Artificial Intelligence Applications Quotes

Pilots used to fly planes manually, but now they operate a dashboard with the help of computers. This has made flying safer and improved the industry. Healthcare can benefit from the same type of approach, with physicians practicing medicine with the help of data, dashboards, and AI. This will improve the quality of care they provide and make their jobs easier and more efficient

Much of clinician burnout is due to spending time writing notes, placing orders, generating referrals, writing prior authorization letters, and creating patient communication. In other words, burnout is caused by physicians having to generate output! With the emergence of large language models that are used to train generative AI solutions, these use cases will be at the frontier of AI’s applications in healthcare.

Value versus Cost Economists tend to focus on cost, and, as economists, we are as guilty of that as anyone. The entire premise of our first book, Prediction Machines, was that AI advances were going to dramatically reduce the cost of prediction, leading to a scale-up of its use. However, while that book suggested that the initial uses of AI would be where prediction was already occurring, either explicitly in, say, forecasting sales or the weather, or implicitly in classifying photos and language, we were mindful that the real opportunity would be the new applications and uses that were enabled when prediction costs fell low enough.

When writing a novel a writer should create living people; people not characters. A character is a caricature. If a writer can make people live there may be no great characters in his book, but it is possible that his book will remain as a whole; as an entity; as a novel. If the people the writer is making talk of old masters; of music; of modern painting; of letters; or of science then they should talk of those subjects in the novel. If they do not talk of these subjects and the writer makes them talk of them he is a faker, and if he talks about them himself to show how much he knows then he is showing off. No matter how good a phrase or a simile he may have if he puts it in where it is not absolutely necessary and irreplaceable he is spoiling his work for egotism. Prose is architecture, not interior decoration, and the Baroque is over. For a writer to put his own intellectual musings, which he might sell for a low price as essays, into the mouths of artificially constructed characters which are more remunerative when issued as people in a novel is good economics, perhaps, but does not make literature. People in a novel, not skillfully constructed characters, must be projected from the writer’s assimilated experience, from his knowledge, from his head, from his heart and from all there is of him. If he ever has luck as well as seriousness and gets them out entire they will have more than one dimension and they will last a long time. A good writer should know as near everything as possible. Naturally he will not. A great enough writer seems to be born with knowledge. But he really is not; he has only been born with the ability to learn in a quicker ratio to the passage of time than other men and without conscious application, and with an intelligence to accept or reject what is already presented as knowledge. There are some things which cannot be learned quickly and time, which is all we have, must be paid heavily for their acquiring. They are the very simplest things and because it takes a man’s life to know them the little new that each man gets from life is very costly and the only heritage he has to leave. Every novel which is truly written contributes to the total of knowledge which is there at the disposal of the next writer who comes, but the next writer must pay, always, a certain nominal percentage in experience to be able to understand and assimilate what is available as his birthright and what he must, in turn, take his departure from. If a writer of prose knows enough about what he is writing about he may omit things that he knows and the reader, if the writer is writing truly enough, will have a feeling of those things as strongly as though the writer had stated them. The dignity of movement of an ice-berg is due to only one-eighth of it being above water. A writer who omits things because he does not know them only makes hollow places in his writing. A writer who appreciates the seriousness of writing so little that he is anxious to make people see he is formally educated, cultured or well-bred is merely a popinjay. And this too remember; a serious writer is not to be confounded with a solemn writer. A serious writer may be a hawk or a buzzard or even a popinjay, but a solemn writer is always a bloody owl.

Quantum Machine Learning is defined as the branch of science and technology that is concerned with the application of quantum mechanical phenomena such as superposition, entanglement and tunneling for designing software and hardware to provide machines the ability to learn insights and patterns from data and the environment, and the ability to adapt automatically to changing situations with high precision, accuracy and speed. 

Whoever perceives that robots and artificial intelligence are merely here to serve humanity, think again. With virtual domestic assistants and driverless cars just the latest in a growing list of applications, it is we humans who risk becoming dumbed down and ultimately subservient to machines.

The best AI techniques are not the ones most talked about or the most sophisticated; the best AI techniques solve a problem with adherence to business needs and constraints.

Plus, from my experience, for most problems, AI development is the easy part. The hard part? Everything else that goes around it. For example, how do you find the right problems to solve?

This cuts the myth at an awkward angle: it is because the [artificial intelligence] systems are idiots, but still find their way into business, consumer, and government application, that human-value questions are now infecting what were once purely scientific values.

no simple mechanism could do the job as well or better. It might simply be that nobody has yet found the simpler alternative. The Ptolemaic system (with the Earth in the center, orbited by the Sun, the Moon, planets, and stars) represented the state of the art in astronomy for over a thousand years, and its predictive accuracy was improved over the centuries by progressively complicating the model: adding epicycles upon epicycles to the postulated celestial motions. Then the entire system was overthrown by the heliocentric theory of Copernicus, which was simpler and—though only after further elaboration by Kepler—more predictively accurate.63 Artificial intelligence methods are now used in more areas than it would make sense to review here, but mentioning a sampling of them will give an idea of the breadth of applications. Aside from the game AIs

Although inefficiencies present themselves in different ways, AI can be a great tool to automate repetitive and time-consuming work where human-level decision-making is involved.

You can use AI to do unethical things—yes. However, if you think about it, that’s more a function of immoral humans than evil AI.

The good news is that you can minimize the bias preserved by AI, but this comes as a joint responsibility between leaders, domain experts, and developers.

Sebastian Thrun, previously the director of the Stanford Artificial Intelligence Laboratory, and now the head of Google’s autonomous car lab, feels the benefits will be significant. “There are nearly 50 million auto accidents worldwide each year, with over 1.2 million needless deaths. AI applications such as automatic breaking or lane guidance will keep drivers from injuring themselves when falling asleep at the wheel. This is where artificial intelligence can help save lives every day.

There’s only a limited amount that AI researchers can do to influence the evolution of global policy on AI. We can point to possible applications that would provide economic and social benefits; we can warn about possible misuses such as surveillance and weapons; and we can provide roadmaps for the likely path of future developments and their impacts. Perhaps the most important thing we can do is to design AI systems that are, to the extent possible, provably safe and beneficial for humans.

As I have already argued, research on tool AI—those specific, innocuous applications such as game playing, medical diagnosis, and travel planning—often leads to progress on general-purpose techniques that are applicable to a wide range of other problems and move us closer to human-level AI.

One of those was Gary Bradski, an expert in machine vision at Intel Labs in Santa Clara. The company was the world’s largest chipmaker and had developed a manufacturing strategy called “copy exact,” a way of developing next-generation manufacturing techniques to make ever-smaller chips. Intel would develop a new technology at a prototype facility and then export that process to wherever it planned to produce the denser chips in volume. It was a system that required discipline, and Bradski was a bit of a “Wild Duck”—a term that IBM originally used to describe employees who refused to fly in formation—compared to typical engineers in Intel’s regimented semiconductor manufacturing culture. A refugee from the high-flying finance world of “quants” on the East Coast, Bradski arrived at Intel in 1996 and was forced to spend a year doing boring grunt work, like developing an image-processing software library for factory automation applications. After paying his dues, he was moved to the chipmaker’s research laboratory and started researching interesting projects. Bradski had grown up in Palo Alto before leaving to study physics and artificial intelligence at Berkeley and Boston University. He returned because he had been bitten by the Silicon Valley entrepreneurial bug.

Imagine an alternate universe in which people don’t have words for different forms of transportation—only the collective noun “vehicle.” They use that word to refer to cars, buses, bikes, spacecraft, and all other ways of getting from place A to place B. Conversations in this world are confusing. There are furious debates about whether or not vehicles are environmentally friendly, even though no one realizes that one side of the debate is talking about bikes and the other side is talking about trucks. There is a breakthrough in rocketry, but the media focuses on how vehicles have gotten faster—so people call their car dealer (oops, vehicle dealer) to ask when faster models will be available. Meanwhile, fraudsters have capitalized on the fact that consumers don’t know what to believe when it comes to vehicle technology, so scams are rampant in the vehicle sector. Now replace the word “vehicle” with “artificial intelligence,” and we have a pretty good description of the world we live in. Artificial intelligence, AI for short, is an umbrella term for a set of loosely related technologies. ChatGPT has little in common with, say, software that banks use to evaluate loan applicants. Both are referred to as AI, but in all the ways that matter—how they work, what they’re used for and by whom, and how they fail—they couldn’t be more different.

So, will deep learning eventually become “artificial general intelligence” (AGI), matching human intelligence in every way? Will we encounter “singularity” (see chapter 10)? I don’t believe it will happen by 2041. There are many challenges that we have not made much progress on or even understood, such as how to model creativity, strategic thinking, reasoning, counter-factual thinking, emotions, and consciousness. These challenges are likely to require a dozen more breakthroughs like deep learning, but we’ve had only one great breakthrough in over sixty years, so I believe we are unlikely to see a dozen in twenty years. In addition, I would suggest that we stop using AGI as the ultimate test of AI. As I described in chapter 1, AI’s mind is different from the human mind. In twenty years, deep learning and its extensions will beat humans on an ever-increasing number of tasks, but there will still be many existing tasks that humans can handle much better than deep learning. There will even be some new tasks that showcase human superiority, especially if AI’s progress inspires us to improve and evolve. What’s important is that we develop useful applications suitable for AI and seek to find human-AI symbiosis, rather than obsess about whether or when deep-learning AI will become AGI. I consider the obsession with AGI to be a narcissistic human tendency to view ourselves as the gold standard.

When I launched my AI career in 1983, I did so by waxing philosophic in my application to the Ph.D. program at Carnegie Mellon. I described AI as “the quantification of the human thinking process, the explication of human behavior,” and our “final step” to understanding ourselves. It was a succinct distillation of the romantic notions in the field at that time and one that inspired me as I pushed the bounds of AI capabilities and human knowledge. Today, thirty-five years older and hopefully a bit wiser, I see things differently. The AI programs that we’ve created have proven capable of mimicking and surpassing human brains at many tasks. As a researcher and scientist, I’m proud of these accomplishments. But if the original goal was to truly understand myself and other human beings, then these decades of “progress” got me nowhere. In effect, I got my sense of anatomy mixed up. Instead of seeking to outperform the human brain, I should have sought to understand the human heart. It’s a lesson that it took me far too long to learn. I have spent much of my adult life obsessively working to optimize my impact, to turn my brain into a finely tuned algorithm for maximizing my own influence. I bounced between countries and worked across time zones for that purpose, never realizing that something far more meaningful and far more human lay in the hearts of the family members, friends, and loved ones who surrounded me. It took a cancer diagnosis and the unselfish love of my family for me to finally connect all these dots into a clearer picture of what separates us from the machines we build. That process changed my life, and in a roundabout way has led me back to my original goal of using AI to reveal our nature as human beings. If AI ever allows us to truly understand ourselves, it will not be because these algorithms captured the mechanical essence of the human mind. It will be because they liberated us to forget about optimizations and to instead focus on what truly makes us human: loving and being loved. Reaching that point will require hard work and conscious choices by all of us. Luckily, as human beings, we possess the free will to choose our own goals that AI still lacks. We can choose to come together, working across class boundaries and national borders to write our own ending to the AI story. Let us choose to let machines be machines, and let humans be humans. Let us choose to simply use our machines, and more importantly, to love one another.

For a well-defined, standard, and stable process involving hand-offs between people and systems, it is preferable to use a smart workflow platform. Such platforms offer pre-developed modules. These are ready-to-use automation programs customized by industry and by business function (e.g., onboarding of clients in retail banking). In addition, they are modular. For example, a module might include a form for client data collection, and another module might support an approval workflow. In addition, these modules can be linked to external systems and databases using connectors, such as application programming interfaces (APIs), which enable resilient data connectivity. Hence, with smart workflows, there is no need to develop bespoke internal and external data bridges. This integration creates a system with high resiliency and integrity. In addition, the standardization by industry and function of these platforms, combined with the low-code functionality, helps to accelerate the implementation.

For example, regressions support workforce planning to ensure adequate staffing, or the identification of the talents that an organization should retain based on relevant traits, practices, and skills. Other applications include predicting the level of liquidity required for a finance department based on trends in disbursement.

In conclusion, smaller companies with a limited budget might opt for open source platforms. Larger, more established, and more demanding companies would typically go with a commercial platform, enabling them to connect their different applications under the same roof. Companies with high growth objectives should leverage scalable cloud-based platforms.

Digital boards, also known as interactive or electronic whiteboards, have revolutionized the way information is presented and shared in various settings, ranging from classrooms to corporate boardrooms. These sophisticated devices combine the benefits of traditional whiteboards with cutting-edge technology, providing a dynamic and interactive platform for communication. Unlike static whiteboards, digital boards are equipped with touch-sensitive screens that respond to both stylus and finger input, allowing users to write, draw, and manipulate content with ease. This versatility enhances collaboration and engagement, making learning and business meetings more interactive and productive. The potential applications of digital boards are vast, from facilitating remote collaboration to enhancing creative brainstorming sessions. As technology continues to advance, we can expect further innovations in digital board design, with features such as artificial intelligence integration and enhanced interactivity. In essence, digital boards have become indispensable tools in modern communication, fostering dynamic and collaborative environments across educational, professional, and creative domains.

General-purpose AI would be a method that is applicable across all problem types and works effectively for large and difficult instances while making very few assumptions. That’s the ultimate goal of AI research: a system that needs no problem-specific engineering and can simply be asked to teach a molecular biology class or run a government. It would learn what it needs to learn from all the available resources, ask questions when necessary, and begin formulating and executing plans that work.

Members of The Center for AI Safety say that mitigating the risk of extinction from AI should be a global priority, because inventing machines that are more powerful than us is playing with fire. While AI has many beneficial applications, it can be used to perpetuate bias, power autonomous weapons, promote misinformation, and conduct cyberattacks. Even as AI systems are used with human involvement, AI agents are increasingly able to act autonomously to cause harm.

Similarly, one of the most popular HR tools at GE Digital—an early adopter of AI for manufacturing applications—shows workers which jobs in the company are natural next steps from the ones they have now.12 Employees can look privately at the tool to see possible paths they can follow, skills they may need to acquire, or even positions that are open. This helps employees feel that they have more opportunities and that they have more control over their positions in the company. Education

But at the rate artificial intelligence applications are advancing, the technology might catch up in only a few years. The other part of the equation would probably be a brain implant that would allow people to hook into the game without the need for a multimillion-dollar machine and techs to supervise them. The chip will replace both." He poured into their glasses. Brandon chuckled. "That would be game over for humanity and the start of the zombie apocalypse. Imagine what governments could do if they could hack straight into the brains of their citizens. It would be The Matrix." "The masses will live inside an illusion," Kian murmured. "Or die. What would the elites want to do with them? Especially if all the manufacturing could be done by robots, including building the robots themselves?

Before wrapping up this chapter, let us look at one of the deadly scams in the Indian primary market history. There was company named ‘MS shoes east’. Shares of this company traded in Rs 150-200 range throughout the year 1994. But towards December 1994 it spurted to Rs 500 without any justifiable rationale behind the raise. Its promoter Pavan Sachedeva and his broker artificially manipulated the stock price to this level.   By February 1995, the company devised an expansion plan for an estimated expense Rs 700 crores. It proposed to raise around Rs 428 crores by means of Fully convertible bonds. These bonds were to be sold at Rs 199 each through public issue. The idea was to provoke people to subscribe the issue with a hope of converting this bond of Rs 199 to a share of Rs 500.   Well, his brokers was constantly buying the stocks from the open market to maintain the price at that high level. But the situation had already worsened. He had bought too much and had too little money at hand that he could not pay the stock exchange for all the purchases he made. BSE could not give money to the sellers of that security. Things turned out to be serious. You may find it hard to believe  - the BSE was shut down for three consecutive days without any business.   Before this drama came to light, FCD ('Fully Convertible Debenture) public issue was a big success and it almost stole the show. Delighted by the overwhelming response from the investing community, MS Shoes had announced to close the public issues few days before the stipulated time. The world came to know that the cruel plan of manipulating the stock price was only to push the bond issue successfully. Even the authorities woke up to the problem. The company was issued a notice. And also it allowed the investors to take back their FCD application. Almost all the investors took back. Even the underwriter refused to buy the unsold portion of the issue because the company had voluntarily announced to close the issue before the end date. The ruling was in favor the underwriter. Sachedeva declared himself to be innocent. MS shoes office resembled a mourning house with  deserted look.   There was one Sachedeva who came to light. There were and probably still are more of them out there.

The key to Amazon is its increasingly digital operating model. Amazon’s operating philosophy centers on digitizing the best understanding of operational excellence through the broad-based application of artificial intelligence and machine learning, advanced robotics, and the instantiation of as much know-how as possible into software.

This book is a compilation of interesting ideas that have strongly influenced my thoughts and I want to share them in a compressed form. That ideas can change your worldview and bring inspiration and the excitement of discovering something new. The emphasis is not on the technology because it is constantly changing. It is much more difficult to change the accompanying circumstances that affect the way technological solutions are realized. The chef did not invent salt, pepper and other spices. He just chooses good ingredients and uses them skilfully, so others can enjoy his art. If I’ve been successful, the book creates a new perspective for which the selection of ingredients is important, as well as the way they are smoothly and efficiently arranged together. In the first part of the book, we follow the natural flow needed to create the stimulating environment necessary for the survival of a modern company. It begins with challenges that corporations are facing, changes they are, more or less successfully, trying to make, and the culture they are trying to establish. After that, we discuss how to be creative, as well as what to look for in the innovation process. The book continues with a chapter that talks about importance of inclusion and purpose. This idea of inclusion – across ages, genders, geographies, cultures, sexual orientation, and all the other areas in which new ways of thinking can manifest – is essential for solving new problems as well as integral in finding new solutions to old problems. Purpose motivates people for reaching their full potential. This is The second and third parts of the book describes the areas that are important to support what is expressed in the first part. A flexible organization is based on IT alignment with business strategy. As a result of acceleration in the rate of innovation and technological changes, markets evolve rapidly, products’ life cycles get shorter and innovation becomes the main source of competitive advantage. Business Process Management (BPM) goes from task-based automation, to process-based automation, so automating a number of tasks in a process, and then to functional automation across multiple processes andeven moves towards automation at the business ecosystem level. Analytics brought us information and insight; AI turns that insight into superhuman knowledge and real-time action, unleashing new business models, new ways to build, dream, and experience the world, and new geniuses to advance humanity faster than ever before. Companies and industries are transforming our everyday experiences and the services we depend upon, from self-driving cars, to healthcare, to personal assistants. It is a central tenet for the disruptive changes of the 4th Industrial Revolution; a revolution that will likely challenge our ideas about what it means to be a human and just might be more transformative than any other industrial revolution we have seen yet. Another important disruptor is the blockchain - a distributed decentralized digital ledger of transactions with the promise of liberating information and making the economy more democratic. You no longer need to trust anyone but an algorithm. It brings reliability, transparency, and security to all manner of data exchanges: financial transactions, contractual and legal agreements, changes of ownership, and certifications. A quantum computer can simulate efficiently any physical process that occurs in Nature. Potential (long-term) applications include pharmaceuticals, solar power collection, efficient power transmission, catalysts for nitrogen fixation, carbon capture, etc. Perhaps we can build quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning. Perhaps a quantum deep learning network can be trained more efficiently, e.g. using a smaller training set. This is still in conceptual research domain.

By the time I began my Ph.D., the field of artificial intelligence had forked into two camps: the “rule-based” approach and the “neural networks” approach. Researchers in the rule-based camp (also sometimes called “symbolic systems” or “expert systems”) attempted to teach computers to think by encoding a series of logical rules: If X, then Y. This approach worked well for simple and well-defined games (“toy problems”) but fell apart when the universe of possible choices or moves expanded. To make the software more applicable to real-world problems, the rule-based camp tried interviewing experts in the problems being tackled and then coding their wisdom into the program’s decision-making (hence the “expert systems” moniker). The “neural networks” camp, however, took a different approach. Instead of trying to teach the computer the rules that had been mastered by a human brain, these practitioners tried to reconstruct the human brain itself. Given that the tangled webs of neurons in animal brains were the only thing capable of intelligence as we knew it, these researchers figured they’d go straight to the source. This approach mimics the brain’s underlying architecture, constructing layers of artificial neurons that can receive and transmit information in a structure akin to our networks of biological neurons. Unlike the rule-based approach, builders of neural networks generally do not give the networks rules to follow in making decisions. They simply feed lots and lots of examples of a given phenomenon—pictures, chess games, sounds—into the neural networks and let the networks themselves identify patterns within the data. In other words, the less human interference, the better.

Amazon Comprehend is a natural language processing (NLP) solution that uses machine learning to find and extract insights and relationships from documents. •​Amazon Forecast combines your historical data with other variables, such as weather, to forecast outcomes. •​Amazon Kendra is an intelligent search service powered by machine learning. •​Amazon Lex is a solution for building conversational interfaces that can understand user intent and enable humanlike interactions. •​Amazon Lookout for Metrics detects and diagnoses anomalies in business and marketing data, such as unexpected drops in sales or unusual spikes in customer churn rates. •​Amazon Personalize powers personalized recommendations using the same machine-learning technology as Amazon.com. •​Amazon Polly converts text into natural-sounding speech, enabling you to create applications that talk. •​Amazon Rekognition makes it possible to identify objects, people, text, scenes, and activities in images and videos. •​Amazon Textract automatically reads and processes scanned documents to extract text, handwriting, tables, and data. •​Amazon Transcribe converts speech to text. •​Amazon Translate uses deep-learning models to deliver accurate, natural-sounding translation.

We became an intelligent species by solving problems, and now that we are entering a technological era where we no longer need to solve problems on our own, leaving the key physiological functions of running the body, eventually the brain itself will become a vestigial organ, like the appendix. As we no longer need to think and act on our own, the cortex will begin to shrink, quite like unused muscle, and eventually, once again after millions of years, the primeval lizard brain, i.e. the limbic brain will gain full control of the new human animal. The rise of AI will be the end of "I". But there is also another side to the picture. It's that, we cannot achieve much more, as a species, than what we already have, without the application of AI. So, the question is not whether AI is good for us - the real question is, are we mature enough to use AI for good.

AI - The Whole Picture In medicine, we have a condition called oxygen toxicity, which means, even oxygen can do harm if inhaled excessively. Imagine that - we usually associate oxygen with life, yet that very oxygen can literally kill you if your lungs are overexposed to it. The same is going to happen with our brain from unrestrained use of AI. With the rise of AI, machines may or may not become sentient, but one thing is for certain - human mind will soon turn into vegetable. We became an intelligent species by solving problems, and now that we are entering a technological era where we no longer need to solve problems on our own, leaving the key physiological functions of running the body, eventually the brain itself will become a vestigial organ, like the appendix. As we no longer need to think and act on our own, the cortex will begin to shrink, quite like unused muscle, and eventually, once again after millions of years, the primeval lizard brain, i.e. the limbic brain will gain full control of the new human animal. The rise of AI will be the end of "I". But there is also another side to the picture. It's that, we cannot achieve much more, as a species, than what we already have, without the application of AI. So, the question is not whether AI is good for us - the real question is, are we mature enough to use AI for good. So how do we use AI without destroying ourselves? Here's how. Use AI to enhance capacity, not to avoid difficulty. Use AI to accomplish tasks that are otherwise impossible. Prioritize AI to solve real-life problems, not to make life more comfortable.

Let's imagine we're standing together on the launch pad at NASA's Cape Canaveral facility near Orlando, and staring up at the stars together. As I write this, the last constellation above the horizon is Centaurus. The centaur's front head is a bright star. In fact, it's three stars—a pair called Alpha Centauri A and B, and, dimmest of the trio, Proxima Centauri. Here, look through this telescope. See? You can tell them apart. But what we can't see is that there is, in fact, a planet circling the faint light of Proxima Centauri. Man, I wish we could see it. Because that planet, Proxima Centauri b, is the nearest known exoplanet to Earth. [...] If we were to board a spacecraft and ride it from the outer edge of our atmosphere all the way to Proxima Centauri b, you and I, who boarded the ship fit and trim, chosen as we were from billions of applicants, would die before the voyage reached even 1/100th of the intervening distance. [...] At a speed of 20,000 miles per hour—the speed of our top-performing modern rockets—4.2 light years translates to more than 130,000 years of space travel. [...] So how will we ever get there? A generation ship. [...] the general notion is this: get enough human beings onto a ship, with adequate genetic diversity among us, that we and our fellow passengers cohabitate as a village, reproducing and raising families who go on to mourn you and me and raise new of their own, until, thousands of years after our ship leaves Earth's gravity, the distant descendants of the crew that left Earth finally break through the atmosphere of our new home. [...] A generation ship is every sociological and psychological challenge of modern life squashed into a microcosmic tube of survival and amplified—generation after generation. [...] The idea of a generation ship felt like a pointless fantasy when I first encountered it. But as I've spent the last few years speaking with technologists, academics, and policy makers about the hidden dangers of building systems that could reprogram our behavior now and for generations to come, I realized that the generation ship is real. We're on board it right now. On this planet, our own generation ship, we were once passengers. But now, without any training, we're at the helm. We have built lives for ourselves on this planet that extend far beyond our natural place in this world. And now we are on the verge of reprogramming not only the planet, but one another, for efficiency and profit. We are turning systems loose on the decks of the ship that will fundamentally reshape the behavior of everyone on board, such that they will pass those behaviors on to their progeny, and they might not even realize what they've done. This pattern will repeat itself, and play out over generations in a behavioral and technological cycle.

Successful AI initiatives start with the right problems, but the right problems don’t necessarily come from your data scientists. They can come from leaders, domain experts, and innovators who sit close to the daily business challenges in your organization. Still, it takes practice to develop the vision for spotting AI opportunities...

AI can be a great tool to automate repetitive and time-consuming work where human-level decision-making is involved.

You can use AI to do unethical things—yes. However, if you think about it, that’s more a function of immoral humans than evil AI. (For example, humans can use AI to make fake videos, easily depicting someone as saying something inflammatory when they didn’t. The AI is just doing what it’s tasked to do.)

Overall, the more ambitious your vision, the more risk and time it can take to see any meaningful results from AI. Plus, some ideas may be so complex that they will never materialize until newer supporting technologies emerge. Ultimately, AI requires long-term commitment and a willingness to adapt with time.

Just as how unconscious bias can seep into algorithms, so can conscious bias. Conscious bias happens when we know we’re being biased toward a particular person or a group of people. Although this is rare in AI, the threat is always there.

A large corporation may have a big budget. One or two years of not having sufficient work for their full-time data scientists may be a risk worth taking to “test” AI and establish best practices.

In human terms, think of AI as an ‘intelligent machine.’ While various definitions exist, at its core, AI is about machines performing tasks typically done by humans, predominantly making predictions. Will you buy a new dress? What’s tomorrow’s weather? Will a stock price rise or fall? Is that a cat or a dog in the picture? Draft an email for a job application or even paint a picture of your favorite vacation spot in the style of Monet.

The world of artificial intelligence is developing rapidly, and the competition between the most advanced AI models is fiercer than ever. Three of the biggest players at the top in 2025 are DeepSeek AI, ChatGPT, and Gemini. Each model has different features, capabilities, and applications that distinguish them. But which one is truly the best? Let’s take a closer look at a comparison to see how these models compare with each other.

The world of artificial intelligence is developing rapidly, and the competition between the most advanced AI models is fiercer than ever. Three of the biggest players at the top in 2025 are DeepseekvsChatGptvsgemini. Each model has different features, capabilities, and applications that distinguish them. But which one is truly the best? Let’s take a closer look at a comparison to see how these models compare with each other.

Usually it takes 4 to 6 years to become a doctor, and another 5 to 10 years to become a specialist. Trying to remember what the world looked like 4 or 6 years ago I recall no widespread use of smartphones, tablets, Google Glass, social media, or artificial intelligence. That is how much the world can change in a few years’ time. The rate of change is even faster now. Current curriculum does not prepare students for these even though new applications and technologies are becoming a crucial part of the medical profession. It is time to change and actually re–think the basics of what we call medical education.

Other firms are working on marketing applications of machine learning that increase customer engagement. Macy’s, for example, is working with both IBM’s Watson and Cognitive Scale, an Austin-based AI vendor, to improve personalization and engagement on its website and mobile app.

While the research and applications are in its early days, many experts see probabilistic programming as an alternative approach in areas where deep learning performs poorly, such as concept formulation using sparse or medium-sized data. Probabilistic programs have been used successfully in applications such as medical imaging, machine perception, financial predictions, and econometric and atmospheric forecasting.

I’m not suggesting that neural networks are easy. You need to be an expert to make these things work. But that expertise serves you across a broader spectrum of applications. In a sense, all of the effort that previously went into feature design now goes into architecture design and loss function design and optimization scheme design. The manual labor has been raised to a higher level of abstraction.

less than two months after Ke Jie resigned his last game to AlphaGo, the Chinese central government issued an ambitious plan to build artificial intelligence capabilities. It called for greater funding, policy support, and national coordination for AI development. It set clear benchmarks for progress by 2020 and 2025, and it projected that by 2030 China would become the center of global innovation in artificial intelligence, leading in theory, technology, and application. By 2017, Chinese venture-capital investors had already responded to that call, pouring record sums into artificial intelligence startups and making up 48 percent of all AI venture funding globally, surpassing the United States for the first time.

Online Courses: Platforms like Coursera and edX offer courses on AI fundamentals and applications. Podcasts: Check out “AI Alignment Podcast” and “The TWIML AI Podcast” for insights and discussions. Webinars: Attend sessions hosted by AI experts, often available through tech companies or educational institutions. Community Forums: Engage with others on sites like Reddit’s r/MachineLearning or AI-focused groups on LinkedIn for diverse conversations and support.

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HMC and HBM Market to Reach USD 49.49 Billion by 2032, Growing at a CAGR of 32.9% from USD 5.08 Billion in 2024 Market Overview According to Maximize Market Research, the global HMC and HBM market was valued at USD 5.08 billion in 2024 and is projected to reach USD 49.49 billion by 2032, growing at a CAGR of 32.9% from 2025 to 2032. This exponential growth underscores the increasing adoption of advanced memory solutions across various industries. Understanding HMC and HBM Hybrid Memory Cube (HMC): HMC utilizes a 3D-stacked architecture, connecting multiple memory layers vertically through through-silicon vias (TSVs). This design offers higher bandwidth and lower latency compared to traditional DRAM. High-Bandwidth Memory (HBM): HBM also employs 3D stacking but is integrated closely with processing units, such as GPUs and CPUs, enabling rapid data transfer and reduced power consumption. Key Drivers of Market Growth Artificial Intelligence and Machine Learning: The surge in AI and ML applications necessitates memory solutions that can handle vast datasets with speed and efficiency. HMC and HBM meet these requirements, facilitating faster training and inference processes. High-Performance Computing (HPC): Scientific simulations, financial modeling, and other HPC tasks demand rapid data processing. The enhanced bandwidth and reduced latency of HMC and HBM make them ideal for these applications. Data Centers and Cloud Computing: As data centers strive for higher performance and energy efficiency, integrating advanced memory technologies becomes crucial. HMC and HBM contribute to achieving these goals by optimizing data throughput and reducing power consumption. Regional Insights The Asia-Pacific region dominates the HMC and HBM market, driven by the presence of major semiconductor manufacturers and increasing investments in AI and HPC infrastructure. Countries like China, South Korea, and Japan are at the forefront, with significant contributions from companies such as Samsung, SK Hynix, and Micron. Challenges and Opportunities While the benefits of HMC and HBM are substantial, challenges such as high production costs and thermal management issues persist. However, ongoing research and development efforts aim to address these concerns, paving the way for broader adoption and innovation in memory technologies.

For the next two thousand years, the ideas of Plato, and the reformulations of them that followed, would be repeatedly tested and found wanting on the anvil of application. It was Aristotle’s formula that would eventually reign supreme,

For example, an AI utilized in the mortgage industry may look at an application to decide credit worthiness of a person in order to approve them for a loan. For simplicity, let’s say the system looks at only a hundred descriptors of the applicant and utilizes a neural network to arrive at a binary approval decision. An explanation which included all hundred features and weights of the neural network would not be very useful, so the system may instead select one of the two most important features and explain its decision with respect to just those top properties, ignoring the rest. This highly simplified explanation would not be accurate as the other 98 features all contributed to the decision and if only one or two top features were considered the decision could have been different. This is similar to how principal component analysis works for dimensionality reduction [75].

Natural Language Generation - An Overview | Yellowfin Natural Language Generation (NLG) is a branch of artificial intelligence that focuses on transforming structured data into human-readable text. By using algorithms and linguistic rules, NLG systems can produce coherent narratives, summaries, and reports, enhancing communication in various applications like chatbots, automated journalism, and personalized content creation. This technology improves efficiency and accessibility in data interpretation. For more information, visit Yellowfin blog.

**AI Technology for Good** is a transformative force, leveraging artificial intelligence to address global challenges such as climate change, healthcare disparities, and poverty. By using AI to optimize resource management, predict environmental changes, and enhance healthcare systems, we can drive positive societal impact and improve quality of life for communities worldwide. Whether through predictive models for disease outbreaks or optimizing energy consumption, **AI technology for good** has the potential to solve complex social issues and create a sustainable future. **Artificial Intelligence ethics** plays a crucial role in ensuring that AI technologies are developed and deployed responsibly. Ethical considerations like fairness, transparency, and accountability are essential to prevent AI systems from perpetuating biases or violating privacy. AI developers must ensure that algorithms are free from discrimination, are understandable, and operate with clear accountability mechanisms to maintain public trust. One of the most exciting applications of AI is in **smart cities**. AI is helping urban centers become more efficient, sustainable, and livable. From managing traffic flow and reducing congestion to optimizing energy use and enhancing public safety, AI is central to the development of smart cities. By integrating AI technologies, cities can offer better services, reduce environmental impact, and create a higher standard of living for their residents.

As AI increasingly integrates into various industries, engaging students in problem‐ and project‐based learning will prepare them for real‐world applications of AI. Because problem‐ and project‐based learning foster essential skills such as critical thinking, collaboration, communication, and creativity, they are crucial for success in an AI‐driven workforce. Similarly, engaging students in complex, open‐ended projects helps build adaptability and resilience — qualities we outlined as critical in an AI‐driven world. Problem‐ and project‐based learning also help foster the interdisciplinary thinking discussed in Chapter 3, encouraging students to draw on knowledge and skills from multiple disciplines to solve complex challenges. Finally, both learning approaches instill a growth mindset and promote lifelong learning through trial and error, and solution‐based thinking.

Top Skills Australia Wants for the Global Talent Visa The Global Talent Visa (subclass 858) is one of Australia’s most prestigious visa programs, designed to attract highly skilled professionals who can contribute to the country’s economy and innovation landscape. Australia is looking for exceptional talent across various sectors to support its economic growth, technological advancements, and cultural development. If you’re considering applying for the Global Talent Visa, understanding the skills in demand will help you position yourself as a strong candidate. In this blog, we’ll outline the top skills and sectors Australia prioritizes for the Global Talent Visa, and why these skills are so valuable to the country’s future development. 1. Technology and Digital Innovation Australia is rapidly embracing digital transformation across industries, and the technology sector is one of the highest priority areas for the Global Talent Visa. Skilled professionals in cutting-edge technologies are highly sought after to fuel innovation and help Australia stay competitive in the global economy. Key Tech Skills in Demand: Cybersecurity: With increasing cyber threats globally, Australia needs experts who can safeguard its digital infrastructure. Cybersecurity professionals with expertise in network security, data protection, and ethical hacking are in high demand. Software Development & Engineering: Australia’s digital economy thrives on skilled software engineers and developers. Professionals who are proficient in programming languages like Python, Java, and C++, or who specialize in areas such as cloud computing, DevOps, and systems architecture, are highly valued. Artificial Intelligence (AI) & Machine Learning (ML): AI and ML are transforming industries ranging from healthcare to finance. Experts in AI algorithms, natural language processing, deep learning, and neural networks are in demand to help drive this technology forward. Blockchain & Cryptocurrency: Blockchain technology is revolutionizing sectors like finance, supply chains, and data security. Professionals with expertise in blockchain development, smart contracts, and cryptocurrency applications can play a key role in advancing Australia's digital economy. 2. Healthcare and Biotechnology Australia has a robust and expanding healthcare system, and the country is heavily investing in medical research and biotechnology to meet the needs of its aging population and to drive innovation in health outcomes. Professionals with advanced skills in biotechnology, medtech, and pharmaceuticals are crucial to this push. Key Healthcare & Bio Skills in Demand: Medical Research & Clinical Trials: Australia is home to a growing number of research institutions that focus on new treatments, vaccines, and therapies. Researchers and professionals with experience in clinical trials, molecular biology, and drug development can contribute to the ongoing advancement of Australia’s healthcare system. Biotechnology & Genomics: Experts in biotechnology, particularly those working in genomics, gene editing (e.g., CRISPR), and personalized medicine, are highly sought after. Australia is investing heavily in biotech innovation, especially for treatments related to cancer, cardiovascular diseases, and genetic disorders. MedTech Innovation: Professionals developing the next generation of medical technologies—ranging from diagnostic tools and medical imaging to wearable health devices and robotic surgery systems—are in high demand. If you have experience in health tech commercialization, you could find significant opportunities in Australia.

Another application that may be particularly vulnerable to adversarial attack is fingerprint reading. A team from New York University Tandon and Michigan State University showed that it could use adversarial attacks to design what it called a masterprint—a single fingerprint that could pass for 77 percent of the prints in a low-security fingerprint reader.14 The team was also able to fool higher-security readers, or commercial fingerprint readers trained on different datasets, a significant portion of the time. The masterprints even looked like regular fingerprints—unlike other spoofed images that contain static or other distortions—which made the spoofing harder to spot.

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