In an increasingly digitized age, the ability to sift through vast oceans of information becomes not just a luxury but a necessity. While platforms like TikTok and Spotify have given us a glimpse into the power of personalized content, I envision a future dominated by the “Personal AI.” This isn’t merely an upgrade of our digital assistants but a transformative leap, a system intricately woven into our daily lives, becoming as indispensable as the air we breathe.
Imagine a day where you wake up to news snippets, not based on global popularity but handpicked based on your past interests, the book you read last night, or even the mood inferred from your sleep pattern. This Personal AI would be a mosaic of your online interactions, preferences, professional requirements, emotional states, and even aspirations. It would not just answer your questions but anticipate them. Want to cook a new dish? Personal AI might suggest recipes based on your current health metrics and what’s available in your fridge, or perhaps even draw from a food memory you mentioned in a casual conversation days ago.
However, diving into the potential risks of software failures, the scenarios become daunting. A misjudgment could lead to misinformation; for instance, prescribing a wrong health regimen based on flawed data interpretation. In financial matters, a software glitch might result in incorrect financial advice, leading to potential monetary losses. If it misinterprets emotional states, it might end up exacerbating mental health issues rather than aiding them (4).
Amidst the splendor of a hyper-personalized AI experience lies a challenging dichotomy: the need for vast reservoirs of personal data. This in itself is a double-edged sword. To sculpt an AI this attuned to our needs, it must be fed a relentless stream of our daily interactions, preferences, conversations, health metrics, and more (2). For many, this could tread dangerously close to the boundaries of privacy infringement. After all, in this digital era, data is the new currency. A breach or leak of this magnitude would be catastrophic, handing over the intricate tapestry of a person’s life to potentially malevolent actors (2). Encrypted storage and transmission of this data aren’t just a good practice; they become the bedrock of trust. Without the absolute assurance of end-to-end encryption and cutting-edge cybersecurity measures, the entire premise of Personal AI collapses (3). A single leak could not only compromise personal and financial details but also expose the deeply private emotional and mental states of an individual; and it’s happened before (1)! The ramifications of such a breach are not merely transactional but deeply human, potentially leading to manipulations, blackmail, and profound psychological impacts. Thus, while the promise of Personal AI is tantalizing, its foundation must be unshakeable security and respect for individual privacy.
Even if we reach our magnum opus with Personal AI, there are still ways for it to be misused, and have negative impact. For instance, an individual might inadvertently feed the AI system with biased or incorrect data, leading the AI to make decisions that aren’t optimal or even harmful. A classic example is when a user continually engages with polarizing content, which might cause the AI to develop a tunnel-visioned perspective, reinforcing only one-sided viewpoints and inhibiting exposure to a diverse range of ideas. In another scenario, a user might misinterpret AI-generated advice, thinking a recommended course of action is the definitive best choice without considering nuances or external variables. This can lead to over-reliance on the AI, sidelining human judgment and critical thinking, which are vital in complex decision-making scenarios.
Weighing the pros and cons, the question of market viability for Personal AI becomes complex. On one hand, the risks associated with software glitches in such an intimate system are enormous. But, let’s juxtapose this with human-operated systems. Humans, by nature, are fallible. We forget, misjudge, and sometimes let emotions cloud our decisions. Personal AI, in its ideal form, offers a respite from these innate human flaws. Moreover, the asymmetric upside of a technology like this is too magnificent to be unexplored, especially in public consumer use-cases, which is why it should exist.
As we stand at the cusp of a revolution with Personal AI, the pressing need for unyielding security and a deeply ingrained ethical framework cannot be overstated. The promise of technology always comes with the responsibility to wield it judiciously. Especially with AI, where the line between machine and human blurs, we must establish stringent measures of data protection and consistently evaluate the moral implications of our advancements. This principle extends beyond just Personal AI and speaks to the heart of all software development. Every line of code, every algorithm, every application should be a testament to our commitment to safeguarding user trust and upholding the highest ethical standards. In our relentless pursuit of innovation, we must ensure that the very essence of humanity remains inviolate. As we navigate the frontier of safety-critical software, it’s a timely reminder that the software isn’t merely tools but extensions of ourselves, and they deserve the same considerations of security, privacy, and dignity as we would demand for any individual.
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“Apple Data Breaches: Full Timeline through 2023.” Firewall Times, 11 July 2023, firewalltimes.com/apple-data-breach-timeline. Accessed 25 Oct. 2023.
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Li, Haoran, et al. “Privacy in Large Language Models: Attacks, Defenses and Future Directions.” ArXiv.org, 2023, arxiv.org/abs/2310.10383. Accessed 25 Oct. 2023.
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Qi, Xiangyu, et al. “Fine-Tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!” ArXiv.org, 2023, arxiv.org/abs/2310.03693. Accessed 25 Oct. 2023.
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Timo Minssen, et al. “The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models.” JAMA, vol. 330, no. 4, 25 July 2023, pp. 315–315, jamanetwork.com/journals/jama/article-abstract/2807167, https://doi.org/10.1001/jama.2023.9651. Accessed 25 Oct. 2023.
** Plenty of benefits/ideas here:
- replace yourself in conversations that you don’t have time for
- trust your replacement for mundane tasks like writing applications, emails, maybe meetings
Some concerns:
- What if I don’t like myself
- What if my clone ended up being a misrepresentation of who I am
- worse, or better
- a sense of ego to be gained/lost
- worse, or better
Open questions:
- should I train on all media output, or just the favourites/popular ones?
- I want to make a high quality version of myself
- should I let my clone be stuck in time, or also grow along myself
Building this is the epitome of building things for yourself
Where to scrape my data from:
- iMessages
- Discord Messages
- Instagram Messages
- Twitter DMs
- linkedin messages
- Messenger messages
- Tweets
- Obsidian
- Website
- Google Drive