Feb
“Distillation” Is Shaking Up The AI Industry
jerry9789 0 comments artificial intelligence, Brandview World
Copyright: Airam Dato-on
Paradigm Shift
We’ve recently written about recent AI advancements and popularity, particularly generative AI like that of ChatGPT, driving renewed demand for data centers not seen in decades. This surging demand pushed tech investors to put $39.6 billion into data center development in 2024, which is 12 times the amount invested back in 2016.
A recent development, however, has stirred things up, especially the concept that billions of dollars needed to be spent for AI advancement. Developed by a Chinese AI research lab, an open-source large language model named DeepSeek was released and performed on par with OpenAI, but it reportedly operates for just a fraction of the cost of Western AI models. OpenAI, however, is investigating if DeepSeek utilized distillation of the former’s AI models to develop their systems.
Copyright: cottonbro studio
What Is “Distillation?”
According to Labelbox, model distillation (or knowledge distillation) is a machine learning technique involving the transfer of knowledge from a large model to a smaller one. Distillation bridges the gap between computational demand and the cost for training large models while maintaining performance. Basically, the large model learns from an enormous amount of raw data for a number of months and a huge sum of money typically in a training lab, then passes on that knowledge to its smaller counterpart primed for real-world application and production for less time and money.
Distillation has been around for some time and has been used by AI developers, but not to the same degree of success as DeepSeek. The Chinese AI developer had said that aside from their own models, they also distilled from open-source AIs released by Meta Platforms and Alibaba.
However, the terms of service for OpenAI prohibits the use of its models for developing competing applications. While OpenAI had banned suspected accounts for distillation during its investigation, US President Donald Trump’s AI czar David Sacks had called out DeepSeek for distilling from OpenAI models. Sacks added that US AI companies should take measures to protect their models or make it difficult for their models to be distilled.
Copyright: Darlene Anderson
How Does Distillation Affect AI Investments?
On the back of DeepSeek’s success, distillation might give tech giants cause to reexamine their business models and investors to question the amount of dollars they put into AI advancements. Is it worth it to be a pioneer or industry leader when the same efforts can be replicated by smaller rivals at less cost? Can an advantage still exist for tech companies that ask for huge investments to blaze a trail when others are too quick to follow and build upon the leader’s achievements?
A recent Wall Street Journal article notes that tech executives expect distillation to produce more high-quality models. The same article mentions Anthropic CEO Dario Amodei blogging that DeepSeek’s R1 model “is not a unique breakthrough or something that fundamentally changes the economics” of advanced AI systems. This is an expected development as the costs for AI operations continue to fall and models move towards being more open-source.
Perhaps that’s where the advantage for tech leaders and investors lies: the opportunity to break new ground and the understanding that you’re seeking answers from unexplored spaces while the rest limit themselves and reiterate within the same technological confines. Established tech giants continue to enjoy the prestige of their AI models being more widely used in Silicon Valley — despite DeepSeek’s economical advantage — and the expectation of being the first to bring new advancements and developments to the digital world.
And maybe, just maybe, in that space between the pursuit of new AI breakthroughs and lower-cost AI models lie solutions to help meet the increasing demand for data centers and computing power.
Copyright: panumas nikhomkhai
Featured Image Copyright: Matheus Bertelli
Top Image Copyright: Airam Dato-on
Jan
The Demand For Data Centers
Do you know how much energy a ChatGPT query consumes? If you use a traditional Google search to find the answer, that particular Google search would use about 0.0003 kWh of energy, which is enough power to light up a 60-watt light bulb for 17 seconds. A ChatGPT query (or even Google’s own AI-powered search) consumes an estimated 2.9 Wh of energy, which is ten times the energy used by a traditional Google search. Multiply the energy consumed by 200 million ChatGPT daily queries by a year and you have enough power for approximately 21,602 U.S. homes annually or to run an entire country like Finland or Belgium for a day.
No surprise then that AI’s growing mainstream popularity and usage have led to an increase in data center demand. For years, data centers were able to maintain a stable amount of power consumption despite their workloads being tripled, thanks to efficient use of the power they consume. However, that efficiency is now challenged by the AI revolution, with Goldman Sachs Research estimating data center power demand growing 160% by 2030.
McKinsey & Company noted in a September 2024 report that data centers consume 3% to 4% of total US power demand today, while Goldman Sachs puts worldwide use at 1% to 2% of overall power. By the end of the decade, data centers could be seen accounting for 12% of total US power and 3% to 4% of overall global energy. This level of demand spurred investors to push $39.6 billion into data center development and related assets in 2024, which is 12 times the amount spent in 2016.
Copyright: panumas nikhomkhai
How All That Power Is Used by AI
What were once warehouse facilities hosting servers mostly found in industrial parks or remote areas, data centers have now progressed into vital institutions in the digital world we’re living in today. The “old” problem used to be that the traditional data centers had some space that was underutilized, while the “new” problem is that space is scarce and critically needed. This has created a surging demand for more of these structures in order to address AI’s accelerating demand for more computing power.
As illustrated earlier, AI workloads consume more power than traditional counterparts like cloud service providers. Out of the two primary AI operations, “training” requires more computing power to build and expand models over time. However, the operation that derives responses from existing models called “inferencing” is growing quickly in volume as AI-powered applications become more popular and widely adopted according to a Moody’s Rating report. Their report further predicts inferencing growth over the next five years to make up a majority of AI workloads.
Amazon, Google, Microsoft, and Meta have stepped up to satisfy the demand by building, leasing and developing plans for hyperscale data centers. Moreover, investors have demonstrated a pronounced prioritization of AI projects over traditional IT investments; in fact, an Axios article noted that “tech leaders are actually worrying about spending too little.”
Copyright: Brett Sayles
Chokepoints in the AI Boom
It’s not only power that tech companies and developers have to consider when addressing the demand for new data centers; their location and access to that power are challenging their progress and development. Data centers may consume up to 4% of US power today, but because they are clustered in certain major markets they pose a substantial stress to local resources. McKinsey & Company’s September 2024 report also noted that there is at least a three-year wait time for new data centers to tap into the power grid of a major market like Northern Virginia. The search for available space, power and tax incentives have led developers to look into other markets like Dallas or Atlanta, according to a Pitchbook article.
Unlike investors, some local governments are not as keen on the development of data centers and may pause contracts or prioritize granting access to their power grid to other projects. An October report by the Washington Post revealed that a number of small towns have strongly pushed back against the construction of data centers in their areas.
In addition to concerns from residents over the strain data centers would put on local resources, carbon dioxide emissions can’t be overlooked, as Goldman Sachs estimates that such emissions will more than double by 2030. Areas with rising energy consumption from data centers might therefore find it challenging to meet climate targets.
Accelerated data center equipment demands are also straining the supply chain, as orders are taking years to fulfill. Planned tariff increases could also stress the delivery of offshore-produced parts and components used by data centers.
Copyright: panumas nikhomkhai
Navigating Towards The Future
While it is exciting to witness AI’s mainstream adoption and technological advancements, the challenges brought forth by its explosive demand for data centers could be just as intimidating. Perhaps Forbes expressed best how we should navigate this period of growth and uncertainty: we need to plan with purpose, not panic. We need to build with responsibility, not exuberance.
While the AI boom granted us the opportunity to correct the oversupply of demand centers resulting from the dot-com boom, we need to tread more carefully when addressing this new need for more of these infrastructures. We need to understand what each data center is being planned and built for, its primary purpose, and how it connects to local resources. Yes, we were able to breathe new life into data centers that were underutilized after the Internet boom, but that took years and a new technological revolution to correct. Who knows if we’re also able to enjoy the same chance to recover if we fumble today’s attempts to address the demand for data centers? For all we know, the next technological wave might such be a swerve that it advances beyond the need for data centers.
We should also improve, innovate or discover new renewable and responsible energy solutions as well as increase the efficiency of our developing technologies’ use of power. There’s also an opportunity for the tech industry to enter into dialogues with local audiences about what is happening in the digital world and what they can expect from AI, thus demonstrating transparency and social responsibility.
As investors and developers eagerly meet AI’s surging demand for data centers, perhaps we can pause and appreciate this opportunity to make decisions that not only fortify the connection between the digital space and the world we live in today, but also forge a more responsible and sustainable path towards the future.
Copyright: TheDigitalArtist
The Impact of DeepSeek
While the tech industry searches for solutions to the data center demand, a new player has emerged to shake things up: Chinese AI research lab DeepSeek has released their open-source large language model. Quickly shooting to the top of Apple Store’s downloads, DeepSeek has challenged contemporary views on AI development with a platform that performs just as powerfully and efficiently as OpenAI while reportedly operating at a fraction of its Western counterparts’ cost. While it feels like we’re on the brink of a paradigm shift, we believe its true impact is yet to be seen. We expect to know and learn more in the coming weeks, and we’ll share our thoughts in a future blog.
Copyright: Yanu_jay
Featured Image Copyright: franganillo
Top Image Copyright: Yamu_Jay
Sep
So Why Use AI For Your Small Business?
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Burning Questions
Artificial Intelligence has actually been around for decades already but it grew past being a buzzword and into the mainstream in 2022 with the surprise popularity of OpenAI’s ChatGPT. Nowadays, it might be challenging to find someone who doesn’t have an iota of an idea of what AI is and what it does. In fact, its widespread cultural adoption belies its real impact behind the scenes where it steadily transforms and shapes businesses and industries towards a more automated and optimized direction.
Now as a small business owner, you might think that last statement doesn’t apply to you and is targeted mostly towards larger scale companies, but that is far from the truth. That last statement is just as relevant to your smaller, local-based trade as it is to any regional or global firm. In fact, 75% of small businesses have taken advantage of AI, according to the Small Business & Entrepreneurship Council. Additionally, 93% of small businesses agree that they save money and improve profitability utilizing AI solutions. We learned about these two interesting points when we attended a webinar hosted by CallRail, “Q&A: Demystifying AI for Small Businesses.”
You might have heard too that AI actually places everybody on the same playing field, and this was underscored at the webinar when they shared that small businesses have access to the same AI technology that big companies employ. At the same time, small businesses are granted a chance to achieve the same impact as their larger counterparts. Small businesses however enjoy being able to adapt or incorporate new technology and processes easier than their larger counterparts.
So how do you join the small businesses using AI to make money and grow? What are examples of AI being utilized by small businesses? Where do you start in understanding and applying AI solutions for your small business?
Copyright: Shafin_Protic
Artificial Intelligence and Its Subsets
Perhaps it’s best to follow suit with the webinar and include a quick look but fundamental understanding of AI and its subset. As you might know, AI technology enables machines like computer systems to simulate or emulate human intelligence and behavior by learning from training data, pattern recognition, decision-making, and problem-solving.
When that pattern recognition is taken one step further by involving huge data sets and advanced algorithms, a subset of AI called Machine Learning is developed. Aside from simulating or emulating human intelligence, Machine Learning allows computer systems to learn and adapt. However, a misstep in ML is the oversight of certain variables affecting the accuracy of the intended output.
A subset of ML called Deep Learning builds upon this limitation of overlooking variables by actually learning from these variables with historical data to generate accurate and high level outputs. DL achieves this by leveraging multiple layers of artificial neural networks for in-depth data processing and analytical tasks.
And when that high level data set is transformed into generated yet fine-tuned content like text, images, or code, we now arrive in the territory of Generative AI. This subset of DL models include the popular ChatGPT.
Copyright: geralt
How Are Small Businesses Using AI?
Like any other company or industry, small businesses have started to use AI to save time by streamlining, automating and optimizing whichever aspect of their processes that they could. One example is speech-to-text where instead of listening to every call, you convert a recorded phone call into summarized text with relevant and possibly actionable information or insight. By filtering calls in this manner, you’re also able to identify which types require the utmost attention and immediate follow-up, an especially valuable feature for qualifying leads.
As they say time is gold and so in the same vein where you free time by outsourcing time-consuming and repeatable tasks to another person or agency, automating processes through AI allow you to devote the time you free up to other more advanced functions or find more opportunities that can help further improve productivity and profitability, growing your business along the way.
Copyright: sohag_hawlader
Will AI Replace Small Businesses?
Now adapting and utilizing AI in your small business isn’t the end-all and be-all; it won’t even be replacing you wholesale anytime since it is, after all, just another tool at one’s disposal. Embracing the hot new tech keeps you at pace with the rest of the pack, but how you stand out will still fall on your business savvy and the intrinsic, unique value you bring to the table. Whether it be for your marketing or improving processes or customer relations, AI will help you glean as many insights as possible from your business transactions, interactions, and communications, but how effective that knowledge becomes will still depend on how well you leverage it.
Aug
The Impact of AI
In The Age of AI, which Henry Kissinger co-wrote with Eric Schmidt and Daniel Huttenlocher, Kissinger tried to warn us that AI would eventually have the capability to come up with conclusions or decisions that no human is able to consider or understand. Put another way, self-learning AI would become capable of making decisions beyond what humans programmed into it and base such conclusions on what it deems the most logical approach, regardless of how negative or devastating the consequences can be.
A common example to illustrate this point is how AI had already transformed games of strategy like chess, where given the chance to learn the game for itself instead of using plays programmed into it by the best human chess masters, it executed moves that have never crossed the human mind. And when playing with other computers that were limited by human-based strategies, the self-learning AI proved dominant.
When applied to the field of warfare, this could possibly mean AI proposing or even executing the most inhumane of plans regardless of human disagreement simply because it considers such a decision the most logical step to take.
The Influence of AI
As part of Kissinger’s warning, it’s been noted just how far-reaching AI’s influence already is in modern life, especially with its usage in innocuous things such as social media algorithms, grammar checkers, and the much-hyped ChatGPT. With the growing dependency on AI, there runs the risk of human thinking being eclipsed by machine-based efficiency and effectiveness. And how it arrives at such efficient and effective decisions becomes questionable because it could become difficult or near impossible to trace what it has learned along the way.
Just imagine someone making a decision influenced by the information fed to them by AI and yet failing to rationalize the thinking behind such a decision. That particular human may not realize it, but at that point they’re living in an AI world, where human decision-making is imitating machine decision-making rather than the reverse. It was this interchangeability Alan Turing was referring to with his famous postulate about artificial intelligence — the so-called “Turing Test” — which holds that you haven’t reached anything that can be fairly called AI until you can’t tell the difference.
Copyright Pavel Danilyuk
Appropriate Use of AI
However, it’s been pointed out that the book doesn’t follow “AI fatalism,” a common belief wherein AI is inevitable and humans are powerless to affect this inevitability. The authors wrote that we are still capable of controlling and shaping AI with our human values, its “appropriate use” as we at Cascade Strategies have been advocating for quite some time. We have the opportunity to limit or restrain what AI learns or align its decision-making with human values.
Kissinger had sounded the warning while others had already made calls to start limiting AI’s capabilities. We are hopeful that in the coming years, with the best modern thinkers and tech experts at the forefront, we progress to more of an AI-assisted world where human agency remains paramount instead of an AI-dominated world where inscrutable decisions are left up to the machines.
Jul
What To Make Of ChatGPT’s User Growth Decline
jerry9789 0 comments artificial intelligence, Burning Questions, Uncategorized
The Beginning Of The End?
More than six months after launching on November 2022, ChatGPT recorded its first decline in user growth and traffic in June 2023. Spiceworks reported that the Washington Post surmised quality issues and summer breaks from schools could have been factors in the decline, aside from multiple companies banning employees from using ChatGPT professionally.
Brad Rudisail, another Spiceworks writer, opined that a subset of curious visitors driven by the hype over ChatGPT could’ve also boosted the numbers of early visits, the dwindling user growth resulting from the said group moving on to the next talk of the town.
The same article also brings up open-source AI gaining ground on OpenAI’s territory as a possible factor, thanks to customizable, faster, and more useful models on top of being more transparent and the decreased likelihood of cognitive biases.
Don’t Buy Into The Hype
But perhaps the best takeaway is Mr. Rudisail’s point that we’re still in the early stages of AI and it’s premature to herald ChatGPT’s downfall with a weak signal like decreased user growth. For all we know, this is what could be considered normal numbers, with earlier figures inflated by the excitement surrounding its launch. Don’t buy into the hype is a position we at Cascade Strategies advocate when it comes to matters of AI.
The advent of AI has taken productivity and efficiency to levels never seen before, so the initial hoopla over it is understandable. However, we believe people are now starting to become a little more settled in their appraisal of AI. They’re starting to see that AI is pretty good at “middle functions” requiring intelligence, whether that be human or machine-based. But when it comes to “higher function” tasks which involve discernment, abstraction and creativity, AI output falls short of excellence. Sometimes mediocrity is acceptable, but for most pursuits excellence is needed.
Excellence Achieved Through High Level Human Thinking
To illustrate just how AI would come out lacking in certain activities, let’s consider our case study for the Gargoyles brand of sunglasses. ChatGPT can produce a large number of ads for sunglasses at little or no cost, but most of those ads won’t bring anything new to the table or resonate with the audience.
However, when researchers spent time with the most loyal customers of Gargoyles to come up with a new ad, they discovered a commonality that AI simply did not have the power to discern. They found a unique quality of indomitability among these brand loyalists: many of them had been struck down somewhere in their upward striving, and they found the strength and resolve to keep going while the odds were clearly against them. They kept going and prevailed. The researchers were tireless in their pursuit of this rare trait, and they stretched the interpretive, intuitive, and synthesis-building capacities of their right brains to find it. Stretching further, they inspired creative teams to produce the award-winning “storyline of life” campaign for the Gargoyles brand.
All told, this is a story of seeking excellence, where hard-working humans press the ordinary capacities of their intellects to higher layers of understanding of a subject matter, not settling for simply a summarization of the aggregate human experience on the topic. This is what excellence is all about, and AI is not prepared to do it. To achieve it, humans have to have a strong desire to go beyond the mediocre. They have to believe that stretching their brains to this level results in something good.
How To Make “Appropriate Use” of AI
But that is not to say that AI and high level human thinking can’t mix. The key is to recognize where AI would best fit in your process and methodologies, then decide where human intervention comes in. This is what we call “Appropriate Use” of AI.
Take for example our case study for Expedia Group and how they engage with millions of hospitality partners. Expedia offers their partner “advice” which helps them receive a booking over their competitors. With thousands of pieces of advice to give their partners, they utilize AI to filter through all those recommendations and present only the best ones to optimize revenue. Cascade Strategies has helped them further by creating a tool called Scenario Analyzer, which uses the underlying AI model to automate the selection of these most revenue-optimal pieces of advice.
Either way, the end decision on which advice to go with (or whether they accept any advice at all) ultimately still comes from Expedia’s partner, not the AI.
Copyright ClaudeAI.uk
A Double-edged Sword
As you can see with ChatGPT, it’s easy to get carried away with all the hype surrounding AI. At launch, it was acclaimed for the exciting possibilities it represented, but now that it has hit a bump in the road, some people and outlets act as if ChatGPT is on its last leg. Hype is good when it’s necessary to draw attention; unfortunately in most cases, it sets up the loftiest of expectations when good sense gets overridden.
This is why we think a sensible mindset is the best way to approach and think about AI — to see it for what it really is. It’s a tool for increasing productivity and efficiency, not the end-all and be-all, as there is still much room for excellent human thinking backed by experience and values to come into play. Our concerns for now may not be as profound and dire as those expressed by James Cameron, Elon Musk, Steve Wozniak and others, but we’d like to believe that “appropriate use” of AI is the key towards better understanding and responsible stewardship of this emerging new technology.
Jun
Appropriate Use of AI
jerry9789 0 comments artificial intelligence, Brand Surveys and Testing, Brandview World, Burning Questions
The Rise Of AI
Believe it or not, Artificial Intelligence has existed for more than 50 years. But as the European Parliament pointed out, it wasn’t until recent advances in computing power, algorithm and data availability accelerated breakthroughs in AI technologies in modern times. 2022 alone made AI relatively mainstream with the sudden popularity of OpenAI’s ChatGPT.
But that’s not to say that AI hasn’t already been incorporated in our daily lives- from web searches to online shopping and advertising, from digital assistants on your smartphones to self-driving vehicles, from cybersecurity to the fight against disinformation on social media, AI-powered applications have been employed to enable automation and increase productivity.
The Woes Of AI
However, the rise of AI also brings concerns and worries over its expanding use across industries and day-to-day activities. Perceived negative socio-political effects, the threat of AI-powered processes taking over human employment, the advent of intelligent machines capable of evolving past their programming and human supervision- that last one is mostly inspired by the realm of science fiction but a plausible possibility nonetheless. A more grounded and present-day concern, however, is the overreliance and misuse of Artificial Intelligence.
Copyright geralt (Pixabay)
Sure, AI is able to perform a variety of simple and complex tasks by simulating human intelligence, efficiently and quickly producing objective and accurate results. However, there are some activities requiring discernment, abstraction and creativity, where AI’s approximation of human thinking falls short. Cognitive exercises like these not only need high-level thinking but also involve value judgments honed and subjected by human experience.
The Expedia Group Case Study
This brings us to our case study for the Expedia Group, whose brand has around a million hospitality partners. Their goal is to increase engagement with their partners. For five years, Expedia grouped their lodging partners, which at the time were mostly chain hotels, with a segmentation model that helped guide their partner sales teams on how they should prioritize spending their time. This “advice” Expedia provides comes through marketing, in-product or through the partner’s account manager. When a partner takes advantage of Expedia’s advice, they usually receive the booking over their competitor.
Copyright geralt (Pixabay)
Now you can imagine that Expedia has thousands of advices or recommendations to give their partners. So how does Expedia determine which recommendation will most likely push their partner to act accordingly and produce optimal revenue?
If you answered “Use AI,” you’re on the right track. With thousands of possible decisions, Expedia just wants AI to filter the bad choices and boil it down to a few but good recommendations optimizing revenue. Expedia wants to use AI to help with decisions, but it doesn’t want AI to make that decision for them or their partners.
Copyright Seanbatty (Pixabay)
But now things are different- Expedia’s partners have grown to also include independent hotels and vacation rentals. So what if Expedia adds additional dimensions to the model allowing them to target partners with recommendations that would be best for their way of thinking and feeling, as well as appeal to their primary motivations as a property?
So that’s exactly where Cascade Strategies stepped in. We followed a disciplined process where — just to name a few things we’ve performed — we interviewed 1200 partners and prospects across 10 countries in 4 regions, converted emotional factors into numeric values and used advanced forms of Machine Learning to arrive at optimal segmentation solutions. Through this five-step disciplined process, we built them a psychographic segmentation formed into subgroups based on patterns of thinking, feeling and perceiving to explain and predict behavior.
Copyright Pavel Danilyuk
It “conceived the game anew” for Expedia Group (in a way suggested by Eric Schmidt and company in their book The Age of AI: And Our Human Future). Now seeing their partners in a different light, they needed to evolve their communications to reflect the new way they view them with the end goal of targeting which segment with which offer. The messages they would deploy should be very action-oriented based on what compels each segment.
Cascade Strategies then created an application called Scenario Analyzer to make this easy for people at Expedia. Its users could just ask the Scenario Analyzer what’s the optimal decision for certain input conditions. Basically, a marketer selects a target group and a region then the Scenario Analyzer answers by saying “You could do any of these six things and you’d make some money. It’s your call.”
If the partner does nothing, Expedia still makes about $1.5 million from these partners during a 90-day period, which is part of their regular business momentum. However, if the partner acts on the top-ranked recommendation which carries the message “Maximize your revenue potential by driving more groups or corporate business to your property,” it would result in about $140,000 more during the same period, which is about a 1% gain. While we couldn’t reach all partners with the same message, causing us to lower our expectations a little, we did slightly better than we expected to do in the end.
The “Appropriate Use” of AI
So what did we did do? We made “Appropriate Use” of AI. It neither made the decision nor guaranteed the money. It warded off the worst ideas and told us which recommendation was best in comparative terms.
Many people in marketing are treating AI as the next cool thing, so they want to jam it in wherever they can, whether it’s helpful or not. “Appropriate Use” stands against that, saying the best way to apply AI to marketing is for Decision Support to remain under human discretion and judgment, instead of letting AI actually make choices.
We think AI can at times be a very poor decision maker but a very good advisor. And we’re not alone as many others share our concern; to illustrate, 61% of Europeans look favorably at AI and robots while 88% say these technologies require careful management.
Another example to consider when thinking about just how important human intervention is when it comes to the “Appropriate Use” of AI is the topic of health care. As noted by frontiersin.org, the legal and regulatory framework may not be well-developed for the practice of medicine and public health in some parts of the world. Throwing artificial intelligence into the mix without careful and thoughtful planning might underscore or aggravate existing health disparities among different demographic groups.
And this is part of the reason why we believe in shaping AI with human values, including the dignity and moral agency of humans. The “defining future technology” that is AI is already proving to be a powerful tool for providing solutions and achieving goals, but it can only unlock levels of excellence, innovation and integrity when guided appropriately by human values and experience.
Other interesting reads:
https://www.wgu.edu/blog/what-ai-technology-how-used2003.html#close
https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp
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