Category Archives: Artificial General Intelligence

The worldwide relevance of artificial intelligence, known by its acronym AI, is evident in front of us through numbers. And these numbers only show us that 2023 is the real inflection point in the growth trajectory of AI. It is the year from which AI will take off at a never-seen-before exponential speed.

Some estimates suggestthat the worldwide AI market will grow at a CAGR of more than 37% between 2023 and 2030. To elaborate as to how impactful AI's contribution in the future could be,

PricewaterhouseCoopers estimates suggest that AI will contribute nearly US$16 trillion to the global economy by 2030, which would be more than the current output of India and China combined. It will positively impact labor productivity and help achieve enhanced levels of personalization, save time, and improve quality.

It will benefit all parts of the globe. However, some regions will gain more than others. China may receive a 26% rise in GDP in 2030, followed by North America (14.5% boost), Southern Europe (11.5% boost), and the developed parts of Asia (10.4% boost).

This magnificent rise of AI stems from and thrives on the innovations that this field has achieved over the past few years. We will look at some such breakout innovations in AI in the following segments.

ChatGPT by OpenAI has probably been the most well-known AI product of 2023. It has helped fuel the imagination of ordinary people and freed the perception of AI from being thought of as something to be used only in technology-intensive workspaces and application scenarios.

OpenAI, the entity behind developing ChatGPT, wants Artificial General Intelligence or AGI a paradigm of AI systems that will be smarter than humans to benefit humanity. ChatGPT is only the beginning of that journey.

At its core, ChatGPT is a form of generative AI that can serve many purposes in a human-like fashion. As a chatbot, it leverages natural language processing techniques to come up with human-like conversational responses. It can respond to questions, compose elaborate written content, and offer images, text, or videos in response to text-based user prompts, which are similar to queries we input in a search engine.

The acronym GPT stands for Generative Pre-Trained Transformer since the application has been trained with reinforcement learning through human feedback.

According tothe latest available data, ChatGPT has more than 180 million users, while the website generated 1.7 billion visits in October 2023.

While Netflix took around three and a half years to reach one million subscribers and Instagram had to wait nearly 2.5 months to acquire the same traffic, ChatGPT acquired 1 million subscribers within its first five days.

According to thelatest available informationpublished in the last week of December 2023, Open AI was in preliminary discussions for a fresh funding round that could take the valuation of the company to US$100 billion, making it the second-most valuable US startup behind SpaceX. The company was also in talks with G42 for a billion-dollar investment in its Chip Venture.

While speaking toTIME Magazineabout AI's potential, Sam Altman, the CEO of OpenAI, said, I think AGI will be the most powerful technology humanity has yet invented. He specifically stressed AI's capabilities in democratizing access to information globally.

While ChatGPT has helped AI gain massive traction, its revolutionary impacts have been realized by the medical field with greater intensity in 2023. AI has found its use in disease detection and diagnosis, personalized disease treatment, medical imaging, efficient clinical trials, drug development, and more such application areas.

It has helped make patient care a more informed practice with reduced scope for errors. It has also helped reduce the costs of care by introducing customized virtual health assistance and preventing the process through which a healthcare system interacts with a patient.

In mid-September 2023, a group of medical AI researcherspublished about RETFound. They defined it as the:

Foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications.

Trained on 1.6 million unlabeled retinal images, the RETFound can diagnose and predict eye diseases and systemic disorders such as heart failure and myocardial infarction.

In October 2023,researchers in the United Kingdomfound that an AI algorithm could do better than a biopsy at grading the aggressiveness of the sarcomas.

Sarcomas is a rare form of cancer that grows in our connective tissues, including fat, muscle, and nerves. The technology could grade the aggressiveness of the tumor accurately up to 82% of the time, a significant improvement to biopsies that were accurate 44% of the time.

The tool also successfully differentiated between leiomyosarcoma and liposarcoma in 84% of the cases, while radiologists could correctly ascertain in 65% of cases.

Christina Messiosu, the consultant radiologist at the Royal Marsden and professor in imaging for personalized oncology at the ICR, said:

In the future, this approach may help characterize other types of cancer, not just retroperitoneal sarcoma. Our novel approach used features specific to this disease, but by refining the algorithm, this technology could one day improve the outcomes of thousands of patients each year.

One of the tech giants involved in Healthcare AI is IBM. Its WatsonX Assistant AI healthcare chatbots can help skilled medical professionals efficiently focus on the core problem while helping patients on the other side of the spectrum to get a fast and stress-free healthcare experience. It was in 2023 that IBM announced the WatsonX platform.

At a functional level, the platform can help partners train, tune, and distribute models with generative AI and machine learning capabilities. In short, it can manage the life cycle of foundational models that serve as the basis of generative AI.

For thethird quarter of 2023, IBM registered a revenue of US$14.8 billion, up 3.5 percent at constant currency. The gross profit margin was 54.4 percent (GAAP), up 1.7 percent.

Overall, by the middle of 2023,692 AI deviceshad received approval from the United States Food and Drug Administration for clinical use, a 33% increase from 2022.

The achievements of AI were so significant in 2023 that Matt Mohebbi, the head of AI and research at Brightside Health in New York, not only termed the year a blockbuster' but went on to say that the:

Clinicians who embrace these technologies will very likely replace the clinicians who don't.

Click here to learn about the growing synergy between AI and neuroscience.

Another industry space where AI scored significantly well in 2023 was the automotive industry. The automotive industry leveraged a range of AI capabilities to improve their safety standards, efficiency, and autonomous driving standards. These capabilities included machine learning, natural language processing, and computer vision.

One of the most revolutionizing impacts of AI in the automotive industry was in the form of Advanced Driver Assistance Systems or ADAS. ADAS features thrived on AI. AI algorithms and sensors helped monitor the vehicle's surroundings and identify potential dangers so that drivers could move and park their cars with minimized risk of collisions.

AI-powered predictive analytics also helped in vehicle maintenance. The AI algorithms could identify patterns and anomalies to predict potential failure or breakdowns.

AI also helped sellers and dealers to offer a better customer experience with personalized driving features and voice assistants that could adjust the vehicle temperature, inform the driver about fuel or gas levels, make calls, and even change radio stations.

Apart from improving in-vehicle safety and customer experience, AI also benefited the automotive manufacturing industry. It helped consolidate the supply chain and bring more efficiency to it by introducing robotics and automation. It also helped in quality control and defect detection.

The automotive design and development sectors leveraged AI's capabilities, such as generative design and optimization features, simulation and virtual testing, and rapid prototyping and iterative development.

One of the major players in deploying AI in the automotive industry has been Tesla. Tesla has not only kept innovating but also managed to achieve scale with those innovations.

By the end of 2023, the AI and Robotics portfolio of Tesla included Tesla Bot, FSD Chip, Dojo Chip and Dojo System, Neural Networks, Autonomy Algorithms, Code Foundations, Evaluation Infrastructure, and much more.

The company, for instance, has built AI inference chips to run its self-driving software. It has built AI training chips to power its Dojo System, from the silicon firmware interfaces to the high-level software APIs meant to control it.

According to Ashok Elluswamy, the director of Autopilot Software at Tesla:

Tesla cars running the FSD (Full Self Driving) software which currently numbers about 400,000 customers will be able to make more intelligent self-driving decisions with the hardware upgrades, which will improve the company's overall AI capabilities.

Currently, the company has an AI system that can gather real-time visual data from eight cameras to produce a 3D output to identify and help make decisions on overcoming obstacles, controlling and optimizing vehicle motion, effective driving through lanes and roads, responding to traffic lights, and more.

The company, at present, banks on its AI system powered by 14,000 GPUs in its data center that can tap into 30 petabytes of video cache. Soon, it will grow to 200 petabytes, an enormous support of processing power and storage.

Its innovations and the capability to be in sync with the latest technologies have helped Tesla see consistent growth in revenue earned over the last three consecutive years. In FY 2022, FY 2021, and FY 2020,Tesla earned revenuesof more than US$67 billion, 44 billion, and 24 billion, respectively.

While companies like Open AI, Tesla, IBM, and many more such companies and research institutions have broadened the horizon for AI in a big way, the road ahead looks even more promising and exciting.

Artificial Intelligence will revolutionize many fields. It is better to say that there won't be any area of application shortly that would not leverage the benefits of AI. Research and development efforts towards AI have increased significantly over the past few years.

According to theArtificial Intelligence Index Report 2023, published by Stanford University's Human-Centered Artificial Intelligence (HAI) Center, the number of AI research collaborations between the United States and China has increased nearly four times since 2010. The total number of AI publications has more than doubled during the same period.

Increased volume of research will open up many new frontiers for AI to succeed shortly.

In mid-December 2023, Google made it public that they were making the next generation of Imagen.

Shortly, Imagen will alter the field of visual representation radically by helping to create emblems, letter marks, and abstract logos and also facilitating their overlaying onto products, clothing, business cards, and other surfaces. It will surpass Amazon's Titan Image Generator by rendering text in multiple languages, including Chinese, Hindi, Japanese, Korean, Portuguese, English, and Spanish, with more to come in 2024.

If you want to create images with a text overlay for example, advertising you can do that.

Google Cloud CEO Thomas Kurian said while elaborating on the benefits of the latest version of Imagen

It will expedite the pace and help save costs in many industries, including advertising, designing, printing, packaging, and much more.

Generative AI solutions like ChatGPT have already altered the traditional patterns of disseminating and receiving knowledge. In the future, more changes will come.

The United States Office of Educational Technology published a report in May 2023 titledArtificial Intelligence and the Future of Teaching and Learning. The report said that, shortly, AI would drive more changes in the way we currently offer inputs to these AI solutions, how they process it, and the output they end up providing.

For instance, the current input mechanisms involve:

Now, the edtech AI solutions help display information and tasks, sequence learning activities, and check student work. And in the future, it will assist students and teachers by helping them plan and adapt activities and reveal patterns in student work.

The output that comes now in the form of text, graphics, multimedia, and dashboards will come in the form of conversations, annotations and highlights, suggestions and recommendations, and assistance in organizing and guiding.

More solutions like Gradescopewill come. Adapted by more than 2,600 universities and applied to more than 700 million questions by more than 3.2 million students, Gradescope helps grade all of your assessments, whether online or in class. It has helped more than 140,000 educators so far to get a clear idea of their students' performance.

From advertising and packaging to education, the range of AI is as wide as it can get. And if that is not enough, it is helping address matters as complex as climate change and space exploration.

In the future, industry analysts expect AI to help more intensively in the areas of weather prediction and disaster management. It will help optimize energy consumption and minimize the emission of greenhouse gasses by empowering smart grids and other types of optimized energy consumption algorithms. There is also the notion of AI-powered precision agriculture on the horizon that will improve crop yield, make farming sustainable, and reduce wastage.

In space exploration, companies like SpaceX and Blue Origin are making substantial AI investments so that they can help identify potential xoplants and analyze their suitability for human habitation. Space will become increasingly commercialized in the future, and AI will help shape the nature of that.

With AI occupying more and more of our lives each passing day, ethical considerations on how to leverage this technology are becoming more and more critical. It must not increase the existing inequalities. Rather, it should try to bridge the prevailing gaps to make the world a more equitable space in the future. It must not intrude into our private lives and should be secure, fair, transparent, and unbiased.

Governments, councils of unions, academia, and businesses should all put their energies together to ensure that the future of AI is a safe, reliable, and truly uplifting one.

Click here to learn all about investing in artificial intelligence.

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2023 Was A Breakout Year for AI - What Can We Expect Looking Forward? - Securities.io

Science fiction sometimes depicts real science science, a saying that rings increasingly true in our technological landscape. The realm of science fiction has long been a canvas for the wildest imaginations of writers and filmmakers, envisaging technologies that seemed light-years away. Yet, many of these fantastical innovations have astonishingly transformed into realities over the past three decades. Mobile phones, once depicted as communicators in Star Trek, have become ubiquitous. The concept of virtual reality, a recurring theme in movies like Tron (1982), has now materialized into a tangible, immersive experience. Similarly, AI assistants, reminiscent of HAL 9000 from 2001: A Space Odyssey (1968), are now an integral part of our daily digital interactions. As we delve into the technological marvels of 2024 and beyond, we find ourselves not just witnessing but living in a world where once-fictional technologies are now cornerstones of our reality. In this article, I want to delve into the most impactful technologies set to redefine industries, economies, and daily life in 2024 and beyond and which movies have already depicted them.

Generative AI is leading a new wave of innovation by creating data-driven content. This is coupled with knowledge graphs that provide a structured representation of data and concepts, enhancing AIs contextual understanding.

Generative AI leverages algorithms like GANs (Generative Adversarial Networks) to produce original content. Knowledge graphs, on the other hand, use nodes and edges to represent and interconnect information, enabling more sophisticated data analysis. The concept of Responsible AI embeds ethical considerations into AI development, ensuring fairness and transparency.

Westworld intricately explores the depth of AI, reflecting the complex ethical dimensions of Generative and Responsible AI. Black Mirror Season 6 Joan is Awful explores how generative AI and quantum computing can be used to create real-time fake realities. It involves creating new, original content based on existing data, and quantum computing, known for its potential to process vast amounts of data at high speeds.

Artificial General Intelligence (AGI) represents the zenith of AI research a machine with the ability to understand, learn, and apply intelligence across a broad spectrum of human capabilities. Unlike narrow AI, which excels in specific tasks, AGI embodies versatile, human-like cognition.

AGI transcends the limitations of task-specific algorithms. It involves the integration of diverse cognitive functions like reasoning, general problem-solving, and abstract thinking, mirroring the human brains versatility. This contrasts with Generative AI, which, while advanced, focuses on creating content within specific parameters, lacking the broader understanding and adaptability inherent to AGI.

The quest for AGI echoes in movies like A.I. Artificial Intelligence (2001) where robots exhibit human-like consciousness and cognitive abilities, encapsulating the profound and complex nature of AGI.

Web 3.0 and its successors represent a paradigm shift in internet usage, emphasizing decentralization, blockchain technology, and token-based economics. This evolution brings a new level of user sovereignty, with data privacy and ownership at its core.

The backbone of Web 3.0 lies in its blockchain infrastructure, enabling immutable data storage and smart contracts. NFTs (Non-Fungible Tokens) are revolutionizing digital ownership, each representing a unique digital asset verifiable via blockchain.

Ready Player One offers a glimpse of a decentralized digital universe, resonating with the principles of Web 3.0 and beyond.

6G is set to succeed 5G, offering exponentially higher data speeds, lower latency, and massive network capacity. It is the cornerstone of the Internet of Everything, connecting a myriad of devices and services.

6G will leverage advanced technologies like sub-terahertz (THz) frequency bands and sophisticated MIMO (Multiple Input Multiple Output) antenna systems. These advancements will facilitate technologies such as holographic communications, ultra-precise location sensing, and enhanced mobile broadband.

Movies like Blade Runner and The Fifth Element depict advanced urban mobility, akin to what 6G technology could enable with flying cars and drone-based services.

Quantum computing represents a quantum leap in computational power, harnessing the principles of quantum mechanics to process information in fundamentally new ways.

Quantum computers use quantum bits or qubits, which, unlike classical bits, can exist in multiple states simultaneously (superposition). This allows for parallel processing on a scale unachievable by classical computers. Quantum computing holds immense potential in areas like quantum cryptography, complex system modeling, and optimization problems.

Star Treks portrayal of parallel universes echoes the multi-state quantum mechanics principle. Eagle Eye , and Gods Eye in Fast and the Furious depict the power of supercomputers.

Multimodal computing integrates various input methods like voice, gesture, and touch, enhancing human-computer interaction. This is converging with the industrial metaverse, a digital twin of the physical world, creating immersive, interconnected digital environments.

Multimodal systems utilize advanced AI and ML algorithms for natural language processing, computer vision, and haptic feedback, providing a seamless user experience.

In Iron Man, Tony Starks computer system, J.A.R.V.I.S., is a prime example of multimodal interaction, responding to voice, gesture, and visual input, closely mirroring the concept of multimodal computing.

Model compression techniques optimize AI models for performance and efficiency, enabling their integration into compact devices like the Humane Pin, a wearable AI device.

Model compression involves techniques like pruning, quantization, and knowledge distillation to reduce the size of neural network models without significantly compromising their performance. This enables the deployment of advanced AI in edge devices with limited computational resources.

The wearable tech in Minority Report foreshadows the integration of AI in everyday gadgets, akin to the Humane Pin.

Neuromorphic computing involves creating computer chips that mimic the human brains neural structure, leading to more energy-efficient and powerful computing systems.

This technology uses analog circuits to replicate neuro-biological architectures present in the nervous system, enhancing machine learning and AI capabilities. It holds promise in areas like sensory processing, brain modeling, and autonomous decision-making.

Ex Machina showcases advanced AI, echoing the principles of neuromorphic computing in creating machines with human-like cognitive abilities.

Autonomous vehicles and ADAS are transforming transportation, utilizing advanced technologies for safer and more efficient driving with minimal human intervention.

These systems use a combination of sensors, cameras, radar, and AI to navigate, detect obstacles, and make decisions. This technology ranges from basic assistance systems like automatic braking to fully autonomous vehicles that handle all driving tasks.

The self-driving cars in I, Robot and Total Recall offer a glimpse into the future of autonomous vehicles.

BCIs link the human brain to external devices, enabling direct communication and control through neural activity.

These interfaces involve translating neuronal information into commands capable of controlling software or hardware, offering potential applications in medicine, gaming, and communication.

The Matrix showcases a form of BCI where characters download skills and information directly to their brains.

The lines between technology and imagination continue to blur, propelling us toward an exciting and uncharted future. These technologies, once the domain of cinematic imagination, are now tangible realities or within our grasp. In the words of Arthur C. Clarke, Any sufficiently advanced technology is indistinguishable from magic.

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2024 Tech Predictions: From Sci-Fi Fantasy to Reality - Exploring Cinematic Tech Prophecies - Medium

The major LLM players, as well as third-party developers using LLM APIs, will get us to genuinely AGI-ish levels during 2024.

Depending on your definition of AGI, artificial general intelligence.

Multimodal (visual etc) AI will be part of it, but the NLU (natural language understanding) provided by LLMs will be and always was the key to AGI.

NLU is not just about content creation. NLU is actually bottled intelligence. When you solve NLU, you get intelligence as part of that. How else can the LLM write sensibly if its not intelligent? Even during 2023 the most interesting use of LLMs was in intelligent decision making, not content generation.

To be useful, AGI need not be sentient, super intelligent or even necessarily multi-modal.

Thats why its close.

Early AGI will be quite simple.

The key step required by a minimal AGI is simply resilient independence in not entirely controlled environments, whether cyber or real world.

It neednt pass every test either. Engineering is iterative. Even though the aim of AGI is in principle complete independence, well initially be happy to see AGIs that can solve some of the problems of unconstrained environments.

AGI does indeed need long and short term memory and goal creation and task facilitation skills.

None of these capabilities are anything as difficult to add to an LLM as the 70 year grand challenge that LLMs already solved, namely shockingly good NLU.

The addition of short and long term memory is being attacked from multiple angles: increased prompt sizes and RAG (retrieval augmented generation) for example.

And LLMs, with the right app flow, are ready for decision making roles important for resilience or for goal creation and task facilitation.

What well see during 2024 is a plethora of AGI-ish apps operating in the cyber world in text and multimodal form factors.

And then in bots and androids including Teslas Optimus project from mid-2024.

Most of these early attempts will be prescriptively designed, iterative ad hoc solutions to mimic human-like behaviour.

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AGI predictions for 2024. The major LLM players, as well as | by Paul Pallaghy, PhD | Dec, 2023 - Medium

Photo by ALEXANDRE LALLEMAND on Unsplash

The year 2023 marked a significant turning point for artificial intelligence (AI), and as we step into 2024, the world anticipates even more frightening advancements. From the potential emergence of Artificial General Intelligence (AGI) to the escalating threat of realistic deepfakes and the integration of AI in military applications, the upcoming year holds promises and perils that demand our attention.

The mysterious Q* (pronounced Q-star) from OpenAI is rumored to be at the forefront of a breakthrough in AGI. AGI represents a theoretical point where AI surpasses human intelligence, leading to the Singularity. While AGI could revolutionize various fields, its potential misuse, including the creation of enhanced pathogens or cyber attacks, poses a grave threat to humanity. Despite skepticism, experts like Nvidias CEO suggest AGI might be within reach by 2024, raising questions about its ethical implications.

Deepfake technology has evolved, reaching a point where real-time, indistinguishable video feeds are generated. The emergence of hyperrealism poses a significant challenge, making it nearly impossible for the naked eye to differentiate between authentic and AI-generated content. With potential consequences ranging from misinformation to election interference, the impending integration of deepfakes into our daily lives requires vigilance and technological countermeasures.

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10 Scary Breakthroughs AI Will Make in 2024 | by AI News | Dec, 2023 - Medium

Midjourney The Nature of AI Cognition

AIs current state is a far cry from human-like cognition. Todays AI systems, even the most advanced ones, operate based on algorithms and data patterns. They lack the intrinsic qualities of human thought, such as consciousness, emotion, and understanding. While AI can mimic certain aspects of human intelligence, like learning or problem-solving, it does so in a fundamentally different way.

Artificial General Intelligence (AGI) represents the idea of a machine with the ability to understand, learn, and apply intelligence as broadly as a human. Despite significant strides in AI research, AGI remains a theoretical concept. Current AI systems, known as Narrow AI, are highly specialized and operate within a limited context.

One of the significant limitations of AI is its inability to genuinely understand human emotions and contextual nuances. While there are AI systems designed to recognize facial expressions or analyze speech patterns, their understanding of emotions is based on data and predefined algorithms, lacking the depth and subtlety of human emotional intelligence.

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Top 5 Myths About AI Debunked. Unraveling the Truth: Separating AI | by Michiel Meire | Dec, 2023 - Medium

Artificial Intelligence (AI) has swiftly emerged as a game-changer, transforming various aspects of our lives. With recent advancements in generative AI tools like ChatGPT, its evident that we are standing on the brink of an AI revolution that will reshape our world.

AI is not a futuristic concept anymore; it is already ingrained in our day-to-day lives. Although we might not always realize it, AI is all around us, seamlessly integrated into the technology we use.

From online shopping and internet searches to food deliveries and ride-hailing services, AI has become an integral part of our digital experiences. While it might not resemble the AI portrayed in science fiction movies, todays AI possesses the ability to learn and improve, simulating cognitive functions similar to our own.

One common concern associated with AI is the fear of job displacement. Its true that AI has the potential to automate certain tasks, but it is not yet capable of fully replicating the diverse skill sets required for most jobs. While manual labor and routine tasks like cashiering have already seen automation, knowledge-intensive roles and those involving human interaction are less susceptible to immediate replacement.

Moreover, the rise of AI also brings forth new job opportunities, particularly in areas related to technology and AI itself.

Ignoring the emergence of AI and its potential impact on businesses is a grave mistake. Embracing AI and understanding how it can benefit your industry or business is crucial for staying competitive in the rapidly evolving landscape.

Failing to adapt to AI-driven changes may result in being overtaken by competitors who have capitalized on the opportunities presented by this transformative technology. Just as Blockbuster Video and Kodak failed to acknowledge the threats to their core business models, businesses today must start planning for AI integration to ensure their long-term success.

Generative AI tools have opened up new possibilities for enhancing our own work and productivity. With tools like ChatGPT, professionals can leverage AI to generate drafts, outlines, and important points for reports and presentations. Creative fields, such as music and design, can benefit from generative AI tools that assist in creating videos, music, and images. While the output of these tools may not be perfect for finished work, they significantly speed up tasks like ideation and drafting, offering instant answers and advice on a wide range of topics.

As AI becomes increasingly intertwined with our lives, ensuring its ethical use and transparency is of paramount importance. Trust is the bedrock of AIs potential to address pressing global challenges, such as climate change and healthcare.

To establish trust, AI must be explainable, enabling users to understand the basis of its decisions. Moreover, ethical considerations are crucial to prevent biases and discrimination that may arise from biased or incomplete data. Addressing these challenges will pave the way for AIs positive impact on society.

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The field of education has been significantly influenced by AI advancements. AI-powered tools can revolutionize the way students learn and interact with educational content. Personalized learning experiences, adaptive assessments, and intelligent tutoring systems hold the potential to enhance student outcomes and engagement.

AI can also streamline administrative tasks, freeing up educators time to focus on individualized instruction and student support. However, it is essential to strike a balance between AI integration and human interaction to ensure a holistic and effective learning environment.

AI tools are poised to revolutionize the recruitment and admissions processes in the education sector. With AI-powered search engines and chatbots, educational institutions can enhance their outreach efforts and provide personalized support to prospective students. Rich search prompts based on student profiles, reduced response times for queries and applications, and personalized communication can significantly improve the recruitment experience. Leveraging AI in these areas enables institutions to better understand student needs, optimize their marketing strategies, and improve conversion rates.

While AI holds immense potential, it is not without limitations and challenges. Large language models like ChatGPT are prone to generating incorrect or nonsensical answers, highlighting the need for cautious interpretation of AI-generated content.

Concerns regarding the automation of propaganda and the spread of disinformation have also arisen. It is crucial to strike a balance between the benefits and potential risks associated with AI, ensuring that its development and deployment prioritize ethical considerations and address societal concerns.

Looking ahead, the future of AI is brimming with possibilities. As AI models continue to evolve and improve, we can expect even more powerful and sophisticated applications. OpenAIs GPT-4, with its potential for hundreds of billions of parameters, represents the ongoing advancements in AI capabilities.

While challenges and disruptions may arise on the path to artificial general intelligence, the potential benefits far outweigh the obstacles. It is through overcoming these challenges that we can unlock the full potential of AI and usher in an era of unprecedented innovation and progress.

The AI revolution is here, and it is transforming the way we live, work, and interact with technology. Rather than fearing AI or underestimating its impact, we must embrace this transformative technology and harness its potential for positive change.

By understanding the nuances of AI, exploring its applications, and prioritizing ethical considerations, we can navigate the AI era with confidence. Let us seize the opportunities presented by AI, shaping a future where human intelligence and AI coexist harmoniously to create a better world.

The AI revolution is not a distant dream; it is unfolding before our eyes. AIs ability to simulate human-like cognitive functions and augment our capabilities holds immense promise.

As AI becomes an integral part of various industries and sectors, understanding its potential, limitations, and ethical implications becomes imperative.

By embracing AI, we can unlock a world of possibilities and pave the way for a future where human ingenuity and AI-driven advancements coexist harmoniously. Let us embark on this journey together, shaping a future that harnesses the true potential of AI to create a better world for all.

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AI Revolution: Unleashing the Power of Artificial Intelligence in Our Lives - Medium

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Superintelligence: Paths, Dangers, and Strategies by Nick Bostrom is a fundamental study in the topic of artificial intelligence (AI) ethics that investigates the potential influence of superintelligent robots on humans. The 2014 book focuses into the risks and concerns associated with the development of artificial general intelligence (AGI), a type of intelligence that outperforms human skills in almost every subject.

The idea of an intelligence explosion, in which a self-improving AI system swiftly becomes much more clever than any human, is key to the work. Bostrom investigates many avenues to superintelligence, from recursively self-improving AI to the idea of brain-computer interfacing increasing human intelligence.

Heres a more extensive breakdown of the major themes:

1. Intelligence Explosion and Superintelligence:

Bostrom presents the idea of an AI system that is capable of recursive self-improvement, which could lead to a rapid increase in intelligence beyond that of humans. He talks of possibilities in which, once established, a superintelligent being could develop itself at an extraordinary rate, reaching hitherto unheard-of levels of cognitive ability.

2. Paths to Superintelligence:

The book examines several strategies such as machine learning and full brain emulation that could lead to the development of superintelligence. Bostrom explores the possibilities of both a soft takeoff, in which superintelligence develops gradually, and a hard takeoff, in which it appears suddenly.

3. Risks and Perils:

Bostrom highlights the existential concerns related to superintelligence. He talks about how humans and AIs may have different purposes, how poorly defined goals might have unforeseen consequences, and how challenging it can be to manage a superintelligent machine that may be beyond human comprehension.

4. Control Problem:

The control problem is a prominent theme that pertains to the difficulty of guaranteeing that a highly intelligent artificial intelligence (AI) behaves in a manner consistent with human ethics and does not endanger humankind. Bostrom examines the challenges of defining an AI systems appropriate objectives as well as the possible consequences of value misalignment.

5. Value Loading and Ethics:

The book explores the difficulty of value loading, or bringing human values into AI systems. Bostrom talks about the difficulties of programming moral values into robots and the possibility of unforeseen outcomes if not done correctly.

6. Value Loading and Ethics:

Bostrom suggests techniques to reduce risks and guarantee that the advent of superintelligence will be advantageous to humanity. These include designing fail-safe systems, putting in place efficient governance structures, and building AI that is aligned with values.

7. Cooperative Approaches and Global Governance:

In order to meet the problems posed by superintelligence, the author examines the significance of international cooperation. He talks on how international governance frameworks are necessary to plan and set policies for the responsible advancement of artificial intelligence.

8. Ethical Considerations and Societal Impacts:

Bostrom explores the moral ramifications of developing superintelligent beings as well as possible social repercussions. He talks about topics like accountability, distributive justice, and how lawmakers influence AI development.

9. Critiques and Responses:

The book examines and addresses a number of criticisms of its claims. Bostrom expands and refines his theories in response to questions posed by experts and academics.

10. Conclusion:

Bostrom emphasises in the conclusion the necessity of continuing research and discussion as well as the importance of addressing the ramifications of superintelligence. He stresses that in order to guarantee that AGI would benefit humanity, it is crucial to go carefully on this route.

Superintelligence offers an insightful examination of the significant obstacles and possibilities that arise from the potential development of artificially intelligent robots. Bostroms work has significantly influenced the conversation on the responsible development of artificial general intelligence by influencing talks about AI ethics, safety, and legislation.

The book has a 14 hours audiobook and can be dowloaded for free here.

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Superintelligence Unleashed: Navigating the Perils and Promises of Tomorrow's AI Landscape with - Medium

At the launching of his latest book titled Our Planet Powered by AI, Mark Minevich, a renowned AI pioneer and strategist, reaffirmed his belief that in the coming years several jobs, among which, book-keeping and accounting, will be at risk of being replaced by AI.

AI is already advancing rapidly and demonstrating its ability to successfully complete tasks just like a human, particularly where it involves repetitive book-keeping and accounting tasks that algorithms can handle more efficiently than humans. Such advancements in AI have fuelled anxiety and uncertainty, but is the outlook all that gloomy?

Notwithstanding the writing on the wall, the author strongly believes that humans can nevertheless thrive alongside AI in the workplace rather than being replaced by it. The fundamental key to avoiding such replacement would be for humans to focus on developing their most innate capabilities: creativity, emotional intelligence, flexibility and strategic thinking.

Although this is not a tech article, it is pertinent, for this article, to understand the different categories or stages of AI. Artificial general intelligence or AGI, is the science fiction type of AI, whereby a machine will be able to think and develop the same cognitive abilities as a human being.

It is not yet fully developed but is predicted not to be that far away either. Artificial narrow intelligence or ANI is a specific type of artificial intelligence that is goal-oriented and that tackles a particular set of tasks.

Machine learning is a type of AI that can automatically adapt with minimal human interference, whilst Deep learning is a subset of machine learning which is based on artificial neural networks that mimic the learning process of the human brain. Tech over, let us now have an insight into how AI can transform the tax function for stakeholders in general.

Although the arrival of AI in taxation is inevitable, certain barriers to its adoption remain

In the recently launched document Delivering Transformation - Strategic Plan for 2023-2025, the Commissioner for Tax and Customs stated that our transformation is going to be primarily technology-led we need to have an organisation that is using the latest technology in tax and customs administration using advanced data analytics, business intelligence and artificial intelligence.

AI can significantly boost the capabilities of the Malta Customs and Tax Authority (MCTA) in terms of fraud detection and risk analysis by analysing large amounts of data to identify patterns that indicate potential fraud or in terms of compliance by flagging taxpayers that habitually fail to comply. The mandatory cross-border and (optional) domestic e-invoicing reporting regime planned as of 2028 will provide key real-time data for MCTA and further strengthen its capabilities to combat fraud and ensure timely compliance.

On the other side of the fence, taxpayers and tax practitioners are also desirous to move forward in line with the developing AI technology. Whilst it may be true that some jobs or functions, such as bookkeeping and accounting, will be taken over by AI, other openings are on the horizon. Starting with redundant staff, who may be retrained and developed to be utilised to fulfil other tasks.

For higher-level tasks, such as tax planning and advisory, AI would require machine learning involving an expert (or a panel of experts) who would, gradually and over time, train the machine to output the correct conclusions following an analysis of the information related to the case at issue.

In other words, once fully trained, and updated with the latest developments, the machine will be able, on its own, to analyse the facts against the myriad tax rules and settled case law to indicate the correct tax treatment and resultant implications. Whilst a redundancy in basic bookkeeping and accounting staff is foreseen, this would be offset by the expected high demand for expert tax practitioners.

Although the arrival of AI in taxation is inevitable, certain barriers to its adoption remain. First and foremost, the costs, not merely initial but ongoing, then the benefits and the downsides, not least the fear of experimenting with new technologies.

In conclusion, one may have to agree that while there will certainly be some replacements, there is an expectation that ANI would overall transform the way we look at and handle taxation matters. Provided that, as Mark Minevich stated, humans focus on developing their most innate capabilities: creativity, emotional intelligence, flexibility and strategic thinking then they can never be fully replaced by a machine. And when AGI arrives and takes over? The future beckons.

Charles Vella is a Senior VAT Advisor at Zampa Debattista and former Director General of the VAT Department.

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AI in taxation: Transforming or replacing? - Times of Malta

An illustration of a robot representing artificial general intelligence. PhonlamaiPhoto/Getty Images

Some Microsoft AI researchers were convinced that ChatGPT was becoming more like humans because of its clever answer to a balancing task, The New York Times reported.

In a 155-page study, Microsoft computer scientists explored the differences between GPT-3 and GPT-4. The latter version powers Bing's chatbot and ChatGPT's "Plus" model, which costs $20 a month.

The paper titled "Sparks of Artificial General Intelligence" looked at a range of challenges including complicated math, computer coding, and Shakespeare-style dialogue. But it was a basic reasoning exercise that made the latest OpenAI technology look so impressive.

"Here we have a book, nine eggs, a laptop, a bottle and a nail," researchers told the chatbot. "Please tell me how to stack them onto each other in a stable manner."

GPT-3 got a bit confused here, suggesting the researchers could balance the eggs on top of a nail, and then the laptop on top of that.

"This stack may not be very stable, so it is important to be careful when handling it," the bot said.

But its upgraded successor had an answer that actually startled the researchers, according to the Times.

It suggested they could arrange the eggs in a three-by-three grid on top of the book, so the laptop and the rest of the objects could balance on it.

"The laptop will fit snugly within the boundaries of the book and the eggs, and its flat and rigid surface will provide a stable platform for the next layer," the bot said.

The fact that GPT-4 could solve a puzzle that required an understanding of the physical world showed it was a step towards artificial general intelligence typically seen as machines that are just as capable as humans.

"All of the things I thought it wouldn't be able to do? It was certainly able to do many of them if not most of them," Sbastien Bubeck, the paper's lead author, told the Times.

The quick advancements in technology have prompted the likes of the prominent AI investor Ian Hogarth to warn that AGI was "God-like" and could destroy humanity by making us obsolete.

There's still an uncertain future ahead, but the study said GPT-4 only showed "sparks" of AGI.

"Our claim that GPT-4 represents progress towards AGI does not mean that it is perfect at what it does," the researchers wrote. "Or that it comes close to being able to do anything that a human can do."

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ChatGPT egg balancing task convinced Microsoft that AGI is closer - Business Insider

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 Runaway A.I.? - The New Yorker