the control and purview of that nation?

Envision another alternative that a large multi-national conglomerate arrives at AGI first. Which nation can say that the AGI is their thing to use and deploy (will it be construed as property or glean instead a variant of legal personhood)? Perhaps all of the nations that the company exists within are to get equal credit. Or maybe only wherever the formal headquarters is geographically placed. It could be a complex splitting of a veritable pot of gold.

In any case, the general popular opinion is that a nation-state is going to be the crucial determiner of attaining AGI. A nation that encourages AI research and development within its national efforts is going to presumably get to AGI sooner than other nations that dont do likewise.

There is a predominant view that the AI race is a national one.

A head-scratching issue is how are we to ascertain whether one nation is ahead of or behind another nation in the AGI race.

In a conventional footrace, we could easily identify metrics that can be used to determine which runners are doing well and which ones are not. The speed of the runner can be readily calculated. This doesnt guarantee that they will finish first, but it at least shows promise. The physical distance between the runners and the distance remaining to the finish line are obviously vital criteria that we can easily measure.

The AGI race doesnt have those kinds of assured forms of metrics or measurements.

We are using all manner of surrogate measures since there isnt any definitive way to calculate where the end line is and nor how far we are from it.

Lets take a look at the types of metrics conventionally being considered. An especially handy source of AI-related global measurements is annually collected and published by the Stanford Institute for Human-Centered AI (HAI) at Stanford University. The report is available online for free and the latest release is entitled The AI Index 2022 Annual Report (based on data collected for 2020-2021). Ill be sharing with you in a moment some highlights of the national comparisons mentioned in their latest compilation.

Metrics that are being used to gauge national and international progress on AI tend to include a bit of everything, at times bordering on the inclusion of the veritable kitchen sink too.

The types of measures typically examined include:

Consider these contemporary indications from the HAI AI Index 2022:

We can abundantly admire and appreciate the hard work involved in compiling those nation-state runner statistics for the AGI race.

Skeptics though quibble quite a bit about using any types of metrics in the AGI race engagement.

The thorny question is whether you can draw any kind of straight line from the number of AI articles or AI conferences within a particular nation to the ultimate attainment of AGI. The same is said for the number of AI jobs, the number of AI companies, and the slew of other metrics. It could be that those counts have little to do with attaining AGI. The argument goes that those measures are more heat than light.

The counterargument is that we have to try and measure where we are and where we are going. Putting your head in the sand does not seem like much of a viable way to assess whether we are heading toward AGI or maybe further away from AGI. It is hoped and generally assumed that the more energy and attention toward making advances in AI, the closer we will get to AGI. These metrics are the best that we can do to glean how much energy and attention is being allocated and consumed in the AGI race.

Round and round that goes.

Each of the metrics can by themselves also be batted about the head.

For example, consider the number of legislative laws or bills about AI.

You can claim that if lawmakers are focusing on AI-related laws, this is a good sign that the nation is taking quite soberly the importance of AI and the societal ramifications of where AI is headed. A case can be made that this showcases that a lot of AI advancing effort is arising in that particular nation. Why would you go to the trouble to enact AI laws unless AI was notably burgeoning and bubbling up as a demonstrative element of your nation?

In that manner, those nations promulging new AI laws are interpreted as a telltale sign or signaling of AGI progress is well underway in that nation.

Some critics assert that proposed new laws about AI are going to stifle AI efforts within each such particular nation. The lawmakers and political leaders are going to shoot their own feet. Laws are going to prematurely put a gloomy shadow over AI efforts underway in that specific nation. The AI advancement faucet is going to get jammed up with legal hair clogging and the pace of AGI progress will slow to a trickle in that AI law proclaiming nation. In the meantime, other nations that arent passing those kinds of AI laws will continue unabated. It is as though you decided to put lead weights on a runner that is already on the 400-meter track. If you aimed to aid them and speed them up, youve done precisely the opposite.

Whoa, the retort goes, the enactment of AI laws is more akin to making sure that there arent any unnecessary roadblocks ahead of the runner. The laws provide guidance in the same means that the lines on the racetrack are there to keep the runner smoothly going in the right direction. Without those painted lines, the runners might go amok. New AI laws will keep them striding in unison toward a desirable outcome. Countries that dont do likewise in terms of new AI laws will find their runners going in every wild direction, including possibly running entirely off the track and harming those that are innocents beyond the AGI race itself.

There are also hidden AI-related laws that some are counting and meanwhile some others are not counting as part of these metrics (making for a mishmash when trying to compare counts).

For example, if a nation enacts a law regarding autonomous vehicles such as self-driving cars, do you count this as an AI law? To clarify an autonomous vehicle such as a fully autonomous self-driving car is going to have an AI driving system that is core to driverless capabilities (see my coverage at the link here). Due to the AI involved, any laws about autonomous vehicles could be sensibly argued as essentially AI laws. On the other hand, you might persuasively assert that the law is about the autonomous vehicle and not per se about the AI, therefore this doesnt count in the AI-specific laws counting.

It is messy.

All of this consternation about metrics might cause you to shrug your shoulders amid the polar opposite views on these weighty considerations. As might be evident, the metrics are nearly always subject to disparate interpretations about what they mean and how the status of a nation regarding AGI is appropriately analyzed.

Geopolitical Maneuvering And Alignment For The AGI Race

Which nations are ahead in the AGI race?

Which nations are falling behind?

The aforementioned metrics attempt to showcase where each of the runners currently is. A basic assumption is that if the metrics do portray an apt indication of AGI seeking positioning, these various pole positions might remain the same over time. Of course, the reality is that national interest and attention can increase or can wane during the bumpy path toward AGI. You might be wisest to expect changes in positioning.

One important consideration is that nations are not really in this race on their own.

Nations are likely to be handing the baton back and forth between each other. The AGI race at times has one or more nations gladly working hand-in-hand. Sometimes this is done warily rather than with friendly glee. In other instances, nations might hold back from each other. At any instant in time, the race posturing can be quite different than it was a few steps back, plus can be quite different a few steps into the future.

Consider this point made by the HAI AI Index 2022 report about cross-country collaborations: Despite rising geopolitical tensions, the United States and China had the greatest number of cross-country collaborations in AI publications from 2010 to 2021, increasing five times since 2010. The collaboration between the two countries produced 2.7 times more publications than between the United Kingdom and Chinathe second highest on the list.

Cynics would say that perhaps the use of cross-collaborations is occasionally done as a ruse. A nation might overtly claim they are cross-collaborating, appearing surface-wise to be doing so, meanwhile deep down they are keeping their best AGI progress a hidden national secret. Maybe this is done to blunt the progress of the cross-collaboration nation. Perhaps this is being done to ensure that the secret sauce is not inadvertently handed out. All kinds of reasons are possible.

In todays modern digital Internet online world, trying to keep AGI insights tightly under wraps can be a difficult chore. The intense desire to uncover or invent AGI is a compelling allure that can spur individual AGI developers and researchers to openly share their latest work. Nations can find that trying to put a cap on such sharing is a lot harder than it might seem, and likely a lot harder than back in the days when everything was paper-based and required physically moving documents around the globe.

The movement toward open source has certainly been a contemporary emphasis for much of the latest AI and AGI research, as mentioned in the HAI AI Index 2022 report: Each year, thousands and thousands of AI publications are released in the open source, whether at conferences or on file-sharing websites. Researchers will openly share their findings at conferences; government agencies will fund AI research that ends up in the open-source; and developers use open software libraries, freely available to the public, to produce state-of-the-art AI applications. This openness also contributes to the globally interdependent and interconnected nature of modern AI R&D.

All in all, nations are generally sharing and yet might only be showing part of their hand. Other nations might not be sharing or only give a pretense of doing so. Some nations struggle mightily with trying to gauge what those within their nation are giving away versus hanging onto. And so on.

Ive characterized the nature of these national moves in these ways:

A nation can be in more than one of those buckets at a time.

A nation can be in one of those buckets for a while, move out of the bucket, and possibly later go back in.

The nation-state desires and attention pertaining to seeking AGI is a dynamic ebb-and-flow that assuredly will keep on going and be a moving target as to which nation is where in the race, and a constant eye will be needed to figure out where all the players are positioned at a given moment in time.

International AI Laws And AI Ethics As Referees In The AGI Race

In prior columns, Ive covered the various national and international efforts to craft and enact laws regulating AI, see the link here and the link here, for example. I have also covered the various AI Ethics principles and guidelines that various nations have identified and adopted, including for example the United Nations effort such as the UNESCO set of AI Ethics that nearly 200 countries adopted, see the link here.

Here's a helpful keystone list of Ethical AI criteria or characteristics regarding AI systems that Ive previously closely explored:

Those AI Ethics principles are earnestly supposed to be utilized by AI developers, along with those that manage AI development efforts, and even those that ultimately field and perform upkeep on AI systems.

All stakeholders throughout the entire AI life cycle of development and usage are considered within the scope of abiding by the being-established norms of Ethical AI. This is an important highlight since the usual assumption is that only coders or those that program the AI are subject to adhering to the AI Ethics notions. As prior emphasized herein, it takes a village to devise and field AI, and for which the entire village has to be versed in and abide by AI Ethics precepts.

Lets consider the impact and vital nature of international AI laws and international proclamations of AI Ethics precepts on the AGI race.

Nations striving toward AGI might do so with abandon and find themselves veering toward some of the oft popularized existential risks of AGI. The hope is that by putting in place international AI laws and international AI Ethics precepts, nations will be guided toward AI For Good and steer clear of AI For Bad.

Per our racetrack analogy, those AI laws and AI Ethics considerations are like trying to keep runners from going outside of the track. There are immense temptations to take shortcuts in the AGI race. Those shortcuts could lead a nation down a seemingly sooner to the finish line path, though simultaneously putting that nation and other nations at undue risk. A subtle but telling example consists of dual-use AI, which Ive examined at the link here, whereby an AI advancement is readily switched with nary much effort from being aimed at goodness to being wrought with producing cataclysmic badness.

You could assert that the international AI laws and international AI Ethics are like referees or umpires.

An assumption is that these internationally devised legal and ethical mechanisms will keep the AGI race on a more even keel. The thing is, whether particular nations opt to heed the referees or umpires is a different matter. Similarly, there is a vexing question of how those authorities can provide penalties or incentives to keep the runners on the right track. The odds are that nations will do as they wish to do, for which other nations might need to shift their weight to bolster support for the off-the-path rule-breaking that some nations are bound to undertake.

Conclusion

Louis Pasteur, the legendary chemist and microbiologist, famously said this: Science knows no country, because knowledge belongs to humanity, and is the torch which illuminates the world. Science is the highest personification of the nation because that nation will remain the first which carries the furthest the works of thought and intelligence.

Can we say that the attainment of AGI knows no country and that AGI will belong to all of humanity?

Or will the nation that first arrives at AGI be possessive of it, becoming drunk with abject power and going power mad?

For those of you that like a bit of a twist on this particular conundrum, consider that AGI might in itself be the type of attainment that is the proverbial snake in the grass. The discoverer of the snake might be the first to get a snakebite. Being first has its risks.

Crossing the finish line on AGI is not necessarily going to be as celebratory and carefree as some might think. Nor will harnessing AGI be necessarily easy. Some might argue that coping with AGI could be nearly impossible since the AGI will seemingly have the comparable deviousness and ingenuity that humankind has. Nations ought to be mindful of what they are trying to attain and what the result will be, doing so beforehand and not getting caught by surprise. They might have a hornet's nest in their national treasure chest.

As Pasteur proffered: Fortune favors the prepared mind.

The rest is here:

AI Ethics And The Geopolitical Wrestling Match Over Who Will Win The Race To Attain True AI - Forbes

Deep Learning Pioneers Yoshua Bengio, Geoff Hinton, Yann LeCun Among the Scores of Industry Experts to Present at Worlds Premier AI Conference, Sept. 19-22

NVIDIA today announced that it will host its next GTC conference virtually from Sept. 19-22, featuring a news-packed keynote by founder and CEO Jensen Huang, and more than 200 sessions with global business and technology leaders. Registration is free and open now.

Huangs keynote will be livestreamed on Tuesday, Sept. 20, at 8 a.m. PT and available on demand afterward. Registration is not required to view the keynote.

GTC will also feature a fireside chat with Turing Award winners Yoshua Bengio, Geoff Hinton and Yann LeCun discussing how AI will evolve and help solve challenging problems. The discussion will be moderated by Sanja Fidler, vice president of AI Research at NVIDIA.

GTC talks will explore some of the key advances driving AI and the metaverse -- including large language models, natural language processing, digital twins, digital biology, robotics and climate science.

Major talks include:

GTC offers a range of sessions tailored for many different audiences, including business executives, data scientists, enterprise IT leaders, designers, developers, researchers and students.

Content for Developers and ResearchersGTC provides participants at all stages of their careers with outstanding learning-and-development opportunities, many of which are free. Developers, researchers and students can sign up for 135 sessions on a broad range of topics, including:

Attendees who wish to strengthen their skills can sign up for hands-on, full-day technical workshops and two-hour training labs offered by the NVIDIA Deep Learning Institute (DLI). Twenty workshops are available in multiple time zones and languages, and more than 25 free training labs are available in accelerated computing, computer vision, data science, conversational AI, natural language processing and other topics.

Registrants may attend free two-hour training labs or sign up for full-day DLI workshops at a discounted rate of $99 through Thursday, Aug. 29, and $149 through GTC.

Insights for Business LeadersThis GTC will feature more than 30 sessions from some of the worlds leading companies in key industry sectors, including financial services, industrial, retail, automotive and healthcare. Speakers will share detailed insights to advance business using AI and metaverse technology, including: building AI centers; the business value of digital twins; and new technologies that will define how we live, work and play.

In addition to those from the companies listed above, senior executives from AT&T, BMW, Fox Sports, Lucid Motors, Medtronic, Meta, Microsoft, NIO, Pinterest, Polestar, United Airlines and U.S. Bank are among the industry leaders scheduled to present.

Sessions for Startups NVIDIA Inception, a global program with more than 11,000 startups, will host several sessions, including:

Explore the full GTC session catalog and register to attend today.

NVIDIA Financial Analyst Q&ANVIDIA management will hold a Q&A session with financial analysts following the keynote; the webcast will be available at investor.nvidia.com.

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NVIDIA GTC to Feature CEO Jensen Huang Keynote Announcing New AI and Metaverse Technologies, 200+ Sessions With Top Tech, Business Execs - NVIDIA Blog

Patent filed for new lithium mineral location technology

TEL AVIV, Israel (ASTERRA PR) Today,ASTERRAannounced a patent filing on advancements in using PolSAR-based technology for lithium exploration that will greatly accelerate the identification of lithium (LI) deposits. The patent was based on extensive field testing for validation.

The expansion to mining is a natural progression of our ability to use AI analytics to monitor soil moisture underground, saidElly Perets, CEO of ASTERRA. It also fulfills our mission to become humanitys eyes to protect the environment.

ASTERRAs complex artificial intelligence (AI) and machine learning (ML) algorithms extract the signal of lithium concentration underground from satellite based PolSAR data and can pinpoint locations containing high lithium. This technology creates a way to find lithium before investing in costly exploration with intensive labor, and where it may result in environmental destruction and civil conflicts.

Inthe United States, theDepartment of Energy published its National Blueprint for Lithium Batteries,which makes sourcing lithium inside the U.S. a priority through the year 2030 because this mineral is often unavailable to meet the needs of the manufacturing industry.Lithium is essential in the transportation, semiconductor, microchip, cell phone, and any industry where a battery is used. Last week, theCHIPS and Science Act of 2022was enacted, further bolstering these initiatives.

Lithium is the wonder metal at the heart of the global desire to move to cleaner energy with reduced carbon emissions, but the demand exceeds the supply. This causes an almost 500 percent increase in lithium prices and harms the effort to stop global warming, saidLauren Guy, the founder and chief technology officer of ASTERRA. Global demand for lithium is insatiable, and the supply crisis is present and significant. ASTERRA can now focus the efforts of companies to mine the metal in a much more efficient and accurate way.

In a report byenergystoragenews.com, Bloomberg rankedChinathe top supplier of lithium ion batteries. SinceChinais the dominant source for lithium, ongoing political conflicts cause supply chain disruptions which have a serious negative global impact. One industry impeded by this is the automotive industry.

On itswebsite, Volkswagen notes, Electric cars are significant contributors to climate protection but the mining of lithium for the batteries is often criticized. Nevertheless, one challenge the manufacturer faces is their critical need for the mineral. An important growth driver isits use in the batteries of electric vehicles. However, lithium is also used in the batteries of laptops and cell phones, as well as in the glass and ceramics industry, the site also states.

ASTERRAs satellite based PolSAR technology is already proven as a solution to find underwater leaks in the water utility industry (winning theAWWA Innovation Awardin 2021), and also provides soil moisture data tomining operations. Because it provides intelligence regardless of weather conditions, time of day, and penetrates the ground and obstructions including pavement, trees, and soil, it is an efficient solution for underground monitoring.

About ASTERRA

ASTERRA (formerly Utilis) provides geospatial data-driven platform solutions for water utilities, government agencies, and the greater infrastructure industry in the areas of roads, rails, dams, and mines. ASTERRA products and services use Polarimetric Synthetic Aperture Radar (PolSAR) data from satellites and turn this data into large-scale decision support tools. The companys proprietary algorithms and highly educated scientists and engineers are the keys to their mission, to become humanitys eyes on the Earth. ASTERRA is investing in artificial intelligence (AI) to bring its products to the next level. Since 2017, ASTERRA technology has been used in over 64 countries, saving over 210,830 million gallons of potable water, reducing carbon dioxide emissions by 134,930 metric tons, and saving 527,070 MWH of energy, all in support of United Nations Sustainable Development Goals. ASTERRA is headquartered inIsraelwith offices inthe United States,United Kingdom, andJapan. Their innovative data solutions are used in multiple verticals around the globe. For more information on ASTERRA and to learn more about their technology, visithttps://asterra.io.

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ASTERRA's new satellite based PolSAR technology uses AI for eco-friendly exploration of lithium Parabolic Arc - Parabolic Arc

New York, USA, Aug. 15, 2022 (GLOBE NEWSWIRE) -- The Global AI in Drug Discovery Market to Surge at a Phenomenal CAGR of 37.67% During the Forecast Period (20222027) | DelveInsight

The AI in drug discovery market is experiencing favorable market expansion as a result of factors such as increased disease prevalence around the world, which has prompted the need for speedier creation of highly safe and efficacious pharmaceuticals. Furthermore, the awareness of the benefits of AI in the pharmaceutical sector is pushing pharmaceutical companies and institutes to invest more in medication research and development. Furthermore, extensive partnerships and collaborations between public and commercial entities at both the national and international levels are projected to drive the AI in drug discovery market forward.

DelveInsight's AI in Drug Discovery Market Insights report provides the current and forecast market, forthcoming device innovation, individual leading companies market shares, challenges, AI in drug discovery market drivers, barriers, and trends, and key AI in drug discovery companies in the market.

Key Takeaways from the AI in Drug Discovery Market Report

To read more about the latest highlights related to the AI in diagnostic and drug discovery market, get a snapshot of the key highlights entailed in the Global AI in Drug Discovery Market Report

AI in Drug Discovery

Artificial intelligence (AI) has become a part of modern life and continues to pique people's interests. AI enables computers and robots to learn from past behavior and mistakes in the same way that humans can. It has invaded our daily lives, from search engines to self-driving cars to Siri. AI has the potential to make complex drug development operations more efficient and cost-effective, with the goal of reducing the time it takes for a novel medication to reach the patient. The hype surrounding AI has drawn the attention of the life sciences sector to technology.

Artificial intelligence (AI) in drug discovery refers to the use of advanced computing techniques such as machine learning, artificial neural networks, and natural language processing to process large amounts of data in order to assist with the target, lead, and other required inputs for drug discovery and development.

AI in Drug Discovery Market Insights

North America is expected to have the highest revenue share in the global AI in drug discovery market in 2021, out of all regions. This is due to the presence of a large patient population affected by various diseases such as cancer and neurological disorders, which drives the demand for diverse treatments with low adverse effects. Furthermore, the region's considerable focus on clinical research, as well as the presence of key companies from both the pharmaceutical and technology domains, contribute to the growth of the North America AI in drug discovery market.

Canada, like the United States, has a robust ecosystem for AI in the drug discovery process, as evidenced by the fact that multiple startups are working in the country to combine AI with drug development.

Thus, all of the factors mentioned above, such as high disease prevalence and increased emphasis on clinical research and medication development, are predicted to contribute to the growing demand for AI in the drug discovery process. Furthermore, the acquisition of new technologies and the extensive presence of technological and pharmaceutical leaders in the region contribute to the AI in the drug discovery market's regional expansion.

To know more about why North America is leading the market growth in the AI in drug discovery market, get a snapshot of the AI in Drug Discovery Market Trends

AI in Drug Discovery Market Dynamics

High capital investment in the drug research and development process is one of the primary factors impacting the growth of the AI in drug discovery market. Following the traditional technique of drug discovery and development consumes 12-14 years until a final product, the approved medicine, enters the AI in drug discovery market for end-use.

Another component of incorporating AI into the drug discovery and development process is leveraging the technology to identify "trending" areas of research for various diseases that may provide insights regarding any scientific advancements that may be used in launching a new medication development program. Furthermore, the use of AI solutions in the clinical trial process eliminates potential hurdles, aids in the reduction of clinical trial cycle time, and considerably enhances clinical trial productivity and accuracy. As a result, the use of these advanced AI systems in drug discovery procedures is gaining traction among stakeholders in the life sciences industry.

However, knowledge gaps between biologists, chemists, and AI experts, as well as limitations of typical machine learning methods in dealing with the volume of data created in the pharmaceutical area, may prove to be tough issues for the rise of AI in drug discovery market.

Additionally, during the COVID-19 pandemic, the AI in drug discovery market was one of the few that had positive growth. The artificial intelligence platform was extensively used in therapeutic development for the SARS CoV-2 virus. AI was also used in drug repurposing, sometimes known as drug repositioning, to assist bring new medicines to market. For example, Remdesivir was found as a potential treatment for Ebola virus sickness; however, using medication repurposing using AI revealed that the medicine had encouraging results in the treatment of COVID-19 infection. As a result, the AI in drug discovery market showed a good trend during the pandemic, presenting a future outlook for AI in drug discovery market during the forecasted period.

Get a sneak peek at the AI in drug discovery market dynamics @ AI in Drug Discovery Market Dynamics Analysis

Scope of the AI in Drug Discovery Market Report

DelveInsight Analysis: The AI in drug discovery market size is expected to grow at a CAGR of 37.67% during the forecasted period (20222027).

Which MedTech key players in the AI in drug discovery market are set to emerge as the trendsetter explore @ AI in Drug Discovery Companies

Table of Contents

Interested in knowing the AI in drug discovery market by 2027? Click to get a snapshot of the AI in Drug Discovery Market Scenario

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AboutDelveInsight

DelveInsight is a leading Business Consultant, and Market Research firm focused exclusively on life sciences. It supports Pharma companies by providing comprehensive end-to-end solutions to improve their performance.

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The Global AI in Drug Discovery Market to Surge at a Phenomenal CAGR of 37.67% During the Forecast Period (20222027) | DelveInsight - GlobeNewswire

Data-Driven Thinking is written by members of the media community and contains fresh ideas on the digital revolution in media.

Over the past few years, advertising has become far more data-driven. AI is playing a large role in the transformation, helping advertisers measure campaign efficacy and transform data into actionable insights.

But AI is far from infallible. The technology reflects human biases. Thats why, to make the most of AI, advertisers must diversify data analytics teams to bring varied perspectives and talents to data collection and decision-making. Then, advertisers can combine artificial and human intelligence to maximize the value of each.

Heres a road map for how to do just that.

Understand and mitigate AIs limitations

To understand AIs limitations, consider this years Australian Open. The tournament used AI to process match data in real time and make predictions about probable victors. In the mens final, Rafael Nadal was down two sets to love against Daniil Medvedev. He was projected to have a 4% chance of winning. But Nadal defied AIs data-driven predictive capacities and won the next three sets to clinch the match.

The same applies to AI in advertising. If we ask a consumer survey question in a different way, we get different answers. If we scan a data set for certain demographic information but make omissions, we risk coming up with skewed analytics and suboptimal decisions. To that end, advertisers need to be aware of AIs blind spots when using the technology for marketing analytics.

One crucial step the data scientist should take to ensure the highest possible accuracy and quality of AI is to actively recognize any selection biases in the data collected and use randomization or statistical correction to remove it.

This is especially true for survey data. Certain survey formats and media attract particular types of participants. Various incentive programs offered by panel-based research organizations may also affect respondent composition. Data scientists need to think about the sample composition rather than just what the data says.

Diversify data analytics teams to minimize AIs blind spots

People work differently when they see a business challenge or hypothesis, and their backgrounds and past experiences inform their approaches. Only by diversifying teams, and the problem-solution approaches they prefer, can we become maximally competent in accounting for all possible solutions.

Lets say a market research team is designing a survey to understand how customers of different genders respond to advertising. A team without anyone who identifies outside the gender binary can fail to account for gender fluidity and nonbinary folks. If the product caters to people of a certain gender or to young and urban populations who often identify as nonbinary, the failure to diversify teams can skew research and undermine the precision of analytics.

Still, diversity is lacking. Nearly six in 10 marketers are white, according to a survey by the ANA of 61 of its 1,400 member companies. Surveys rarely include options for respondents to identify as nonbinary. And anyone whos worked in marketing knows that diversity drops among key decision-makers. For example, people identifying as female account for 75.1% of admins and 70.8% of entry-level marketing professionals, according to the ANA, while they account for just 54.8% of senior management.

Agencies must tackle the diversity issue. That means not just hiring diverse staff but also diversifying at senior levels.

Combine artificial and human intelligence

In advertising analytics, the primary barrier to maximizing the combined value of artificial and diverse human intelligence is multiple-choice-style market research. The advertising industry needs to collect information about customers through more open-ended experiments and surveys that account for nuance. Then, it can use AI-driven text mining and other tactics to transform more unstructured data into actionable insights.

Another area for improvement is the use of analytics to understand customer emotions. Feelings are too complex to be articulated in terms of binary data. Natural language processing tools can equip teams to analyze free-form customer expression on public platforms, assessing such varied questions as how much buzz a campaign is generating, how people feel about a new brand or product, or how customer sentiment has evolved since a major corporate event. Analysis of unstructured data removes bias brought on by humans and enables a more accurate data-driven approach to audience analytics and marketing.

These days, advertising analytics teams and market researchers eliminate human ambiguity from the front end, forcing customers to fit into binaries in surveys that dont reflect their complexity. Then, when it comes to making decisions based on oversimplified data, we rely too much on human intuition, introducing error and magnifying bias.

Ideally, the opposite would be true. The advertising industry should leave more room for ambiguity in information collection and lean on technology more when it comes to interpreting data, calibrating the media mix, and determining attribution. If we can move in that direction, advertising will be on its way to becoming a more equitable, representative, and truly data-driven business.

Follow mSix (@MSIXagency) and AdExchanger (@adexchanger) on Twitter.

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Advertisers Need More Than AI. They Need Diverse Human Talent - AdExchanger

NEW YORK--(BUSINESS WIRE)--Perfect Corp., the leading artificial intelligence (AI) and augmented reality (AR) beauty and fashion tech solutions provider, today announced that their Beauty SaaS Brand Console dedicated to AI Skin Analysis is compliant with the United States federal statute governing the security and privacy of protected health information (PHI).

Industry-Leading AI Skin Solution with HIPAA-Compliant Personal Data Protection Mechanisms

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law that protects health-related personal information. Compliance with HIPAA indicates that Perfect Corp. delivers industry-standard security procedures around a personally identifiable health and health-related data coming from their proprietary AI Skin Analyzer solution. This includes diagnosis data, clinical care data, and lab results such as images and test results.

Ensuring the Highest Level of Protection for Private Healthcare Information

With this certification, brands using Perfect Corp.s AI Skin solution benefit from protection against PHI loss (private healthcare information loss), increased wellbeing of patients thanks to standards on handling patients sensitive data, as well as reduction of potential corporate liability. On the end user side, patients can use Perfect Corp.s AI Skin Solution with full confidence, knowing that their personal data adheres to the HIPAA standards.

Data Protection as a Core Commitment

We have always been committed to protecting the data of both the brands that we work with and the end users of our solutions, shared Perfect Corp. Founder and CEO Alice Chang. Compliance with HIPAA further highlights this dedication, and will reassure our partners that we are taking every step necessary in safeguarding their valuable data.

About Perfect Corp.

Perfect Corp. is the leading SaaS AI and AR beauty and fashion tech solutions provider, dedicated to transforming shopping experiences through empowering brands to embrace the digital-first world. By partnering with the largest names in the industry, Perfect Corp.s suite of enterprise solutions deliver synergistic, technology-driven experiences that facilitate sustainable, ultra-personalized, and engaging shopping journeys, as well as equipping brands with next generation of consumer goods. Perfect Corp. offers a complementary suite of mobile apps, including YouCam Makeup and YouCam Perfect, to provide a consumer platform to virtually try-on new products, perform skin diagnoses, edit photos, and share experiences with the YouCam Community. To learn more, please visit PerfectCorp.com.

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Perfect Corp.'s Beauty SaaS Brand Console for AI Skin Analysis is Confirmed to Be HIPAA-Compliant - Business Wire

Adapted from a press release:

On July 21, 2022, Grupo Nutec held its event, Nutrimos CONCIENCIA, at the Quertaro Congress Center in central Mexico. The event name, which translates in English to Nurturing Consciousness, sought to raise awareness on how people make decisions, encouraging mindfulness in decision making. In this regard, the company stated its decisions rely on science and technological change.

The event focused on people talents and their integration within an inspiring business culture, including technology and artificial intelligence (AI), to achieve a successful future in business. Jrmie Larcher, CEO of Grupo Nutec, kicked off with a passionate talk about the importance of finding our goals in life and working toward them, both on a business and personal level.

At the event, four keynote speakers delivered compelling and engaging presentations. The event started with the talk Develop consciousness: A step toward fullness by Eduardo Garza, Ph.D. Afterward, Gonzalo Villar, who holds masters degree in animal nutrition, spoke about the future of food with a talk called Nutrition 4.0: How will new technologies change our way of doing nutrition?

The second half of the event started with the talk How will artificial intelligence change the operations of companies? by Dr. Ayesha Khanna, one of the most prominent women in technology in Asia. The cycle of presentations concluded with Felipe Gmez and his inspiring chat, When talent, attitude and passion come together.

As a finishing touch to a great day, attendees enjoyed a gala dinner accompanied by a mixology show, including live jazz music and mariachi. Without any doubt, it was an event that guests enjoyed and will remember for a long time, said the press release.

Grupo Nutec is market leader in Mexico of premium pet food products. The brand Nupec leads the domestic market in the veterinary channel and is the best-selling brand at online outlets like Amazon and Mercado Libre.

The company recently inaugurated a new facility, the innovation and research center Sanuren, in the state of Queretaro. The center focuses on palatability and digestibility trials and has the support of a wide network of scientists specialized in animal nutrition who work on site.

In Mexico, only a few pet food companies own research centers to run these types of trials. Such facilities provide the company a competitive advantage over other players and raise the confidence of the brand, as innovation is essential to pet food.

Accordingly, Nupec announced at its event the launch of 16 new products for dogs and cats in the second half of 2022.

Ivn Franco is the founder of Triplethree International and has collaborated on hundreds of research projects for several consumer goods industries. He was granted the Global Consultant of the Year award by Euromonitor International and authored the book 17 Market Strategies for Growth (in Spanish).

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Nupec brings innovation and AI to pet food in Mexico - Petfood Industry