MY PITCH IS GOOD by Yves Curtat. Chairman and Founder of Retail Reload. In this exclusive interview, Yves explains the performance that his RFID solution with AI brings to Retail.
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AI offers unrivalled versatility. From business process automation to financial analysis. From personalised advice to optimise business models, to writing and summarising complex texts.
Artificial Intelligence can also be used for video production and even stage design.
But where it is most in demand is in the field of software development and IT security.
The range of possibilities is immense, making it a powerful tool for carrying out a variety of tasks efficiently.
Its ability to learn, understand and communicate surpasses anything you’ve experienced before.
It doesn’t yet know your personality or the way you work. But it will if you let it. Imagine a world where AI could take your business idea and help you make it a reality.
Your state of mind is the first building block in the collaboration you will have with Artificial Intelligence.
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To work effectively with an AI in a project, usually a business project, you need to adopt an open and collaborative mindset.
A large dose of curiosity to help you discover its capabilities.
Flexibility to incorporate new approaches, confidence in its skills while recognising the added value of AI.
You will nevertheless remain responsible for supervising the results and working closely with it.
Adapt quickly and agilely to the information provided by AI.
By combining these attitudes, you will be able to exploit the full potential of AI to improve efficiency and achieve the first milestones in the project.
The last essential point is to remain attentive to the responsible and beneficial use of this emerging technology.
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It is vital to define a clear objective for any project.
This is particularly true when it comes to artificial intelligence (AI).
For example, we might insist that AI algorithms are aligned with overall business objectives. From this, the definition of a business model can be glimpsed and challenged with AI.
Rather than focusing on the technical capabilities of AI, it is advisable to start by determining what the company is trying to achieve.
Numerous examples are given to illustrate how AI can be used in different industries. They can be used to stimulate creativity, automate processes or improve customer experience.
Generative AI, in the creation of new opportunities, provides professionals with a tool to realise their aspirations.
Think about your own business goals and reconsider them throughout the learning process. Always bear in mind their relevance to the lessons and learnings presented.
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Recent advances in artificial intelligence (AI) are challenging the competitive advantage traditionally associated with proprietary data.
Recent generative AI models are now trained on the world’s public knowledge.
They are thus transforming this data into a commodity accessible to all.
Furthermore, these advances allow AI models to create synthetic data or even learn without the input of prior data. This is invaluable in situations where access to training data is limited.
The democratisation of AI lowers the barriers to entry for companies without large data sets.
However, for companies that rely heavily on proprietary data, it is essential to rethink their strengths. They need to broaden their skills beyond just data capabilities.
While proprietary data remains important, it is no longer sufficient to guarantee competitive advantage.
AI models, can be enhanced through fine-tuning. This can leverage task-specific data.
So, to thrive in this new AI landscape, companies need to adopt a more ‘holistic’ and diversified approach, combining proprietary data (1st Party data) and extended capabilities beyond simple data mining.
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The launch of Chat GPT (Generative Pretrained Transformer) represents a major advance in the field of artificial intelligence.
These general-purpose AI models have a broader objective function and are already trained on all public knowledge.
They are designed to perform well in many tasks, acting as generalists.
Models can be specialised in a specific domain by adapting them to unique examples. This is known as fine-tuning.
Fine-tuning means adjusting the weights of the connections between neurons so that they are pre-trained for the new task without significantly disturbing pre-existing knowledge.
This approach has a wide range of applications in many fields
During this fine-tuning process, the model adjusts its parameters to better match the specific data while continuing to learn from its past knowledge.
This allows the model to become a specialist in the desired domain while retaining its ability to generate in a general way.
This process can be compared to training athletes for specific disciplines.
Not specialising an application risks making it similar to those of others, forcing you to use the same capabilities as everyone else.
Although fine-tuning can maintain a competitive edge, it is expensive and difficult to maintain.
As we have seen, AI relies on data models and algorithms that determine how it learns and applies knowledge to perform intellectual tasks.
Generative models, for example, create new content using information acquired from various forms of data.
Recent advances have given rise to a new fundamental model, which serves as the basis for many future AI systems.
This development is part of a historical progression in AI systems. It starts with rule-based algorithms, then moves on to search and optimisation algorithms.
Finally, it leads to machine learning techniques such as supervised, unsupervised and reinforcement learning.
These techniques have paved the way for massive advances in computer vision and natural language processing.
Deep learning algorithms then emerged, with models such as GPT, based on the architecture of ‘Transformers’, particularly effective in generating and understanding natural language.
Transformers use a technique called ‘self-attention’ to better understand input data and improve accuracy.
Self-attention is used for many language processing tasks.
Diffusion models are also promising, particularly for generating realistic images.
while reinforcement learning based on human feedback opens up new prospects for improving model performance.
Prompt engineering is another key tool for refining AI models, offering new possibilities for interaction and learning. We will discuss this in a future article.
At GOWeeZ, we support companies in their innovation-related strategic development.
Would you like us to help you with a strategic innovation project, the development of AI-based architecture or fund-raising?
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The bridge between your Data-Digital Transformation and AI
Artificial intelligence is very much in demand in the fields of software development and IT security, and the possibilities are immense as it becomes a powerful tool for carrying out a variety of tasks efficiently. Its ability to learn, understand and communicate is obvious. We'd like to give you a few tips on the mindset and ability to set goals with AI as part of a professional project.
