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In today’s world, access to affordable, high-quality legal advice can be a challenge. Hourly rates start at €150 up to €400 per hour. LexiCounsel wants to change this. Powered by generative AI, LexiCounsel provides 24/7 legal advice at a fraction of the cost of traditional lawyers, with the accuracy and reliability that users can expect from a legal professional.

At its core, LexiCounsel is about making legal advice accessible and affordable. By offering flexible subscription plans and a pay-per-use service, LexiCounsel ensures that anyone – whether a startup or an individual – can afford fast and reliable legal advice. With pricing as low as €5 per case, or €20 per month, it is almost guaranteed your legal fees will decrease.

But affordability doesn’t mean compromising on quality. By establishing partnerships with legislators, legal organisations and other government organs, LexiCounsel is guaranteed to be up to date. LexiCounsel’s chatbot will be trained on new laws long before they come into effect. New judgements, which may provide precedents for future cases, are uploaded to LexiCounsel within 24 hours. All this to ensure that LexiCounsel’s knowledge is up to date.

To test its quality, LexiCounsel will undergo regular testing by lawyers, attorneys, and legal experts. LexiCounsel is committed to a 98% accuracy rate in the legal advice it provides. This means that in the vast majority of cases, users can trust the information they receive to be as reliable as what they’d get from a human lawyer. By combining the expertise of legal professionals with the efficiency of AI, LexiCounsel ensures that its users are never left with outdated or incorrect information.

However, LexiCounsel isn’t just about answering questions—it’s about streamlining legal processes that often take hours of a lawyer’s time. Whether you need help drafting a contract, reviewing terms, or navigating compliance issues, LexiCounsel can handle it in minutes. This saves businesses both time and money, allowing them to focus on their core activities instead of getting bogged down in legal complexities.

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By focusing on affordability, quality, and accessibility, LexiCounsel is positioned to disrupt the legal services industry. For individuals and small businesses who might find traditional legal services inaccessible, LexiCounsel provides a cost-effective, always-available alternative that doesn’t sacrifice reliability. In short, it’s not just a cheaper way to get legal advice—it’s a smarter, more modern solution to legal challenges.

The future of legal services is changing, and with LexiCounsel, affordable, reliable legal help is finally within reach for everyone.

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The Model Collapse Theory highlights serious concerns about the future of generative AI and its reliance on high-quality human data. Below, the number of papers found by PubMed containing the word “delve” is shown. It does not take a background in statistics to note the large increases since 2023, the same year that ChatGPT gained popularity. Coincidentally, ChatGPT is known to use the word “delve” more frequently than humans.

The chart demonstrates that AI generated content has found its way into our everyday lives. The line between a response from ChatGPT and a piece written by a human is fading quickly. In 2023, an expert even estimated that 90% of online content could be generated by AI by 2025. This leads to an urging question: can we even distinguish AI generated from human created content anymore?

The crux of the issue lies in how generative AI models are trained. Generative AI models require vast datasets of high-quality human content to train on. Human content is more nuanced, creative, and reflective of the real world. However, as more AI generated content is produced, it becomes increasingly more difficult to keep AI generated content out of the datasets. Training generative AI models on AI generated data has been shown to decrease both the quality and diversity of AI generated content (Briesch et al., 2023). This results in a feedback loop where the AI recycles patterns without innovation, leading to stagnation. In short: using AI to generate content has polluted the very data sources needed to train future generative AI models.

In response, GenAI companies have been looking for solutions. This sparked a race to secure exclusive partnerships with organizations who can provide human created data. These partnerships may postpone the issue, but do not solve it. At some point, more data will be needed, and proper data sources will become increasingly difficult to find. The only remaining option will be to use the content that is available, which includes AI generated content.

Some experts warn that the influx of AI generated content will slow down generative AI advancement – or potentially even bring them to a halt. Without sufficient high-quality human data, we risk a future where genAI models produce content that lacks depth and creativity. From my personal use, I found that the lack of creativity of GenAI is one of its greatest limitations. I think that genAI could provide much more value if it could be more innovative in its output. To think that generative AI might become less creative is a serious concern for me.

But how do you assess the risk of Model Collapse? Is Model Collapse a real threat, or do you have a solution in mind?

Sources: 

Briesch, M., Sobania, D., & Rothlauf, F. (2023, November 28). Large language models suffer from their own output: An analysis of the Self-Consuming Training Loop. arXiv.org. https://arxiv.org/abs/2311.16822

Banner: https://www.thedigitalspeaker.com/content/images/2023/06/Danger-of-AI-Model-Collapse-Futurist-Speaker.jpg

Chart: https://www.linkedin.com/posts/marnimolina_the-rise-of-delve-in-scientific-literature-activity-7186362840360869888-Sz16

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The past year, I have been using Soulie as social media. Soulie was an app that introduced “anti-mindless scrolling”. However, the app closed down on July 1st. This blog post will look at the idea behind Soulie, and why the “anti-mindless scrolling” application didn’t work out.

So what was the idea behind Soulie? Soulie aimed to give users more control over the content they were shown. With sliders, users can select the mood of the content (positive, neutral or negative) and how easy the content is to read (intellectual vs relaxed reading). Users could also select the specific fields they wanted to receive content from, such as: finance, art, and tech.

Based on the user’s preferences, Soulie recommends 10 articles or videos. Although users can refresh for 10 different articles, the number of refreshes are limited each day. After a user has read an article, they review it with a small survey. This gives Soulie some indication of success, and forces the user to evaluate what they just read. Users can also share articles with each other and comment on them.

So why did Soulie not work out? Initially, there was a lot of interest. During market research an overwhelming 84.7% of respondents claimed to have a problem with mindless scrolling. The beta test showed that 85% of users that signed up also actually started using the app. The main issue turned out to be retaining users, as the retention rate was around 5%.

This may give us an indication of why Soulie did not work as an alternative to other apps like Instagram and TikTok. Apps like Instagram and TikTok rely on an addictive experience. Soulie’s experience was not addictive at all. From my experience, using Soulie felt similar to reading a newspaper. Addictions are incredibly hard to break, and as the novelty of Soulie fades, users return to their default apps.

When Soulie was launched, its team introduced a subscription model. Although there had been a lot of interest in Soulie, very few people were willing to pay for it. In hindsight, this seems easy to explain: why pay for social media when there are free competitors? Ultimately, Soulie ran out of capital, and could not sustain its development. The app was shut down on July 1st, 2024.

What lessons can be learned from Soulie? First, it highlights the entry barrier to social media. Acquiring and retaining users is extremely difficult without a large marketing team or addictive product. Second, it emphasizes the challenge of monetization in the field of social media. Users are accustomed to free services and balk at the idea of paying for social media. Soulie’s subscription service was a deterrent to mass adoption. Lastly, Soulie’s example poses the question whether mindful scrolling is even a need that people have identified on a large scale. Despite a lot of interest, most people returned to their old habits shortly after signing up for the app. From my own experience, I occasionally used Soulie, but kept Instagram as my default social media. Soulie could be compared to some gym memberships; signed up for with good intentions, but ultimately largely unused.

In the end, Soulie serves as a reminder that, although there is an interest in mindful social media, competing with tech giants for attention, particularly without an addictive product, is an uphill battle.

What do you think of the concept of mindful scrolling? Do you think a platform for intentional social media would have been viable under different conditions, or is it a lost cause?

References:
[1] https://souliehq.medium.com/results-soulie-social-media-survey-2023-part-i-687b594e4e09