Innovation Good Bad - AGI - Michigan 2024 BFHR

INNOVATION GOOD/BAD - AGI
NEG
1NC---Shell
Current AI is weak and narrow
Brotons 23 (Brotons holds an MBA in Marketing and Strategy from the University of Geneva, and several certifications from Google, Microsoft, Amzon for data science, analytics, GenAI and ML.) “The Limitations of Generative AI, According to Generative AI” 2023, https://lingarogroup.com/blog/the-limitations-of-generative-ai-according-to-generative-ai // ZX
Generative AI tools can do all sorts of things, such as write blog posts like this one, albeit partially and with a lot of edits by a human writer. This post uses three text-to-text AI writers (anonymized for obvious reasons) to write articles about the limitations of generative AI.
What Generative AI can and can't do, according to AI writer #1
Generative AI is a powerful technology that has the potential to revolutionize almost every sector of our lives. From writing blog posts, creating images and videos, building songs based on a short melody, and helping developers plug code into their programs—generative AI can do it all. However, this raises the question—what are the limitations of generative AI? Let’s take a closer look at what generative AI is capable of and its boundaries.
What can generative AI do?
Generative AI can be used to automate tasks that would otherwise require human labor. It can be used to analyze large sets of data to identify patterns or trends that may not be obvious to humans, then implement those patterns and trends to create similar yet entirely new data. New data can take the form of novel digital content and data insights, such as insights into customer preferences and behavior which could help businesses better serve their customers and stay ahead of trends.What can’t generative AI do?
Generative AI is still limited in what it can accomplish due to its reliance on data-driven algorithms. While these algorithms may be able to recognize patterns or trends within data sets, they have difficulty understanding context when presented with new information or scenarios outside of their training parameters. This means that generative AI cannot draw conclusions or make decisions based on complex situations — something that only humans can do at present. Furthermore, generative AI cannot replace human creativity completely as it lacks the ability to come up with novel ideas or recognize abstract concepts such as humor or irony — all things which require a human touch.
In conclusion, while generative AI has the potential to revolutionize many aspects of our lives by taking over time-intensive creative tasks and providing business insights — it still has its limitations. There will always be some tasks which will require human intervention in order for them to truly succeed. As such, we must ensure that we use this tool responsibly if we want it to reach its full potential without sacrificing our own ingenuity in the process.
The limitations of generative AI, according to AI writer #2
Generative AI relies on pre-existing data to learn and identify patterns that it will then use to synthesize new data. For example, a machine learning algorithm can be used to generate new images based on a dataset of existing images. But as AI evolves and becomes more sophisticated, so does our understanding of its limitations.Limited training leads to limited range of outputs
For illustration purposes, let's focus on generative AI tools that can create images. While such tools can create novel images (i.e., images that aren't found in the AI's training dataset), there are limitations to what it can do. For example, a machine learning algorithm can only generate new images based on a dataset of existing images. This means that if the training dataset is limited in scope, so too will the generated images be.
For instance, if the AI's training dataset is comprised of run-of-the-mill bicycles, it'll be highly unlikely for the AI to create an image of a bike with hubless and spokeless wheels.
Generative AI can't generate new ideas or solutions
One of the key limitations of AI is its inability to generate new ideas or solutions. This lack of creativity is a result of the way AI is typically developed. Most AI systems are based on pre-existing data and rules, and the concepts of "breaking rules" and "thinking outside the box" are completely contrary to any computer programming.
To illustrate, if you train a generative AI-powered robot on the principles of how a Rubik’s cube can be twisted in order to be solved, it can generate countless solutions for every jumbled configuration you present it. However, it would never propose to smash the cube on the ground and reassemble it back together into its solved configuration.
Therefore, generative AI can only produce results that are similar to what has been done before. While this isn’t necessarily a bad thing, it does mean that AI still has some way to go before it can be truly considered intelligent in the way humans are.
Innovation breakthroughs lead to AGI
Nath 23 (Deepu S Nath is a programmer turned entrepreneur and a technology evangelist with over 18 years' experience in IT industry. ) “The Roadblocks to Achieving Artificial General Intelligence (AGI): Why we need more than just faster computers” Feb 21, 2023 https://medium.com/@deepusnath/the-roadblocks-to-achieving-artificial-general-intelligence-agi-why-we-need-more-than-just-e40593e5ec1f // ZX
Most AI systems today are designed to perform specific tasks, such as recognizing images or processing natural language. These models are not designed to be flexible or adaptable in the way that a human brain is and it is known as narrow AI, which is the primary type of AI that we currently have.
AI systems should be able to learn and generalize across different domains to achieve the desired form of AGI. Artificial General Intelligence (AGI) refers to a theoretical form of intelligence that can understand or learn or perform any intellectual task exactly as human beings do. It is a primary goal of some AI research.Need Breakthrough research in Machine Learning, Natural Language Processing, and Computer Vision
Achieving AGI will require significant breakthroughs in a variety of research areas, including machine learning, natural language processing, and computer vision.
We need to develop new algorithms, techniques, and architectures that can enable AGI to learn, reason, and adapt in a way that is similar to human intelligence.
Need Understanding how human cognition and thinking works
Through decades of research in fields such as neuroscience, psychology, and cognitive science, we have gained a significant understanding of many aspects of the brain’s structure and function, including the roles of different regions and the mechanisms underlying various cognitive processes.
This includes not only the way that our brains process information, but also the way that we perceive the world and make decisions. While there has been progressing in neuroscience and cognitive psychology, we are still far from a complete understanding of these complex processes.
A cross-collaboration with experts in other fields such as biology, psychology, and philosophy is required to gain a more comprehensive understanding of human cognition so that it can make a revolutionary understanding of how machines can be made to learn new things and identify its saturations on different thought processing aspects.Need for higher computing and other resources
To achieve Artificial General Intelligence (AGI), we will need access to higher computing resources than what is currently available with the existing computing architectures.
This means not only more processing power, but also more data storage and management, optimized computing architecture, and increased energy efficiency.
AGI leads to extinction---outweighs nuclear war
Mandel 2023 (Mandel is a researcher at Intelligence, Influence, and Collaboration Section, Defence Research and Development Canada
Department of Psychology, York University) 2023. “Artificial General Intelligence, Existential Risk, and Human Risk Perception” https://arxiv.org/pdf/2311.08698 // ZX
As with any modern technology, contemporaneous societal and civilizational pressures and scientific breakthroughs continue to shape the evolution of AI. Its effects, much like those of nuclear technology, can and likely will change substantially over time. For instance, the perceived necessity of securing a decisive military advantage over the Axis powers during the Second World War motivated the US’s development of nuclear weapons, but this gave way to a military arms race once the US and Soviet Union were locked into the decades-long Cold War. Since then, there has been an expansion of nuclear-armed states and the pressures for expansion continue. While the threat of mutually-assured destruction serves as a potent first-use deterrent, there have been several close calls that raise the question of how long humans can avoid a global nuclear catastrophe (i.e., a nuclear winter) given that there appears to be no harmonious “end of history” in sight (Barrett et al., 2013; Baum, 2015). Attention to existential risk posed by AI invariably concerns possible future AI rather than the current state of AI. Although current AI carries significant risk—for example, algorithmic processes may be unexplainable (Angelov et al., 2021; Vilone & Longo, 2021) or biased and possibly unfair (Fazelpour & Danks, 2021)—and possibly biased for inexplicable reasons (Bostrom & Yudkowsky, 2018)—this is not what most experts regard as existential risk from AI. Current AI does not pose an existential threat because its development has not reached the stage where machines have parity with human intelligence across a wide range of tasks and domains.
However, many forecasters believe that parity is not far off. Consider forecasting data from Metaculus, a reputation-based online forecasting and aggregation platform devoted to predicting the scientific and technological outcomes and their consequences (Aguirre, 2016). As of July 20, 2023, the median probability estimate of 1,041 Metaculus forecasters that human-machine parity in intelligence will be reached by 2040 was a staggering 92%.1The criterion for scoring parity as having occurred is based on the requirement that the machine system outperform at least two out of three graduate students drawn from elite universities and having backgrounds in physics, mathematics, and computer science, respectively, on a two-hour intelligence test. The machine and three humans could access the Internet but could not consult with other humans. Certainly, more stringent criterion for scoring intelligence parity would be expected to lower the pooled estimate. Nevertheless, other estimates based on takeoff speed models (e.g., Davidson, 2023) agree that by 2040, AGI will be capable of performing virtually all human tasks regardless of their intelligence requirements. Such models rest on many assumptions, however, and their coherence with forecasters’ estimates is just information, not proof. The prospect of intelligence parity is not necessarily bad. AGI that could think as intelligently as humans but at much faster speeds—what Vinge (1993) calls weak superhumanity —could accelerate scientific and technological progress that humans might have eventually made but over much longer times. This process has even been given the sticky name, PASTA—Process for Automating Scientific and Technological Advancement (Karnofsky, 2021). If AGI (or PASTA) were necessary to avert an existential calamity (or worse, a mass extinction), having it would be a very good thing. The differential clock speeds of humans and machines, however, casts doubt on the parity notion. As soon as AGI could perform as well on any intelligence test (broadly defined) as the best performing human, it will have exceeded human intelligence since, if nothing else, it will have faster-than-human speed.
Unlike humans, AGI would likely be able to access its source code, modify it, and replicate either original or modified copies. These abilities would set off a positive feedback loop constituting what Good (1966) referred to as an intelligence explosion. As a result, AGI would evolve very quickly to be strongly superintelligent; i.e., it would not only think faster, it would think smarter than the smartest human (Yukdowsky, 2008).It is the prospect of superintelligent AGI that raises concern regarding the continued existence our species and that has caused many AI experts and others alike to call for a pause in advanced AI development (e.g., Future of Life Institute, 2023). If AGI were more powerful than humans, a misalignment of human and AGI goals would likely not end well for humanity. This alignment problem—the question of whether AGI will become misaligned with human goals and values—is a threat analysis problem concerning a set of possible futures having devastating consequences for humans as a species. Since there are no tightly coupled reference classes to draw from in determining the probability of human extinction (or catastrophic demise) from AGI misalignment, estimating existential risk from AGI constitutes an extreme version of the reference class problem (Hájek, 2007; Reichenbach, 1949) with the various pro and con arguments drawing heavily on loose analogies. Therefore, it is of little surprise that estimates of risk vary greatly. A recent study by Karger et al. (2023) compared the forecasts of 32 AI experts, 48 experts on areas of existential risk other than from AI, and 89 “superforecasters” who demonstrated top tier forecasting skill on a wide spectrum of short-range topics (Mellers et al., 2015) and who tend to exhibit higher than average coherence across a range of probabilistic judgment tasks (Mellers et al., 2017).
The authors found that among various existential threats considered (i.e., AI, nuclear, biological, and climate), existential risk from AI prompted the greatest level of disagreement. The inter-quartile range of estimates of human extinction from AI by 2100, remarkably, was greater among AI experts than among other experts from other areas or superforecasters. As well, Karger et al. (2023) found that the largest absolute disagreement between superforecasters and area-relevant experts (i.e., AI experts for AI topics, nuclear experts for nuclear topics, and so on) focused on AI risk. However, this was largely attributable to the fact that both groups regarded the probability of extinction from AI to be larger than for other risks considered. For instance, whereas superforecasters’ median estimated risk of human extinction by 2100 due to AI was 0.38%, the comparable estimated risk due to nuclear technologies was 0.074%. Likewise, whereas AI experts’ median estimated risk of extinction by 2100 was 3.0%, the comparable risk from nuclear technologies forecasted by nuclear experts was 0.55%. Remarkably, among both superforecasters and domain experts, the judged probability of human extinction by 2100 due to AI is just over five times higher than the perceived probability of extinction due to nuclear technology. Other existential risks considered by the forecasters in Karger et al.’s study were estimated to be even lower than for nuclear technology, placing AI at the top of the “most probable” list of existential risks among experts and competent generalists (i.e., superforecasters).
2NC---Uniqueness
AI is declining now
Ropek 4/16 (Lucas Ropek is a staff writer for Government Technology. He has worked as a newspaper reporter and writer in Massachusetts and New York. ) “Actually, Corporate Investment in AI Saw a Significant Drop in 2023” Apr 16, 2024 https://gizmodo.com/actually-corporate-investment-in-ai-saw-a-significant-1851411926 // ZX
Every year, Stanford University’s Institute for Human-Centered Artificial Intelligence releases a big report about the status of the artificial intelligence industry. This year’s report, published this week, is a whopping 502 pages and includes a wealth of insights on the trendy technology. Tucked into its section on the economics of the AI industry is the surprising revelation that global corporate investment in AI actually dropped last year for the second straight year in a row. This might come as a surprise to some since 2023 was largely considered to be a period of peak AI hype. New chatbots and companies spawned aplenty, as top executives spread hyperbolic rhetoric about the technology’s potential. And yet, the new HAI report notes that in 2023, total investment in AI…
…dropped to $189.2 billion, a decrease of approximately 20% from 2022. Despite a slight reduction in private investment, the most significant downturn occurred in mergers and acquisitions, which fell by 31.2% from the previous year. However, over the past decade, AI-related investments have increased thirteenfold.
The peak of global corporate investment in AI was actually 2021, according to the report. During that year, total investment crested $337 billion. In 2022, it then dropped to $234 billion, before dropping another $40-ish billion last year.
Industry leaders facing funding reckoning.
Wiggers 24 (Kyle Wiggers is a senior reporter at TechCrunch with a special interest in artificial intelligence.) “Investors are growing increasingly weary of AI” April 15, 2024 https://techcrunch.com/2024/04/15/investors-are-growing-increasingly-wary-of-ai // ZX
After years of easy money, the AI industry is facing a reckoning.
A new report from Stanford’s Institute for Human-Centered Artificial Intelligence (HAI), which studies AI trends, found that global investment in AI fell for the second year in a row in 2023.
Both private investment — that is, investments in startups from VCs — and corporate investment — mergers and acquisitions — in the AI industry were on the downswing in 2023 versus the year prior, according to the report, which cites data from market intelligence firm Quid.
AI-related mergers and acquisitions fell from $117.16 billion in 2022 to $80.61 billion in 2023, down 31.2%; private investment dipped from $103.4 billion to $95.99 billion. Factoring in minority stake deals and public offerings, total investment in AI dropped to $189.2 billion last year, a 20% decline compared to 2022.
Yet some AI ventures continue to attract substantial tranches, like Anthropic’s recent multibillion-dollar investment from Amazon and Microsoft’s $650 million acquisition of Inflection AI’s top talent (if not the company itself). And more AI companies are receiving investments than ever before, with 1,812 AI startups announcing funding in 2023, up 40.6% versus 2022, according to the Stanford HAI report.
So what’s going on? Gartner analyst John-David Lovelock says that he sees AI investing “spreading out” as the largest players — Anthropic, OpenAI and so on — stake out their ground.
“The count of billion-dollar investments has slowed and is all but over,” Lovelock told TechCrunch. “Large AI models require massive investments. The market is now more influenced by the tech companies that’ll utilize existing AI products, services and offerings to build new offerings.”
Umesh Padval, managing director at Thomvest Ventures, attributes the shrinking overall investment in AI to slower-than-expected growth. The initial wave of enthusiasm has given way to the reality, he says: that AI is beset with challenges — some technical, some go-to-market — that’ll take years to address and fully overcome.
“The deceleration in AI investing reflects the recognition that we’re still navigating the early phases of the AI evolution and its practical implementation across industries,” Padval said. “While the long-term market potential remains immense, the initial exuberance has been tempered by the complexities and challenges of scaling AI technologies in real-world applications … This suggests a more mature and discerning investment landscape.”
Other factors could be afoot. Greylock partne

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