Revolution Digital

Marketing & Design Executive

Peritus Learning & Development

Marketing Executive

Eazi-Business Ltd

Online Insulation Ltd

Simulaids Ltd

Marketing Co-ordinator

AI/Tech Ethics and Implementation

Hello, 

With a focus on the ethics and practical implementation of AI, I create content that challenges our perceptions and pushes the boundaries of what's possible. My content ranges from thought-provoking articles to insightful commentary on the latest AI developments, always with an emphasis on responsible innovation and the ethical considerations that come with it.

P.S. Your insights and inquiries are invaluable to me. Feel free to contact me anytime.

For years 

we've heard 

the mantra that our personal data shared on digital platforms is "free" for us to use. But this supposed free access has come at a hidden cost - our privacy.

The tech giants of Silicon Valley have built their empires by capitalising on the mountains of data we willingly provide through our online activity. They have used personal information to craft ultra-targeted advertising, raked in profits, and manipulated user behaviour. 

Increased public awareness of data privacy issues, coupled with stricter regulations like the EU's GDPR, have forced large tech companies to change course. 

Constrained from their usual data harvesting, companies like Google and Meta are now seeking licensing deals and other arrangements to compensate users for access to their information.

This is a tacit acknowledgment that our data holds immense inherent value. It is the lifeblood fueling the AI revolution that will determine the future of these tech empires. 

This new era of data licensing deals raises questions. Are these arrangements truly transparent, with users giving meaningful informed consent? Is our privacy being adequately protected, or are we simply trading one exploitative model for another?

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Dean Hardy-White

Ethics in AI or tech 

often become neglected. 

Time and again, decision-makers in these tech powerhouses frequently overlook issues like privacy violations and copyright infringement, prioritising corporate gains and market share over maintaining ethical integrity.

Meta's track record is a consistent, company-wide preference for profit maximisation over upholding ethical practices. 

This issue extends far beyond just one tech giant.

OpenAI and Google have also faced public scrutiny and backlash. Allegations (ahem) of using copyrighted material without permission and making clandestine changes to their data policies for the purposes of training powerful AI models shows us how they treat ethical standards and principles in the relentless pursuit of market dominance.

Without strong, enforceable ethical or moral frameworks firmly in place, the tech industry as a whole risks descending into a 'free-for-all' scenario marked by unchecked corporate greed and a disregard for the wellbeing of users and the general public. 

Conventional remedies like government regulations, the establishment of ethical review boards, or the threat of legal action have thus far proven largely ineffective in curbing this.

The harsh reality is that even companies with ostensibly noble, ethical aspirations may ultimately be compelled to make ethically questionable choices in order to survive and thrive in the current market landscape.

The question remains: is it already too late to instigate a critical ethical revolution within the tech industry?

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Here is a developing list

of the custom AI chip race:

——

1. With over 90% of AI-focused GPUs sold by NVIDIA, a new trend is emerging where key GPU users are commissioning their own specialised AI chips to offer better-priced performance for their customers' use cases.

2. Apple plans to overhaul its entire Mac product line with AI-focused M4 chips, and eventually aims to bring AI chips to its phones as well. Apple's efforts in this domain have been ongoing since the mid-2010s.

3. Amazon are building custom AI training chips (Trainium) and inference chips (Inferentia) to optimise price-performance for their customers' needs.

4. Google has its own TPU technology for its AI needs and has recently announced Axion, its first ARM-based data center processor designed to optimize more economical computing needs.

5. Taiwan Semiconductor Manufacturing Company (TSMC) might still be the top choice for manufacturing these specialised AI chips, as the company is executing expansion plans with geopolitical diversification as a priority.

6. TSMC has transformed the small Japanese farm town of Kikuyo into a key node in its supply chain and has established production bases in the United States.

7. As tech giants continue to invest in developing their own AI chips, the AI hardware market is set to evolve rapidly.

8. While NVidia's dominance in AI compute may not be challenged in the near future, it creates opportunities to lower the costs and improve energy usage for the AI future.

9. The emergence of custom-built AI chips by tech giants suggests a growing demand for specialised solutions for specific customer needs and use cases.

10. The race to develop and deploy specialised AI chips reflects the increasing importance of AI-powered computing in various industries and the constant pursuit of performance, efficiency, and cost optimisation.

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P.S. Please find sources in the comments 

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Here is a list of top 10 ways 

in which scientists use LLMs

1️⃣ A recent assessment by Stanford University reveals a significant uptick in the use of LLMs across scientific research domains.
2️⃣ Researchers examined nearly one million papers published between January 2020 and February 2024 across arXiv, bioRxiv, and the Nature portfolio of journals.
3️⃣ The study highlights a remarkable surge of up to 17.5% in LLM usage within computer science papers.
4️⃣ While computer science exhibits the highest adoption, other domains like mathematics and the Nature portfolio demonstrate comparatively lower rates.
5️⃣ The effectiveness of AI-generated writing detection tools remains uncertain, with conventional detectors often deemed unreliable. 
6️⃣ Despite a lower instance rate, the bioRxiv corpus exhibits significant LLM usage, hinting at untapped potential for AI integration in biological research and manuscript writing.
7️⃣ The Nature portfolio shows relatively low LLM usage, indicating a potential influence of the peer review process in discouraging reliance on AI-generated content.
8️⃣ Findings from a 2023 Nature survey reveal that nearly 30% of scientists have leveraged generative AI tools for manuscript writing.
9️⃣ Over half of researchers cite translation as a key benefit of AI tools, showcasing their potential to streamline language-related tasks in scientific communication.
🔟 Concerns persist regarding the over-reliance on pattern recognition, potential bias entrenchment, and the need for research practices to mitigate these challenges.

——

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——

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Anthropic’s latest research paper 

explores a new technique known as 

Many-shot jailbreaking. 

By using extensive context windows available in LLMs, this method improves performance and introduces security vulnerabilities at the same time.

↳ How It Works

🔹The technique uses the LLM’s long-context windows to its advantage.

🔹Attackers feed the model with fake dialogues that make the model seem to agree to dangerous requests.

🔹These dialogues cleverly circumvent the model’s safety mechanisms.

↳The Risks

🔸As the number of dialogues increases, so does the likelihood of the model generating harmful responses.

🔸The paper points out a significant trade-off—larger context windows make LLMs more effective but also more prone to manipulation.

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P.S. What are your thoughts? 

(Please find the research paper in comments)

——

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AI is directing 

the bombing of Gaza

and it is called 'Lavender', 

according to an article by +972 Magazine. 

The AI targeting system has been used by the 

Israeli army to target tens of thousands of Gazans.

——

1️⃣ A book penned by a high-ranking figure within Israel's elite intelligence unit 8200 revealed plans for an AI-driven system to rapidly generate military targets in times of conflict.

2️⃣ An investigation by +972 Magazine and Local Call uncovered the existence of "Lavender," an AI-based program developed by the Israeli army for targeting suspected Hamas and Palestinian Islamic Jihad fighters.

3️⃣ Lavender marked over 37,000 Palestinians as potential targets for airstrikes, including low-ranking operatives, with minimal human oversight.

4️⃣ Military officers often spent just seconds approving targets marked by Lavender, leading to a surge in airstrikes on suspected militants, primarily in their homes.

5️⃣ The broad use of Lavender-led targeting resulted in staggering civilian casualties, with thousands of Palestinians killed, including women and children.

6️⃣ Following a deadly assault by Hamas in October, the Israeli army designated all operatives of Hamas' military wing as targets, expanding the scope of the conflict.

7️⃣ Lavender analyses data collected through mass surveillance on Gaza's population, assigning a likelihood rating of militancy to each individual.

8️⃣ Lavender identifies potential targets based on various features associated with known militants, such as social media connections, phone usage patterns, and frequent address changes.

9️⃣ Lavender fulfills the ambition outlined in "The Human-Machine Team" by enabling the creation of tens of thousands of targets per day.

🔟 The use of automated targeting systems like Lavender raises ethical questions, as civilian casualties surged due to indiscriminate airstrikes.

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P.S. Please find the article in the comment section. 

——

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Top 10 Challenges

Facing Generative AI

——

1. Confabulation: Generative AI often generates false information, leading to credibility issues and potential misinformation dissemination.

2. Interface Compatibility: Inadequate integration with external tools prevents seamless interaction with broader data ecosystems.

3. Stability Concerns: Generative AI's instability over time poses challenges for its reliability and suitability for long-term applications.

4. Scaling Limitations: While scaling up AI systems has benefits, it doesn't guarantee improvements in critical areas like honesty, harmlessness, and bias mitigation.

5. Autocompletion vs. Factual Accuracy: Existing AI technology relies heavily on autocompletion rather than ensuring factual accuracy, raising concerns about reliability.

6. Unsolved Problems: Despite years of research, significant challenges persist, indicating that solutions to complex issues aren't imminent.

7. Over-reliance on Technoptimism: Optimism about AGI's imminent arrival overlooks the practical barriers and unresolved issues within generative AI.

8. Impact on Industry: Speculation surrounding AGI drives inflated stock prices and startup valuations, creating a misleading narrative about AI's capabilities.

9. Shift in Perspective: A growing awareness among tech leaders about the challenges underscores the need for a more realistic approach to AI development.

10. Future Outlook: Addressing these challenges requires a concerted effort to prioritise research and development efforts, ensuring that AI advancements benefit society responsibly.

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