“Since AI has been around for many years already, I expect a comparable diffusion in one or two years.”
We spoke about this in the EM Interview in February 2022.
https://link.springer.com/article/10.1007/s12525-021-00516-w
“Since AI has been around for many years already, I expect a comparable diffusion in one or two years.”
We spoke about this in the EM Interview in February 2022.
https://link.springer.com/article/10.1007/s12525-021-00516-w
Adopting large language models (LLMs) is not without its challenges, especially for enterprises.. To overcome these challenges and achieve maximum benefit from these models, the combination of composite AI with large language models is the best way forward for enterprises.
Better Control and Customization:
One of the main benefits of combining composite AI with large language models is the ability to have better control and customization over the models. Composite AI allows enterprises to combine multiple AI models to create a custom solution that fits their specific needs. This is particularly important for large language models, which can be too generic and may not provide the level of control that enterprises require.
Improved Accuracy and Performance:
Another benefit of the combination of composite AI with large language models is improved accuracy and performance. Large language models can generate huge amounts of data, which can be difficult to manage and interpret. Composite AI allows enterprises to use multiple models to analyze and interpret the data generated by large language models, leading to improved accuracy and performance. This is particularly important for applications such as customer service, where the accuracy of the model’s responses can have a significant impact on customer satisfaction.
Better Data Privacy and Security:
Data privacy and security are major concerns for enterprises when it comes to adopting large language models. Composite AI allows enterprises to control the data that is used by the models and ensures that sensitive information is properly secured. This can help to mitigate the risks associated with large language models and ensure that enterprises can adopt these models with confidence.
Lower Costs:
Adopting large language models can be expensive, both in terms of hardware and software costs. By combining composite AI with large language models, enterprises can reduce these costs and achieve more cost-effective solutions. This is because composite AI allows enterprises to use multiple models, each of which can be optimized for specific tasks, reducing the overall cost of the solution.
Better Integration with Existing Systems:
Large language models can generate huge amounts of data, which can be difficult to manage and integrate with existing systems. Composite AI allows enterprises to integrate multiple models and manage the data generated by large language models more effectively. This leads to better integration with existing systems and ensures that the data generated by the models is properly stored and managed.
In conclusion, the combination of composite AI with large language models is the best way forward for enterprises adopting large language models. By adopting this approach, enterprises can maximize the benefits of large language models and ensure that they are delivering the results they need.
PS: This is btw the opinion of ChatGPT itself
Adopting large language models (LLM) such as ChatGPT in an enterprise is not without its challenges. In this post, we’ll discuss some of the key challenges that enterprises face when it comes to adopting large language models and how they can overcome them.
Data Privacy and Security Concerns:
One of the biggest challenges that enterprises face when adopting large language models is data privacy and security. These models are trained on massive amounts of public data. For a LLM to become useful in the enterprise context they need to be retrained on often sensitive information such as personal data, financial information, and confidential business information. To mitigate these concerns, enterprises need to ensure that their data is properly secured and that the models are not accessing or using sensitive information without permission. This requires implementing robust security measures such as encryption, data masking, and access controls.
Integration with Existing Systems:
Another challenge that enterprises face when adopting LLMs is integration with existing systems. Large language models can generate huge amounts of data, which can be difficult to manage and integrate with existing systems. Enterprises need to ensure that the data generated by the models is properly stored and managed, and that it can be easily accessed and integrated with existing systems such as databases and analytics platforms.
Cost:
Large language models can be very expensive, both in terms of hardware and software costs. Enterprises need to ensure that they have the budget to purchase and maintain these models, as well as the infrastructure to support them. This can be a significant challenge, especially for small to medium-sized enterprises.
Skills Shortage:
Another challenge that enterprises face when adopting large language models is a skills shortage. There is a lack of talent with expertise in these models, which can make it difficult to implement and use them effectively. Enterprises need to invest in training and development programs to ensure that their teams have the necessary skills to use these models effectively.
Bias and Halluzination:
Large language models can be biased due to the data they are trained on, which can lead to incorrect results. Enterprises need to ensure that their models are trained on unbiased data and that the predicted results from the LLM are corroborated against actual data in the enterprise.
In conclusion, while large language models offer significant potential benefits to enterprises, there are several challenges that need to be overcome in order to adopt them effectively.
ChatGPT is one of the most advanced language models in the world. While it is often referred to as a “AI language model,” it is important to note that it is not truly autonomous or intelligent in the same way that humans are. Instead, ChatGPT can be thought of as a highly sophisticated “stochastic parrot.”
A parrot is a bird that has the ability to imitate sounds and mimic speech. While a parrot may seem like it is speaking of its own accord, it is actually just repeating what it has heard before. Similarly, ChatGPT is a model that has been trained on a large dataset of text and has learned to generate responses based on the patterns it has seen in that data.
However, unlike a parrot, ChatGPT uses probabilistic methods to generate its responses. This means that instead of simply repeating what it has seen before, it generates new responses by predicting what is most likely to come next based on the input it receives. This is why ChatGPT is referred to as a “stochastic” model – it generates responses based on probability rather than determinism.
While ChatGPT is extremely advanced and can generate responses that are very human-like, it is still limited by the data it was trained on. For example, if the data it was trained on contains biases or inaccuracies, ChatGPT will also generate biased or inaccurate responses. Additionally, since ChatGPT has not had direct experiences in the world like humans have, it can sometimes produce responses that are nonsensical or that lack context.
Despite these limitations, ChatGPT is still a valuable tool for a wide range of applications. For example, it can be used to generate natural language responses in customer service interactions, to help with content creation, or to assist with language translation. However, it is important to keep in mind that ChatGPT is not a replacement for human intelligence – it is simply a tool that can be used to augment human capabilities.
In conclusion, ChatGPT can be thought of as a highly sophisticated “stochastic parrot.” While it is an impressive model that can generate human-like responses, it is limited by the data it was trained on and is not truly autonomous or intelligent. However, it remains a valuable tool for a wide range of applications and has the potential to greatly augment human capabilities.
PS: The text was generated by ChatGPT….
Robert Solow, an economist, said in 1987 that computers show up everywhere except for the productivity statistics. It took a good two decades at the time for this to fundamentally change.
I predict the same “Solow Paradox” will apply to AI and its newest incarnation LLMs (of which GPT is a variant). Over the next few years a lot will happen in terms of adoption that will not show up positively in productivity statistics (in some cases it will actually show up negatively… 😉).
The difference will be that gains will start to show up at the end of a single decade of adoption instead of two.
In the finance industry a fat finger event describes a typo with consequences. A few years ago, a Deutsche Bank trader mistakenly transferred 6 billion.
Prediction: This is exactly what will happen with GPT as well. Actually, it will happen with system like Github Copilot. Some programmer isn’t paying attention, doesn’t check the code fragment in which – can also happen GPT – the wrong variable is in it and the live system executes the equivalent of a fat finger..
With the release of ChatGPT, a wide audience has become aware of what AI can do today.
I can remember when we started using the early versions of Google for the first time in 1997/8, we knew: That was it for AltaVista & Co.
The advances in Large Language Models (LLMs) of which GPT is one are the same: a moment when the future will be different from the past.
2022 was truly a turning point year.
PS: We as citizens should regulate AI quickly and comprehensively as I suggested for some time now. I compare it to an airplane: it’s only safe because it’s strictly regulated. We should apply the same logic to AI.
Inflation is a nuisance and yet an opportunity for some. However, it is more of a devilish opportunity: inflation is caused by profiteering.
It is caused when companies or individuals increase prices for their own benefit instead of reflecting changes in supply and demand. One such case might be the Coop carrot salad.
Until last autumn it cost 2.80 CHF. Since the New Year, the same salad has cost CHF 2.95. That is a price increase of 5.3% and well above the general inflation rate of 3% here in Switzerland… Coop, can you explain that above?
Welcome to a new year!
Welcome to a linear year.
2022 was truly extraordinary. Some attribute to it the character of a turning point in time. Like 9/11 (2001) or the fall of the Berlin Wall (1989), February 24th was a decisive event.
The year 2023 will be different. It’s about digesting the changes that have been initiated. It will be a linear year. That doesn’t mean that the changes won’t be substantial, but they have started in recent years. For example:
Putin’s misguided attack on Ukraine will destroy Russia itself. The country will show dissolution tendencies. Hundreds of thousands of young brutalized men pouring back into the regions with no prospects for the future will become the straw that will break the camel’s back. We will see a year of government change. And it won’t be easy (Nukes anyone…).
Ukraine will win. And thus continue the process of tectonic (positive) change in Europe that was initiated last year.
In America, TFG’s Fizzle Out will continue. He will be charged several times and maybe convicted and in the end nobody from his base will care anymore. Republicans will continue on their chosen path of denial of reality. Lying, deception and demagogy as trademarks. The elected MP Santos, whose entire CV is a fabrication, is a symbol of this.
It was once said that the quickest way from a billionaire to a millionaire was to buy an airline. Musk will show us that this can also be done with tech. Tech in general will continue down the path of levelling. Mostly self-inflicted – just hubris – partly also due to legislation (especially here in Europe).
Overall a good development: all the autocrats and autocrat-lovers (Yes, Roger, I mean you…), right-wing extremists and conspiracy theorists are taught a lesson. TFG gone, Bolsonaro voted out, Marine defeated, Brexit a catastrophe and now Russia and China severely cut back. These folks will not learn it and will continue to make noise, but for everyone else it will be apparent how hollow their arguments have always been. Democracy may be arduous and associated with trials and tribulations, but Crowd beats bossiness everywhere, maybe not on the first try but certainly on the second.
Speaking of the second attempt: China will experience an unexpected catastrophe in the next few weeks. The exit from the Zero-Covid policy is cruel for the people and a threat for the world for the second time in the short term (Covid mutations) but good in the long term because it ties the Chinese regime back.
As always, Europe will be scolded for many things this year and yet gain strength. There is even a chance that Switzerland and the EU will normalize their relationship.
The economy will go through rough times and still move forward, the turn towards a greener future continues to gain momentum. Inflation will come down, but not go away completely, and while energy prices have come down significantly, freight rates are a fraction of what they were a few months ago. Quite simply: there are oligopolies or oligopoly-like situations in more and more industries. In these industries, hard mark-ups are pushed through under the guise of inflation (while in many countries small incomes are denied inflation compensation).
In Great Britain, the Brexit disaster continues and many Brexiteers still do not want to admit it. Instead they will try to hold the Remoaners accountable – you have to fix Brexit now. Linear continuation of the nonsense of the last few years.
in our village we are (finally) consistently pursuing the path of sustainability. And we continue to develop our village cleverly in other ways too.
We in our company also experienced this “watershed” moment in 2022. We will now reap the fruits of this in this and the coming years.
A linear year….
Trust and your Time
These are the 2 most valuable things a leader can give.
In your professional life, you’ll find that the longer a relationship lasts, the more you realise that the gifts or treats – things that can be purchased – are meaningless. What the people in your life want is meaningful time with you, even if it’s spent doing nothing but being in each other’s company.
Trust is also massively important – and it goes both ways.
Almost by design, as a leader, the people in your life trust you to guide them, keep them safe and be well provided for. Trust is the cornerstone of any and all relationships.
People often ask me why we, the Squirro team, have stuck together for so long, especially in this fast-paced startup world.
We’re simply fond of each other, so we give each other our time and our trust.
Trust and your Time