THE BLOG

Antony Slumbers 5/3/24 Antony Slumbers 5/3/24

10 ‘must do’s’ to thrive in the age of artificial intelligence.

In order to leverage the capabilities of AI, and to develop a competitive edge, whether as an individual a business or a society, we need to do the following. Leaning in to these basic principles WILL stand you in good stead. Do they cover everything? No. But these are high level ‘learnings‘ we need in place to act as foundations for everything else.

As an Individual:

1. Embrace lifelong learning: To stay relevant in the age of AI, continually update your skills, knowledge, and understanding of emerging technologies.

2. Develop uniquely human skills: Focus on cultivating creativity, empathy, curiosity, and emotional intelligence, as these are harder for AI to replicate.

3. Be open to collaboration with AI: View AI as a tool to augment your abilities rather than a threat to your job or way of life.

4. Maintain a balanced perspective on AI: Recognise both the potential benefits and risks associated with AI technology.

5. Adapt to new ways of working: Be prepared to work alongside AI systems and adapt to new roles and responsibilities that emerge as a result of AI integration.

6. Advocate for responsible AI development: Support initiatives that prioritise transparency, accountability, and ethical considerations in AI development.

7. Stay informed about AI advancements: Actively seek out information about the latest developments in AI to make informed decisions and participate in discussions about its impact on society.

8. Embrace interdisciplinary thinking: Draw insights from various fields to better understand and navigate the complexities of the AI-driven world.

9. Cultivate a growth mindset: Embrace change, be willing to experiment, and view challenges as opportunities for personal and professional growth in the age of AI.

10. Focus on solving problems that matter: Use AI as a tool to address pressing challenges in your personal life and community, leveraging its potential to create positive change.

As a Business:

1. Invest in AI adoption: Recognise the potential of AI to drive innovation, efficiency, and competitive advantage, and allocate resources accordingly.

2. Develop an AI strategy: Create a comprehensive plan that aligns AI initiatives with overall business objectives and considers the ethical and social implications of AI adoption.

3. Foster a culture of innovation: Encourage experimentation, risk-taking, and continuous learning to stay ahead of the curve in the rapidly evolving AI landscape.

4. Prioritise data management: Invest in robust data collection, storage, and analysis infrastructure to support effective AI implementation.

5. Emphasise human-AI collaboration: Focus on developing AI systems that augment and complement human capabilities rather than replacing them entirely.

6. Reskill and upskill the workforce: Provide training and education opportunities to help employees adapt to new roles and responsibilities in the age of AI.

7. Ensure transparency and accountability: Develop clear guidelines and mechanisms for ensuring the transparency, fairness, and accountability of AI systems.

8. Collaborate with diverse stakeholders: Engage with experts from various fields, policymakers, and the public to address the complex challenges and opportunities presented by AI.

9. Anticipate and mitigate risks: Proactively identify and address potential risks associated with AI adoption, such as job displacement, privacy concerns, and algorithmic bias.

10. Embrace agility and adaptability: Foster an organisational culture that can quickly respond to the rapid pace of change in the AI-driven business landscape.

As a Society:

1. Harness AI for economic growth: Encourage the adoption and integration of AI technologies across industries to boost productivity, efficiency, and innovation, ultimately driving economic growth and prosperity.

2. Invest in AI research and development: Allocate resources to support AI research and development initiatives, fostering a thriving ecosystem of innovation that can generate new opportunities and solutions.

3. Develop AI talent and skills: Invest in education and training programs to develop a highly skilled AI workforce, ensuring that society can fully capitalise on the economic and social benefits of AI.

4. Leverage AI for societal challenges: Prioritise the development and deployment of AI solutions that address pressing societal challenges, such as healthcare, education, climate change, and social inequality.

5. Foster AI entrepreneurship: Encourage and support AI-driven entrepreneurship and startups, creating an environment that nurtures innovation and allows for the rapid scaling of successful AI ventures.

6. Promote public-private partnerships: Facilitate collaborations between government, industry, and academia to accelerate AI development and deployment, ensuring that the benefits of AI are widely distributed across society.

7. Adopt AI in the public sector: Embrace AI technologies in the public sector to improve the efficiency and effectiveness of government services, ultimately benefiting citizens and society as a whole.

8. Establish AI-friendly regulations: Develop a regulatory framework that strikes a balance between promoting AI innovation and ensuring public safety, trust, and ethical considerations.

9. Encourage international collaboration: Foster global cooperation and knowledge sharing in AI development and governance, enabling societies worldwide to collectively harness the benefits of AI while mitigating potential risks.

10. Ensure inclusive AI-driven growth: Implement policies and initiatives that promote equitable access to AI technologies and the benefits they generate, ensuring that no segment of society is left behind in the AI-driven economy.

How many of these can you tick off?

Antony Slumbers 4/14/24 Antony Slumbers 4/14/24

Three Ways to Incorporate Your Own Data When Working with Generative AI

A quick guide for business people.

Generative AI, via the likes of Open AI's GPT-4, Google's Gemini and Anthropic's Claude, is revolutionising the way businesses interact with and utilise artificial intelligence. These models can generate human-like text, assist with various tasks, and provide intelligent responses to user queries. However, to truly harness the power of generative AI for your business, it will often be the case that you wish to incorporate your own data into the process. In this article, we'll explore three methods to integrate your company's knowledge with generative AI and discuss the pros and cons of each approach.

Method 1: Uploading Documents within the Context of a Question

The simplest way to incorporate your own data when working with generative AI is to upload relevant documents within the context of a question. This approach involves providing the AI with specific excerpts or documents that contain the information needed to answer a given query. By doing so, you can ensure that the AI has access to the most relevant and up-to-date information from your company's knowledge base.

Pros:

Easy to implement and doesn't require any additional technical setup
Allows for targeted information sharing based on the specific question
Provides a straightforward way to control the information the AI uses to generate responses

Cons:

Limited by the size of the AI's context window, which may not accommodate larger documents or extensive knowledge bases
Requires manual effort to select and upload relevant documents for each question
The AI may still generate hallucinations if the provided context is incomplete or if it makes assumptions beyond the given information

Method 2: Connecting a ChatGPT Model to an Internal Directory of Documents

Another approach is to connect a model to an internal directory of documents. This method involves setting up a system where the AI can access and retrieve information from a centralised repository of your company's documents, such as reports, presentations, and knowledge base articles. One might do this via creating a 'GPT' via ChatGPT, or by interacting with a models API.

Pros:

Enables the AI to access a larger volume of information compared to uploading documents within the context of a question
Allows for a more automated and scalable way of incorporating your company's knowledge into the AI's responses
Provides a centralised and organised way to manage and update your company's knowledge base

Cons:

Requires technical setup and maintenance to connect the AI model to the document directory and ensure smooth information retrieval
The relevance of the retrieved information may be limited by traditional keyword-based search techniques
The AI may generate hallucinations if the retrieved documents are not entirely relevant or if there are gaps in the information

Method 3: Using a Vector Database and Retrieval-Augmented Generation (RAG) with Claude or A N Other model.

The most advanced and effective way to incorporate your own data when working with generative AI is to use a vector database and Retrieval-Augmented Generation (RAG). This approach involves converting your company's knowledge into high-dimensional vectors and storing them in a vector database. The RAG technique is then used to retrieve the most relevant information from the vector database and integrate it seamlessly with the AI's generation process.

Pros:

Offers the most accurate and relevant information retrieval based on semantic similarity
Enables a tight integration between the retrieved information and the AI's generation process, minimising the risk of hallucinations
Provides the highest level of customisation and fine-tuning to suit your specific business needs

Cons:

Requires significant technical expertise and resources to implement and maintain
Involves additional costs for setting up and running a vector database
Requires careful data preparation, preprocessing, and vectorisation to ensure optimal performance

Choosing the Right Approach

The best approach for incorporating your own data when working with generative AI depends on your business requirements, available resources, and the desired level of customisation and accuracy. If you prioritise ease of implementation and have a limited amount of data to incorporate, uploading documents within the context of a question may be sufficient. If you have a larger knowledge base and want a more automated solution, connecting a ChatGPT model to an internal directory of documents could be a good choice.

However, if your primary goal is to minimise hallucinations, maximise the relevance of the AI's responses, and have the highest level of customisation, using a vector database and RAG with one of the foundational models (GPT, Claude et al) is the most suitable option. This approach ensures that the AI is always grounded in your company's factual information and provides the most accurate and reliable responses.

Regardless of the approach you choose, it's essential to regularly review and update your company's knowledge base, monitor the AI's performance, and have human oversight to identify and correct any potential inaccuracies or hallucinations.

By incorporating your own data into generative AI, you can unlock the full potential of this technology and create AI-powered solutions that are tailored to your business needs.

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A Note on Hallucinations:

When it comes to minimising hallucinations and ensuring accurate information (such as with a customer service chatbot), the choice of approach can have a significant impact.

A) Uploading documents within the context of a question:

Hallucination risk: Moderate to High

Explanation: While providing relevant documents within the context can help ground the responses in factual information, there is still a risk of hallucinations if the model makes assumptions or extrapolates beyond the provided context. The limited size of the context window may also lead to incomplete information, increasing the chances of hallucinations.

B) Connecting a ChatGPT model to an internal directory of documents:

Hallucination risk: Moderate

Explanation: By connecting the model to a directory of documents, you can provide a larger volume of information for the model to draw from. This can help reduce hallucinations compared to the first approach. However, the effectiveness of this approach depends on the quality and relevance of the documents retrieved by the keyword-based search. If the retrieved documents are not entirely relevant or if there are gaps in the information, the model may still generate hallucinations.

C) Using a vector database and RAG with Claude:

Hallucination risk: Low

Explanation: The combination of a vector database and RAG offers the best chance of minimising hallucinations. The vector database enables more accurate and relevant information retrieval based on semantic similarity, reducing the chances of missing important context. The RAG technique ensures that the retrieved information is tightly integrated with the generation process, keeping the model grounded in the factual information stored in the vector database. This approach allows for more control over the information the model uses to generate responses, minimising the risk of hallucinations.

Antony Slumbers 4/14/24 Antony Slumbers 4/14/24

Why Real Estate Needs a New Mindset to Succeed with Generative AI

STOP!!!

I need you to take a deep breath and work through this step by step.

Those of you who, like me, spend too long talking to large language models, will recognise this counterintuitive phrase.

An instruction to an inanimate object to do something we know it cannot, but that weirdly, does produce superior outcomes.

What is going on? What delusion is being played out here? It’s a machine yet telling it to take a deep breath makes it work better.

What’s going on is that you ARE dealing with software, but not software that behaves as you have come to expect.

And that’s why, most likely, you are making mistakes in your plans to incorporate it into your business.

Historically software has been very deterministic. It does A, then B, then C. And it does the same thing over and over again. So we can easily decide what we want it to do and then expect it to do just that.

But large language models DO NOT behave like this. You don’t know if they will do A, then B, then C and you certainly do not know if they will do the same thing twice.

You also don’t know if they are going to tell you the truth. Ask the wrong sort of question, in the wrong sort of way, and they’ll give you an answer, a very plausible one. But it will have little to do with the truth.

Conversely, they will also make things up when you WANT them to - ‘give me 10 ideas about XYZ’.

So is that 'hallucination' bug actually a feature?

Can you have creativity without imagining things?

Either way, unless you have deeply internalised the actualité of what you are dealing with, you are going to come a cropper.

And you cannot deeply internalise the actualité of large language models unless you have used them extensively yourself, in a myriad of ways.

Which brings us back to STOP!!!

I am hearing and reading many accounts of real estate companies saying they are building this or that application, but doing so in a manner that suggests only a tendentious familiarity with the above. Not least of all a general lack of awareness as to what is generative and what is not generative AI (Generative AI is a creative tool, whereas Predictive AI is an analytical tool. That's quite some difference)

Which is of course understandable. Everyone is forever under pressure to deliver. And when we’re talking ‘flavour of the month’ the pressure can be intense.

But the outcomes will not be good. We need to approach generative AI from a different direction.

And that involves two things; practice and patience.

Practice in that you need to have as many people as possible within your company trying to incorporate using a language model in everything they do. You need to see what emerges from this. What works, what does not, what is surprising, what is disappointing. Where ‘might’ these things REALLY be useful? What should we get them to do, what should we do together, and what should we keep for ourselves?Where will they allow us to do something faster, better, cheaper? And much more importantly, where might they enable us to do something we could not do before? And what might that mean?

And patience in that Claude 3 has just been released but soon (in weeks maybe months, but not a year) we will have ChatGPT 5 and Gemini 2. And we’ve already been told, by Sam Altman himself, not to try and make certain hard and complicated things work today that the next version, coming soon, will happen right out of the box.

Bloomberg spend a fortune on BloombergGPT, their own customised version of GPT 3.5, only to find that today’s GPT 4 Turbo beats it for $20 a month.

So practice and be patient.

This is the fastest developing technology in history but it is more like an alien being than anything else we are familiar with. There is no user guide because the developers do not know what it can, or cannot, do. It is an emerging technology.

Ultimately, use of these tools will be embedded in everything your company does, and everyone in your company will use them in everything they do. But for now, the best advice is to use them, learn about them and see what comes from that. Only then should you be trying to productise them.

So ….. take a deep breath and let’s work through this step by step.

Antony Slumbers 4/9/24 Antony Slumbers 4/9/24

Why Real Estate Needs a New Mindset to Succeed with Generative AI