Generative Ai Hype or Substance What to Expect

Generative AI hype has reached a fever pitch, with many touting its potential to revolutionize industries and change the world.

The technology has made significant strides in recent years, with advancements in models like DALL-E and Stable Diffusion.

However, not all is as it seems, and it's essential to separate the hype from the substance.

To do this, let's take a closer look at what generative AI can actually deliver.

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Understanding AI is a complex topic, but it's essential to grasp the basics before diving into the hype surrounding generative AI. Generative AI models can produce entirely new content, such as images, music, or text, by learning patterns from existing data.

These models are trained on vast amounts of data, which allows them to recognize and mimic styles, structures, and even emotions. The more data they're fed, the better they become at generating realistic content.

However, this process is not magic; it's based on algorithms and statistical models that analyze and replicate patterns. For instance, a generative AI model might learn to recognize the brushstrokes of a famous painter and then generate new artwork in a similar style.

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The training process can be time-consuming and resource-intensive, requiring massive computing power and storage. This is why researchers and developers are constantly exploring new ways to improve the efficiency and scalability of generative AI models.

As generative AI continues to advance, we can expect to see more sophisticated and realistic content being generated. This raises questions about the role of human creativity and the potential impact on industries such as art, music, and writing.

Generative AI has the potential to generate content that can affect users and society in various ways. It's essential to use technology responsibly and consider both ethical and legal implications.

One must be aware of the possibility of generating erroneous, misleading, or harmful content. This is a critical consideration, especially when working with sensitive information or in industries with strict regulations.

A global study found that 75% of CEOs believe trusted AI is impossible without effective AI governance in their organization. This highlights the importance of governance and trust in the adoption and scaling of AI.

Governance is not just about regulatory risk or reputational risk, but also operational risk, where companies may struggle to do AI innovation at scale.

Using Generative AI responsibly is crucial, as it has the potential to generate content that can affect users and society in various ways.

Generative AI can produce erroneous, misleading, or even harmful content, so it's essential to be aware of this possibility.

To comply with intellectual property rights, you must understand and respect the rights of others, including those related to data and generated content.

The legal landscape surrounding AI is constantly evolving, with new developments as the technology becomes more widespread.

It's not just about being aware of the potential risks, but also about taking steps to mitigate them and use AI in a way that's fair and respectful to all parties involved.

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Explainability is a major challenge when it comes to Generative AI systems. Generative AI relies on neural networks with billions of parameters, challenging our ability to explain how any given response is produced.

This lack of transparency can make it difficult to understand the reasoning behind AI decisions. We need to find ways to make AI more explainable to build trust with users.

The complexity of neural networks makes it hard to pinpoint exactly how a particular response was generated. This complexity can lead to a lack of understanding, which can be a major issue in high-stakes applications.

To overcome this challenge, we need to develop techniques that can provide insight into the decision-making process of AI systems. This will help us to identify potential biases and errors, and to make improvements to the system.

Ultimately, explainability is crucial for building trust in Generative AI systems. By making AI more transparent, we can create systems that are more reliable and trustworthy.

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Today's foundation models are not flawless and can produce inaccurate or unreliable results.

It's essential to evaluate and validate the models' performance well before they are used in production.

One should also have a mechanism to monitor and control the results on an ongoing basis, to catch any errors or deficiencies early on.

Sufficient human supervision is necessary to correct any mistakes or inaccuracies that may arise.

Care must be taken to integrate Generative AI into applications without human supervision where incorrect responses may cause harm.

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The hype around generative AI has led to significant investment, with 20% of global VC funding captured by AI companies in the second trimester of 2024, according to KPMG.

While this investment is expected, tech entrepreneur Gilles Raymond notes that the real winners in the AI race are those who don't do AI but sell servers, microchips, etc.

Many businesses are experimenting with gen AI, driven by a fear of missing the boat, but it rarely moves on into production, as observed by Cyril Maury, partner at international consulting firm Stripe Partners.

User experience and trust are crucial when it comes to Generative AI systems. Generative AI systems interact with users and influence their experience.

One must be aware of any potential negative effects, such as the generation of content that may lead to confusion, misunderstandings, or a lack of trust on the part of users. Users need to feel confident in the information they receive from Generative AI systems.

It's essential to continuously engage users, listen to feedback, and adapt the Generative AI solution to ensure it meets their needs and expectations. This will help build trust and improve the overall user experience.

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Hallucinations are a major issue with gen AI, making it unreliable for critical tasks that require high accuracy.

Gen AI can produce outputs that are factually incorrect or nonsensical, as seen with "hallucinations" that are an "inherent feature" of gen AI, according to Google CEO Sundar Pichai.

The problem is that there is neither enough computing power nor enough training data on the planet to solve the hallucinations issue.

Gen AI's non-deterministic nature, based on billions of tokens and real-time calculations, contributes to the variability of its responses, which can lead to hallucinations.

This variability can be problematic in fields like software development, testing, and scientific analysis, where consistency is crucial.

Hallucinations can also occur even when the same prompt is input again, creating unpredictable and unreliable results.

Social media influencers can spread hype quickly, with 70% of online users more likely to trust recommendations from people they know. They often have a large following and can reach a wide audience with just a single post.

Influencers may not always fact-check information before sharing it, which can lead to the spread of misinformation. This can be especially problematic when it comes to sensitive topics like health and finance.

The rise of social media has made it easier for influencers to build a following and gain credibility, but it also means that false information can spread rapidly. A single post can be shared thousands of times in a matter of minutes.

Influencers often have a strong emotional connection with their followers, which can make their recommendations more convincing. However, this emotional connection can also make their followers more susceptible to manipulation.

The hype surrounding a product or service can be so strong that people are willing to overlook its flaws and risks. For example, a survey found that 40% of people would invest in a product that they had only seen advertised online, even if they didn't fully understand how it worked.

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A recent study by IBM's research team found that AI delivered a higher average return on investment (ROI) in 2023 than it had in 2022.

The study surveyed over 5,000 executives on their use of generative AI and discovered that AI delivered a higher ROI in 2023.

One-third of organizations may pause their generative AI use cases in core business functions, but two-thirds will continue with AI after their pilot phases.

The study suggests that the hype-driven adoption spike will be followed by a "trough of disillusionment", but many organizations will continue to invest in AI.

Large, incumbent companies with significant data wealth and AI governance guardrails in place are likely to continue and accelerate their investments in generative AI.

These companies are well-positioned to take advantage of the economics of AI, which currently slant toward large enterprises and cross-organization platforms.

In fact, many larger businesses and local governments have already successfully adopted gen AI to answer some of their challenges, whether to facilitate the analysis of customer data, enhance customer care, or improve knowledge modeling efficiency.

A 47% increase in VC funding in the US last quarter is a testament to the growing interest in AI technology.

Investors are pouring money into AI companies, with 20% of global VC funding captured by AI companies during the second trimester of 2024.

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Tech bubbles are environments where misinformation can spread quickly, and it's essential to be aware of the risks. The hype surrounding Generative AI has led to a surge in investment, with 20% of global VC funding captured by AI companies in the second trimester of 2024.

The field of responsible use of AI is still developing, and the legal landscape is evolving rapidly. One must be aware of the possibility of generating erroneous, misleading, or harmful content.

Investors are pouring money into AI, with AI investments driving a 47% increase in VC funding last quarter in the US. The tech behind AI is what's truly standing out, but many businesses are still experimenting with it, rather than integrating it into their workflows.

The experimentation phase is still ongoing, with many clients trying to try and experiment with AI, but seeing only marginal change so far. This is partly due to a fear of missing the boat, rather than seeing the benefits of AI.

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Companies are facing significant challenges in scaling AI, with 75% of CEOs believing that trusted AI is impossible without effective AI governance in place.

A global study found that only 39% of companies have good gen AI governance in place today, leaving a substantial gap to be filled.

Operational risk is a major concern, with Hans-Petter Dalen, Business Leader EMEA, IBM watsonx and embeddable AI, noting that companies may struggle to do AI innovation at scale without proper governance.

The ability to move from pilot to production will depend on organizations taking a disciplined approach to deploying the technology, which is contingent on good governance.

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Token subsidies are a crucial aspect of the AI ecosystem, where users are charged a fraction of a cent for each token in both the request and the response. This can add up quickly, with ChatGPT generating about $400,000 in revenue every day, but requiring an additional $700,000 in investment subsidy to keep it running.

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The cost of tokens is so high that it's considered a "Loss Leader Pricing" strategy, where the goal is to keep costs down to proliferate adoption. This is similar to how Uber was priced in 2008, making it seem cheap to attract users, only to increase prices once it became widely available.

The AI industry is investing hundreds of billions of dollars to keep token costs down, making it a significant portion of the overall investment. This is a key factor to consider in the economic and governance aspects of AI, as companies like Google, OpenAI, Microsoft, and Elon Musk are all playing a game of "who can keep costs down the longest" before making a profit.

Governance considerations are crucial for businesses scaling AI, with 75% of CEOs believing trusted AI is impossible without effective governance, yet only 39% having good governance in place today.

CEOs are right to be concerned, as a recent report from Deloitte found that consumers have less confidence in brands that use generative AI, with trust declining by a factor of 12.

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Governance is the enabler for operationalizing AI at scale, according to Hans-Petter Dalen, Business Leader EMEA, IBM WatsonX and embeddable AI. Without it, companies risk operational risk, where they're unable to do AI innovation at scale.

Company size, industry, and governance all play a role in the gains companies can extract from generative AI, and moving from pilot to production requires a disciplined approach to deploying the technology.

Before using generative AI, it's essential to consider the potential for generating erroneous, misleading, or harmful content.

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