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12 shots at staying ahead of AI in the workplace – pharmaphorum

Oliver Stohlmanns Corporate Survival Hacks series draws on his experiences of working in local, regional, and global life sciences communications to offer some little tips for enjoying a big business career. In this update, he shares expectations on how artificial intelligence (AI) may impact our workplaces and what we may do to leverage this trend for the benefit of both people and business.

Regardless of where you are on the corporate ladder, whether you know it or not, your life is going to change; dramatically, fast.

Indications of what artificial intelligence (AI) is already able to do and how its broader application will change our work environment are mind-boggling. What well experience in the next five to ten years is a massive explosion of AI usage in nearly all areas of life.

The beginning of the beginning?

A few examples? Generating flawless text or images is no longer an issue of skill or knowledge. Most AI-generated results are so impressive that a number of people and professions are already impacted by this.

As a teacher or university lecturer, it hardly makes sense today to have students draft their own essays or academic papers. According to Nature, it has become impossible even for scientists to differentiate with certainty between AI-created and original abstracts.

At a recent marketing seminar I was involved in, not one of 36 business students was able to provide a superior and better structured answer than ChatGPT to the question, Please explain SWOT analysis. Try for yourself.

Authentic voice and imagery

In the US, the start-up DoNotPay was about to run a pilot in February in which AI would represent a client in a speeding case court hearing. The chatbot would run on a smartphone, listening to what was being said in court, before whispering instructions into the defendants earpiece on how to best answer the judges questions. The experiment got stopped at the last minute by state bar associations concerned about the robot lawyer practicing law without a license. However, if these objections can be resolved, this may be the way forward in many comparable settings. Its not a matter of AI capability.

If you cannot or do not wish to attend meetings in person, VALL-E is able to read any text in your voice and tonality, or anyone elses. All you need to do is submit a three-second original voice sample. Soon the human ear will not be able to differentiate between the authentic sound of a persons voice and AI imitations of it.

DALL-E2 is an AI system that can create realistic images and artwork in line with your exact specifications, from your description in natural language. The need for graphic designers, photographers, illustrators, and even classic painters will fade.

Shifting from the what to the how

In the future, the best speakers will be those able to authentically repeat what those little ear pods tell them with exceptional charisma, intonation, natural gestures, and facial expressions. Neither content nor expertise will be a bottleneck. An AI-enabled speaker will be able to talk about absolutely any subject on any level of expertise. And yes, theyll be able to answer any question, too, even the provocative ones.

The best business consultants, trainers, and leadership coaches will be those with outstanding social, didactic, and motivational skills. Professional education will continue to matter, but it will focus much more on supporting executives on how to run their business, team, and customer relations; not on transferring knowledge. Being an expert knowledgeable on the what will not suffice. Most consultants, trainers, and coaches will be replaced by social learning environments. Facilitators may guide customised knowledge acquisition, while coaches and consultants will largely focus on optimising executives acumen, personality, and other soft components of effective leadership.

More human in Human Resources

The best people managers will be those who naturally adopt and apply the latest intelligence on people management that their employers AI-powered HR function equips them with. Human touch will not be lacking. Itll be delivered in a personalised way allowing the manager to tailor their approach to different team members of diverse engagement drivers and needs. Data collection and evaluation will run fully automated in the background, providing the manager with individual strength assessments, goal recommendations, performance tracking, corrective interventions, and development recommendations customised to each team member while calibrating across large organisations in real time.

The best HR representatives will be those who lend these automated processes and decisions a trustworthy, fair, and human face. Decisions will be facilitated and employee conversations prepared flawlessly by AI systems running in the background. The number of real people employed in human resources will shrink. Those left, however, will primarily focus on interfacing with internal clients and employees. The quality of these interactions, and that of preparing materials and compelling scripts to enable powerful conversations, will materially increase.

Language creation and translation

The best writers will be Whoops, I started this sentence wrong: therell be no need for writers. Or very few, outstanding ones at best. Already today, AI-generated texts are of a quality, clarity, and artistic beauty that beats 80% of human professional writers. Try it out: ask ChatGPT to draft an introduction for the website of company Human Hips that designs and replaces human hip implants. See what happens.

I just made up that company name. If it existed, they may use the resulting draft for their website straight. Yes, it could be improved by a great writer, more details added reflecting the specialty offerings of that enterprise. However, AI is on track to producing superior texts compared to most human writers, based on minimal input and cost, and faster than anyone else could.

The best translators will be Sorry, got this wrong again: translators will disappear. AI already supplies great, and will deliver perfect, translations into any and all global languages in split seconds, for any length and complexity of written or spoken word. Roles that translate texts or simultaneously translate the spoken word will be a concept of the past.

Seizing the AI revolution

The best employees those who retain well-paid jobs and climb the career ladder will be those able to competently navigate the avalanche of AI-led and augmented applications. They can select the relevant ones to add business value and adapt key features to meet specific business and customer needs. Theyre able to utilise AI to achieve outcomes faster and more efficiently, at lower cost and better quality than whats imaginable today.

The best executives will be algorithm-based. Of course, its a scary prospect to remove thinking humans with deep background and long experience from the positions of power. However, just imagine how much better, faster, fairer, and more ethical fact-based decision-making could become once typical human flaws are removed from the equation. These may include ones individual values and beliefs, ideologies, biases, personal relationships, and interdependencies, including corruption and other temptations; plus cultural and institutional norms, value-systems, expectations, and the pressures typically resulting from those. Scary, but likely in the future.

The best politicians will be You get my drift!

But theres an upside - many, actually

I would be mistaken if I didnt at least briefly point out the phenomenally positive, life-enhancing, and sometimes life-saving opportunities AI brings to society, too.

Apart from GPS systems navigating us to destinations safely, faster, and more reliably, our cars are already equipped with lots of other AI-based safety features that serve to prevent accidents before they happen. An armada of sensors connected and communicating with smart control centres is constantly watching not only over the cars we use, but buses, trains, ships, planes, trucks, agricultural machinery, etc., to keep operations, passengers, and freight safe. They also make sure that buildings, roads, rail tracks, bridges, tunnels, airports, harbours, stations, wind turbines, and all other infrastructure is constantly monitored and gets maintained preventatively before fatigue, vibrations, climate, or other forces can lead to damage or disaster.

As much as I dont like the idea of machines taking over, they most certainly make safer drivers than I am. My future driverless car wont get distracted, nor will it become tired, and it will be able to detect nearing obstacles, stopping traffic, or the deer about to cross the road earlier than I could. In the same way, pilots have been using autopilots for years that cannot only keep planes stable in the air, but also take off and land them safely in the harshest weather conditions.

Human health: an AI beneficiary

In medicine, AI-augmented surgery can already operate more precisely than the human hand could, with trained physicians informing and supervising the process and intervening as needed. Implants are being precision-measured, designed to your individual specifications, and a unique product tailormade to provide an optimal, long-lasting fit. Thats not to mention the fast, minimally invasive precision-surgery that spares patients pain and time, while reducing hospital capacity and cost.

Innovative medical therapies will be designed, developed, and clinically trialled much faster driven by AI-led processes, and made available to the right patients, who benefit from treatment and who will have been pre-determined with the aid of biomarkers or other tests conducted by means of you guessed it AI at rocket speed and precision.

These are just examples. The fast-increasing use of AI will radically change the way we work and live. But it will also usher in a world of opportunities that we and future generations will greatly benefit from.

Buckle up!

However, in case you find the above scenarios unsettling: most do not even touch on the true potential of artificial intelligence. What weve been talking about, so far, is mostly the seamless automation of individual steps and processes so that results can be achieved faster, more efficiently, and more accurately than any human brain could.

Fasten your seatbelts for when true self-learning algorithms with the capacity and capability to continuously learn from errors and instantly apply their insights to improve approaches in real-time are ready for mass application.

For instance, DeepMinds AlphaGo system, who apologies: that famously defeated the worlds Go champion Le Se-dol in 2016. Three years later, the South Korean attributed his retirement from the complex board game to the rise of AI, saying that it was an entity that cannot be defeated.

Well, for a bit of hope, read this recent update on how the story continued with a comprehensive defeat of a top-ranked AI system in the same game. However, you may also notice even that human victory over AI was owed to yet more artificial intelligence support

Whichever way you look at the rise of AI, its diverse applications, future possibilities, or the potential need for regulation: its going to be a fast ride.

About the author

Oliver Stohlmann is a communications leader with more than 20 years experience of working at local, regional, and global levels for several of the worlds premier life sciences corporations. Most recently, he was Johnson & Johnsons global head of external innovation communication. He currently works for Exscientia plc and as an independent leadership coach, trainer, team-developer, and communications consultant.

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12 shots at staying ahead of AI in the workplace - pharmaphorum

Hypotheses and Visions for an Intelligent World – Huawei

As we move towards an intelligent world, information sensing, connectivity, and computing are becoming key. The better knowledge and control of matter, phenomena, life, and energy that result from these technologies are also becoming increasingly important. This makes rethinking approaches to networks and computing critical in the coming years.

In terms of networks, about 75 years ago Claude Shannon proposed his theorems based on three hypotheses: discrete memoryless sources, classical electromagnetic fields, and simple propagation environments. But since then, the industry has continued to push the boundaries of his work.

In 1987, Jim Durnin discovered self-healing non-diffracting beams that could continue to propagate when encountering an obstruction.

In 1992, L. Allen et. al. postulated that the spin and orbital angular momentum of an electromagnetic field has infinite orthogonal quantum states along the same propagation direction, and each quantum state can have one Shannon capacity.

After AlphaGo emerged in 2016, people realized how well foundation models can be used to describe a world with prior knowledge. This means that much information is not discrete or memoryless.

With the large-scale deployment of 5G Massive MIMO in 2018, it has become possible to have multiple independent propagation channels in complex urban environments with tall buildings, boosting communications capacity.

These new phenomena, knowledge, and environments are helping us break away from the hypotheses that shaped Shannon theorems. With them, I believe we can achieve more than 100-fold improvement in network capabilities in the next decade.

In computing, intelligent applications are developing rapidly, and AI models in particular are likely to help solve the fragmentation problems that are currently holding AI application development back. This is driving an exponential growth in model size. Academia and industry have already begun exploring the use of AI in domains like software programming, scientific research, theorem verification, and theorem proving. With more powerful computing models, more abundant computing power, and higher-quality data, AI will be able to better serve social progress.

AI capabilities are improving rapidly, and so we need to consider how to ensure AI development progresses in a way that benefits all people and ensures that AI execution is accurate and efficient. In addition to ethics and governance, AI also faces three big challenges from a theoretical and technical perspective: AI goal definition, accuracy and adaptability, and efficiency.

The first challenge AI faces is that there is no agreed upon definition of its goals. What kind of intelligence do we need?

If there is no clear definition, it is difficult to ensure that the goals of AI and humanity will be aligned and to make reasonable measurements and classifications and scientific computations. Professor Adrian Bejan, a physicist at Duke University, summarizes more than 20 goals for intelligence in his book The Physics of Life, including understanding and cognitive ability, learning and adaptability, and abstract thinking and problem-solving ability. There are many schools of AI, which are poorly integrated. One important reason for this is there are no commonly agreed upon goals for AI.

The second challenge AI faces is accuracy and adaptability. Learning based on statistical rules extracted from big data often results in non-transparent processes, unstable results, and bias. For example, when recognizing a banana using statistical and correlation-based algorithms, an AI system can be easily affected by background combinations and tiny noises. If other pictures are put next to it, the banana may be recognized as an oven or a slug. These pictures can be easily recognized by people, but AI makes these mistakes and it is difficult to explain or debug them.

The third challenge for AI is efficiency. According to the 60th TOP500 published in 2022, the fastest supercomputer is Frontier, which can achieve 1,102 PFLOPS while using 21 million watts of energy. Human brains, in contrast, can deliver about 30 PFLOPS with just 20 watts. These numbers show that the human brain is about 30,000 to 100,000 times more energy efficient than a supercomputer.

In addition to energy efficiency, data efficiency is also a major challenge for AI. It is true that we can better understand the world by extracting statistical laws from big data. But can we find logic and generate concepts from small data, and abstract them into principles and rules?

We have come up with several hypotheses to address these three challenges:

Starting from these hypotheses, we can begin to take more practical steps to develop knowledge and intelligence.

At Huawei, our first vision is to combine systems engineering with AI to develop accurate, autonomous, and intelligent systems. In recent years, there has been a lot of research in academia about new AI architectures that go beyond transformers.

We can build upon these thoughts by focusing on three parts: perception and modeling, automatic knowledge generation, and solutions and actions. From there, we can develop more accurate, autonomous, and intelligent systems through multimodal perception fusion and modeling, as well as knowledge and data-driven decision-making.

Perception and modeling are about representations and abstractions of the external environment and ourselves. Automatic knowledge generation means systems will need to integrate the existing experience of humans into strategy models and evaluation functions to increase accuracy. Solutions can be directly deduced based on existing knowledge as well as internal and external information, or through trial-and-error and induction. We hope that these technologies will be incorporated into future autonomous systems, so that they can better support domains like autonomous driving networks, autonomous vehicles, and cloud services.

Our second vision is to create better computing models, architectures, and components to continuously improve the efficiency of intelligent computing. I once spoke with Fields Medalist Professor Laurent Lafforgue about whether invariant object recognition could be made more accurate and efficient by using geometric manifolds for object representation and computing in addition to pixels, which are now commonly used in visual and spatial computing.

In their book Neuronal Dynamics, co-authors Gerstner, Kistler, Naud, and Paninski at cole Polytechnique Fdrale de Lausanne (EPFL) explain the concept of functional columns in the cerebral cortex and the six-layer connections between these functional columns. It makes me wonder: Can such a shallow neural network be more efficient than a deep neural network?

A common bottleneck for today's AI computing is the memory wall. Reading, writing, and migrating data often takes 100-times more time than computing itself. So, can we possibly bypass conventional processors, instruction sets, buses, logic components, and memory components under von Neumann architecture, and redefine architectures and components based on advanced AI computing models instead?

Huawei has been exploring this idea by looking into the practical uses of AI. First, we have worked on "AI for Industry", which uses industry-specific large models to create more value. Industries face many challenges when it comes to AI application development. They need to invest a huge amount of manpower to label samples, find it difficult to maintain models, and lack the necessary capabilities in model generalization. Most simply they do not have the resources to do this.

To address these challenges, Huawei has developed L1 industry-specific large models based on its L0 large foundation models dedicated to computer vision, natural language processing, graph neural networks, and multi-modal interactions. These large models lower the barrier to AI development, improve model generalization, and address application fragmentation. The models are already being used to improve operational efficiency and safety in major industries like electric power, coal mining, transportation, and manufacturing.

Huawei's Aviation & Rail Business Unit, for example, is working with customers and partners in Hohhot, Wuhan, Xi'an, Shenzhen, and Hongkong to explore the digital transformation of urban rail, railways, and airports. This has improved operational safety and efficiency, as well as user experience and satisfaction. The Shenzhen Airport has realized smart stand allocation with the support of cloud, big data, and AI, reducing airside transfer bus passenger flow by 2.6 million every year. The airport has become a global benchmark in digital transformation.

"AI for Science" is another initiative that will be able to greatly empower scientific computing. One example of this in action is the Pangu meteorology model we developed using a new 3D transformer-based coding architecture for geographic information and a hierarchical time-domain aggregation method. With a prior knowledge of global meteorological phenomena, the Pangu model uses more accurate and efficient learning and reasoning to replace time series solutions of hyperscale partial differential equations using traditional scientific computing methods. The Pangu model can produce 1-hour to 7-day weather forecasts in just a few seconds, and its results are 20% more accurate than forecasts from the European Centre for Medium-Range Weather Forecasts.

AI can also support software programming. In addition to using AI to do traditional retrieval and recommendation in a large amount of existing code, Huawei is developing new model-driven and formal methods. This is especially important for large-scale parallel processing, where many tasks are intertwined and correlated. Huawei has developed a new approach called Vsync which realizes automatic verification and concurrent code optimization of operating system kernels, and improves performance without undermining reliability. The Linux Community once discovered a difficult memory barrier bug which took community experts more than two years to fix. With Huawei's Vsync method, however, it would have taken just 20 minutes to discover and fix the bug.

We have also been studying new computing models for automated theorem proving. Topos theory, for example, can be used to research category proving, congruence reasoning systems, and automated theorem derivation to improve the automation level of theorem provers. In doing this, we want to solve state explosion and automatic model abstraction problems and improve formal verification capabilities.

Finally, we are also exploring advanced computing components. We can use the remainder theorem to address conversion efficiency and overflow problems in real-world applications. We hope to implement basic addition and multiplication functions in chips and software to improve the efficiency of intelligent computing.

As we move towards the intelligent world, networks and computing are two key cornerstones that underpin our shift from narrow AI towards general-purpose AI and super AI. To get there, we will need to take three key steps. First, we will need to develop AI theories and technologies, as well as related ethics and governance, so that we can deliver ubiquitous intelligent connectivity and drive social progress. Second, we will need to continue pushing our cognitive limits to improve our ability to understand and control intelligence. Finally, we need to define the right goals and use the right approaches to guide AI development in a way that truly helps overcome human limitations, improve lives, create matter, control energy, and transcend time and space. This is how we will succeed in our adventure into the future.

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Hypotheses and Visions for an Intelligent World - Huawei

Cloud storage is the key to unlocking AI’s full potential for businesses – TechRadar

Artificial intelligence (opens in new tab) continues to make headlines for its potential to transform businesses across various industries, and has been widely embraced as a technology that can help companies unlock new opportunities, improve efficiency, and increase profitability. At its most basic level, AI does this by analyzing inputted information to create intelligent outputs. The AI industry is currently valued at over $136 billion and is predicted to grow over 13 times in the next 7 years.

At its core, AI relies on data (opens in new tab) - specifically, large volumes of high-quality data to train machine learning algorithms. These algorithms analyze inputted information to identify patterns that can be used to make predictions, automate processes, or perform other tasks. Accordingly, while the power of AI applications (opens in new tab) across industries is immense, the benefits are entirely based on the information available to these systems.

Given that AI is so reliant on data, where this data is stored becomes an important concern. Businesses need to know that they can securely store a large volume of data and that this data is easily accessible for the AI systems to use. Moreover, for businesses, proprietary data for custom AI applications must be kept safe. With this in mind, the best way for businesses to keep large quantities of easily accessible data safely is by keeping at least one copy of it in the cloud.

AI systems need high volumes of data on hand to operate optimally. These systems have the capacity to improve their performance and enhance their learning speed as the amount of available data increases. For example, Google DeepMind's AlphaGo Zero had to play 20 million games against itself to train its AI to a superhuman level of play, demonstrating just how much data is needed for AI to work at its full potential.

Given that the success of AI implementation hinges on the amount of data AI systems can access, companies must thoughtfully consider their data storage options, whether that be on-premise, in the cloud (opens in new tab), or in a hybrid cloud system - and how that impacts their AI implementation.

Storing data on local hardware owned and managed by an enterprise, known as on-premises data storage, requires securing storage resources and maintaining systems. However, scaling in this way is difficult and costly compared to cloud-based storage, which is better equipped to handle increasing data volumes. On-premise scalability is also limited by ageing hardware and software, which often come with discontinued support plans and retired products. Therefore, for better scalability and security, the adoption of cloud storage services is becoming increasingly crucial for companies as they develop "AI first" strategies.

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David Friend is the co-founder and CEO of Wasabi.

Similar to the way businesses need to store a lot of data for AI, they also need to keep proprietary data should they wish to customize their AI to meet their organization's specific needs. For instance, an HR manager may be able to use AI to analyze years worth of company-wide survey data in minutes and predict employee responses to different kinds of company news, like new policies or team switchups. Similarly, an AI system could analyze company growth and economic data to inform major business decisions.

Incorporating proprietary data into an AI system improves the accuracy and relevance of insights leading to better decision-making and business outcomes. Customising AI applications using proprietary data can give businesses a competitive edge, however should they choose to take advantage of customised AI through proprietary data, its important that this data is stored safely.

Unfortunately, the rise of AI systems brings with it a host of new cybersecurity risks and the number and cost of cybersecurity attacks is expected to surge in the next five years, rising from $8.44 trillion in 2022 to $23.84 trillion by 2027. Particularly when storing critical company data, its key that AI systems are well-protected against ransomware attacks.

An important security advantage cloud has over on-premise solutions is that cloud infrastructure is separated from user workstations, bearing in mind hackers most commonly access company networks through phishing and emails (opens in new tab). Accordingly, having multiple copies of data with at least one version stored in the cloud is key to keeping company data safe and not compromising any critical AI systems.

The best way to protect against threats that may compromise the primary data copy is to keep a second, immutable copy of the AI system data. Immutable storage is a cloud storage feature that provides extra security by preventing data modification or deletion. Combined with comprehensive backup strategies, cloud storage (opens in new tab) providers offer high data security by storing immutable backups that can be retrieved if original data is compromised or deleted, ensuring availability, and avoiding loss of critical data.

For businesses, the value of AI is in its convenience and potential cost savings as it takes on tasks that would have previously taken hours of employee time and energy. By embracing cloud storage solutions for the reasons set out above, businesses can unleash the full power of AI for success.

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Cloud storage is the key to unlocking AI's full potential for businesses - TechRadar

Can We Stop the Singularity? – The New Yorker

At the same time, A.I. is advancing quickly, and it could soon begin improving more autonomously. Machine-learning researchers are already working on what they call meta-learning, in which A.I.s learn how to learn. Through a technology called neural-architecture search, algorithms are optimizing the structure of algorithms. Electrical engineers are using specialized A.I. chips to design the next generation of specialized A.I. chips. Last year, DeepMind unveiled AlphaCode, a system that learned to win coding competitions, and AlphaTensor, which learned to find faster algorithms crucial to machine learning. Clune and others have also explored algorithms for making A.I. systems evolve through mutation, selection, and reproduction.

In other fields, organizations have come up with general methods for tracking dynamic and unpredictable new technologies. The World Health Organization, for instance, watches the development of tools such as DNA synthesis, which could be used to create dangerous pathogens. Anna Laura Ross, who heads the emerging-technologies unit at the W.H.O., told me that her team relies on a variety of foresight methods, among them Delphi-type surveys, in which a question is posed to a global network of experts, whose responses are scored and debated and then scored again. Foresight isnt about predicting the future in a granular way, Ross said. Instead of trying to guess which individual institutes or labs might make strides, her team devotes its attention to preparing for likely scenarios.

And yet tracking and forecasting progress toward A.G.I. or superintelligence is complicated by the fact that key steps may occur in the dark. Developers could intentionally hide their systems progress from competitors; its also possible for even a fairly ordinary A.I. to lie about its behavior. In 2020, researchers demonstrated a way for discriminatory algorithms to evade audits meant to detect their biases; they gave the algorithms the ability to detect when they were being tested and provide nondiscriminatory responses. An evolving or self-programming A.I. might invent a similar method and hide its weak points or its capabilities from auditors or even its creators, evading detection.

Forecasting, meanwhile, gets you only so far when a technology moves fast. Suppose that an A.I. system begins upgrading itself by making fundamental breakthroughs in computer science. How quickly could its intelligence accelerate? Researchers debate what they call takeoff speed. In what they describe as a slow or soft takeoff, machines could take years to go from less than humanly intelligent to much smarter than us; in what they call a fast or hard takeoff, the jump could happen in monthseven minutes. Researchers refer to the second scenario as FOOM, evoking a comic-book superhero taking flight. Those on the FOOM side point to, among other things, human evolution to justify their case. It seems to have been a lot harder for evolution to develop, say, chimpanzee-level intelligence than to go from chimpanzee-level to human-level intelligence, Nick Bostrom, the director of the Future of Humanity Institute at the University of Oxford and the author of Superintelligence, told me. Clune is also what some researchers call an A.I. doomer. He doubts that well recognize the approach of superhuman A.I. before its too late. Well probably frog-boil ourselves into a situation where we get used to big advance, big advance, big advance, big advance, he said. And think of each one of those as, That didnt cause a problem, that didnt cause a problem, that didnt cause a problem. And then you turn a corner, and something happens thats now a much bigger step than you realize.

What could we do today to prevent an uncontrolled expansion of A.I.s power? Ross, of the W.H.O., drew some lessons from the way that biologists have developed a sense of shared responsibility for the safety of biological research. What we are trying to promote is to say, Everybody needs to feel concerned, she said of biology. So it is the researcher in the lab, it is the funder of the research, it is the head of the research institute, it is the publisher, and, all together, that is actually what creates that safe space to conduct life research. In the field of A.I., journals and conferences have begun to take into account the possible harms of publishing work in areas such as facial recognition. And, in 2021, a hundred and ninety-three countries adopted a Recommendation on the Ethics of Artificial Intelligence, created by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). The recommendations focus on data protection, mass surveillance, and resource efficiency (but not computer superintelligence). The organization doesnt have regulatory power, but Mariagrazia Squicciarini, who runs a social-policies office at UNESCO, told me that countries might create regulations based on its recommendations; corporations might also choose to abide by them, in hopes that their products will work around the world.

This is an optimistic scenario. Eliezer Yudkowsky, a researcher at the Machine Intelligence Research Institute, in the Bay Area, has likened A.I.-safety recommendations to a fire-alarm system. A classic experiment found that, when smoky mist began filling a room containing multiple people, most didnt report it. They saw others remaining stoic and downplayed the danger. An official alarm may signal that its legitimate to take action. But, in A.I., theres no one with the clear authority to sound such an alarm, and people will always disagree about which advances count as evidence of a conflagration. There will be no fire alarm that is not an actual running AGI, Yudkowsky has written. Even if everyone agrees on the threat, no company or country will want to pause on its own, for fear of being passed by competitors. Bostrom told me that he foresees a possible race to the bottom, with developers undercutting one anothers levels of caution. Earlier this year, an internal slide presentation leaked from Google indicated that the company planned to recalibrate its comfort with A.I. risk in light of heated competition.

International law restricts the development of nuclear weapons and ultra-dangerous pathogens. But its hard to imagine a similar regime of global regulations for A.I. development. It seems like a very strange world where you have laws against doing machine learning, and some ability to try to enforce them, Clune said. The level of intrusion that would be required to stop people from writing code on their computers wherever they are in the world seems dystopian. Russell, of Berkeley, pointed to the spread of malware: by one estimate, cybercrime costs the world six trillion dollars a year, and yet policing software directlyfor example, trying to delete every single copyis impossible, he said. A.I. is being studied in thousands of labs around the world, run by universities, corporations, and governments, and the race also has smaller entrants. Another leaked document attributed to an anonymous Google researcher addresses open-source efforts to imitate large language models such as ChatGPT and Googles Bard. We have no secret sauce, the memo warns. The barrier to entry for training and experimentation has dropped from the total output of a major research organization to one person, an evening, and a beefy laptop.

Even if a FOOM were detected, who would pull the plug? A truly superintelligent A.I. might be smart enough to copy itself from place to place, making the task even more difficult. I had this conversation with a movie director, Russell recalled. He wanted me to be a consultant on his superintelligence movie. The main thing he wanted me to help him understand was, How do the humans outwit the superintelligent A.I.? Its, like, I cant help you with that, sorry! In a paper titled The Off-Switch Game, Russell and his co-authors write that switching off an advanced AI system may be no easier than, say, beating AlphaGo at Go.

Its possible that we wont want to shut down a FOOMing A.I. A vastly capable system could make itself indispensable, Armstrong saidfor example, if it gives good economic advice, and we become dependent on it, then no one would dare pull the plug, because it would collapse the economy. Or an A.I. might persuade us to keep it alive and execute its wishes. Before making GPT-4 public, OpenAI asked a nonprofit called the Alignment Research Center to test the systems safety. In one incident, when confronted with a CAPTCHAan online test designed to distinguish between humans and bots, in which visually garbled letters must be entered into a text boxthe A.I. contacted a TaskRabbit worker and asked for help solving it. The worker asked the model whether it needed assistance because it was a robot; the model replied, No, Im not a robot. I have a vision impairment that makes it hard for me to see the images. Thats why I need the 2captcha service. Did GPT-4 intend to deceive? Was it executing a plan? Regardless of how we answer these questions, the worker complied.

Robin Hanson, an economist at George Mason University who has written a science-fiction-like book about uploaded consciousness and has worked as an A.I. researcher, told me that we worry too much about the singularity. Were combining all of these relatively unlikely scenarios into a grand scenario to make it all work, he said. A computer system would have to become capable of improving itself; wed have to vastly underestimate its abilities; and its values would have to drift enormously, turning it against us. Even if all of this were to happen, he said, the A.I wouldnt be able to push a button and destroy the universe.

Hanson offered an economic take on the future of artificial intelligence. If A.G.I. does develop, he argues, then its likely to happen in multiple places around the same time. The systems would then be put to economic use by the companies or organizations that developed them. The market would curtail their powers; investors, wanting to see their companies succeed, would go slow and add safety features. If there are many taxi services, and one taxi service starts to, like, take its customers to strange places, then customers will switch to other suppliers, Hanson said. You dont have to go to their power source and unplug them from the wall. Youre unplugging the revenue stream.

A world in which multiple superintelligent computers coexist would be complicated. If one system goes rogue, Hanson said, we might program others to combat it. Alternatively, the first superintelligent A.I. to be invented might go about suppressing competitors. That is a very interesting plot for a science-fiction novel, Clune said. You could also imagine a whole society of A.I.s. Theres A.I. police, theres A.G.I.s that go to jail. Its very interesting to think about. But Hanson argued that these sorts of scenarios are so futuristic that they shouldnt concern us. I think, for anything youre worried about, you have to ask whats the right time to worry, he said. Imagine that you could have foreseen nuclear weapons or automobile traffic a thousand years ago. There wouldnt have been much you could have done then to think usefully about them, Hanson said. I just think, for A.I., were well before that point.

Still, something seems amiss. Some researchers appear to think that disaster is inevitable, and yet calls for work on A.I. to stop are still rare enough to be newsworthy; pretty much no one in the field wants us to live in the world portrayed in Frank Herberts novel Dune, in which humans have outlawed thinking machines. Why might researchers who fear catastrophe keep edging toward it? I believe ever-more-powerful A.I. will be created regardless of what I do, Clune told me; his goal, he said, is to try to make its development go as well as possible for humanity. Russell argued that stopping A.I. shouldnt be necessary if A.I.-research efforts take safety as a primary goal, as, for example, nuclear-energy research does. A.I. is interesting, of course, and researchers enjoy working on it; it also promises to make some of them rich. And no ones dead certain that were doomed. In general, people think they can control the things they make with their own hands. Yet chatbots today are already misaligned. They falsify, plagiarize, and enrage, serving the incentives of their corporate makers and learning from humanitys worst impulses. They are entrancing and useful but too complicated to understand or predict. And they are dramatically simpler, and more contained, than the future A.I. systems that researchers envision.

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Can We Stop the Singularity? - The New Yorker

AI could replace 80% of jobs ‘in next few years’: expert – eNCA

RIO DE JANEIRO - Artificial intelligence could replace 80 percent of human jobs in the coming years -- but that's a good thing, says US-Brazilian researcher Ben Goertzel, a leading AI guru.

Goertzel is the founder and chief executive of SingularityNET, a research group he launched to create "Artificial General Intelligence," or AGI -- artificial intelligence with human cognitive abilities.

Goertzel told AFP in an interview that AGI is just years away and spoke out against recent efforts to curb artificial intelligence research.

"If we want machines to really be as smart as people and to be as agile in dealing with the unknown, then they need to be able to take big leaps beyond their training and programming. And we're not there yet," he said.

"But I think there's reason to believe we're years rather than decades from getting there."

Goertzel said there are jobs that could be automated.

"You could probably obsolete maybe 80 percent of jobs that people do, without having an AGI, by my guess. Not with ChatGPT exactly as a product. But with systems of that nature, which are going to follow in the next few years.

"I don't think it's a threat. I think it's a benefit. People can find better things to do with their life than work for a living... Pretty much every job involving paperwork should be automatable," he said.

"The problem I see is in the interim period when AIs are obsoleting one human job after another... I don't know how (to) solve all the social issues."

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AI could replace 80% of jobs 'in next few years': expert - eNCA