The AI & Data Digest - 5th Edition
?? The AI & Data Digest ??
Hi everyone and welcome back to the 5th edition of the AI & Data Digest.
We now find ourselves in June and at the hump point of 2024. Looking back on the first 6-months of this year, we have seen so much advancement in regards to AI and the data landscape is becoming increasingly complex as organisations seek to maximise the ways in which they can apply AI over their own data-sets to drive unbounded business value.?
New models, new regulations, new products and expanding capabilities. It’s never been a dull moment over the last 6-months. Personally, I am excited about what the next half of this year will look like and I have a feeling the final quarter of 2024 will be full throttle as organisations try to set themselves up for a wider adoption of AI as we move into 2025.?
So, let’s get straight to your essential roundup of this week's top news, insights, and opportunities in AI, data, and digital transformation.
?? Welcome to Your Weekly Intelligence Brief!
It’s been a big week for all things AI & Data. Let’s start with our 10 BIG weekly news beats you might have missed!?
?? Top 10 News Beats This Week
? ???????????? ?????????? ???????????????? ?????????????? – Key resignations at OpenAI, including co-founder Ilya Sutskever, reveal deep divisions over CEO Sam Altman’s push for rapid AI development. ??
? ?????? ???? ???????????? ?????????????????? ???? ???????????? – In response to internal disputes, OpenAI forms a safety committee, chaired by CEO Sam Altman, to enhance safety and security measures for its projects. ??
? ???? ?????? ???????????????????????????? ???????????? ???? ???? – The EU establishes an office to enforce the AI Act, ensuring safe and ethical deployment of high-risk AI applications and fostering innovation. ??
? ????????’?? ?????? ???????????? $?? ?????????????? – Elon Musk’s AI venture, xAI, secures $6 billion in funding, aiming to launch innovative AI tools and compete with established players like OpenAI. ??
? ????????????'?? ???? ?????????????? ???????????? ???????????????? ???????? ???????????????? – Google’s AI Overview feature faces criticism for generating incorrect and misleading information, highlighting the need for more rigorous testing and quality control.?
? ????????????'?? ?????? ???? ???????? ???? ?????????????????? – Anthropic introduces a new feature for Claude, allowing users to create custom AI assistants using external APIs, enhancing AI accessibility and functionality. ??
? ?????????????????? ???? ?????????? ?????? ???????????????????????????? – AI tools like Jasper and Otter.ai are proving transformative for individuals with neurodiverse conditions, enhancing productivity and inclusivity. ??
? ???? ???????? ???????????????? ?????? ???????????????????? ??????????????? – At the Bilderberg Meeting, CEOs from Google DeepMind, Microsoft AI, and Anthropic discuss AI safety and economic challenges, with a focus on the impact of AI on jobs and income. ????
? ???????? ???????????????????? ????-???????????? ?????????????????? ?????????????? ?????????????????? – Meta uncovers an AI-generated content campaign on Facebook and Instagram, emphasising the need for tech giants to manage AI-driven disinformation. ??
? ???????????????????????? ???????? ???????????? ?????????????? ???????? ?????????? – A significant data breach at Ticketmaster exposes sensitive information on 560 million users, highlighting the ongoing risks of data security breaches. ???
?? My Insights:
It was a relatively quiet week for me in terms of blogs and new material. I’ve been working on some personal projects that I will aim to share later this year. However, over the course of the week, I did share my slides from the recent Generative AI Summit where I talked through the Last 12 Months of Generative AI Adoption in the Enterprise. You can find those slides here if you are interested.
In terms of other material you might be keen to take a look at, I thought I would share some thoughts from the archives that are still super relevant today. This piece about how to build the business case for ML & AI in your organisation can be a useful starting point for businesses that are looking to execute an AI transformation. The business case component is important to nail for any AI transformation and it’s often the hurdle that many businesses fail to orient themselves around in terms of having a clear understanding as to why AI will make a difference for their business.?
Keeping to the AI trail, and specifically, the generative AI trail. I drafted this piece previously about embedding generative AI in your software delivery lifecycle. This is one key area that I believe generative AI can play a massive role in supporting businesses who don’t usually build their own custom applications. In order to get to a point where they have less dependency on third parties or commercial off the shelf software that doesn't really do the right job for their business.?
I’ve seen many teams apply generative AI in their software development lifecycles in the last year and many have often reported an uptick of at least 30% in terms of their development throughput. As well as reporting that the cohesion and performance of their code is improving. Bye bye technical debt!?
?? Guest Blog:
For this week's guest blog, I have opted to include a piece by Anthony Alcaraz, who is Chief Product & AI Officer at Fribl. A Gen-AI powered platform that connects corporates with talents to fully automate HR recruitment processes within all industries.
Anthony has been posting some amazing content recently and as such, I wanted to share his recent publication When to Build a Knowledge Graph RAG System which you can find over at Medium.?
In the piece, Anthony discusses the increasing challenge of making sense of vast and complex knowledge corpora across industries and how the integration of two approaches: Retrieval-Augmented Generation (RAG) and knowledge graphs can make the linking and identification of vast data-sets faster and easier. For those not in the know, RAG involves retrieving relevant information to inform language model outputs, while knowledge graphs capture entities and relationships in a structured format.
Anthony provides some suggestions and a framework to help organisations decide when to invest in a knowledge graph RAG system, based on the characteristics of their data and business needs. I really enjoyed the piece and have seen the value of Knowledge Graphs over the last few years. Covering everything from supply chain optimisation, third party risk management, customer 360 and operational resilience. The latter one being very much something of special interest to myself!?
I hope you enjoy the read as much as I did and thanks to Anthony for creating the great content!
?? Success Stories:
Check out these three stories that highlight how AI is being deployed across sectors to solve complex problems and open up new opportunities for efficiencies across the world:
??Klarna is using AI to revolutionise personal shopping, customer service, and employee productivity.
领英推荐
?? Upcoming AI Events in London This June:
Check out these AI events in London over the course of this month.
?? Artificial Intelligence for Cybersecurity -? 12th-13th June, Tobacco Dock, London?
?? The AI Summit -? 12th-13th June, Tobacco Dock, London?
?? The AI Summit - Afterparty Sponsored by HPE & NVIDIA -? 12th June, Tobacco Dock, London?
?? AI Hardware & Edge Summit -? 18th-19th June, ETC Venues, Aldersgate Street, St.Pauls, London
?? Moving Target: AI Risk Management Fundamentals -? 25 Copthall Avenue London EC2R 7BP
?? Research & Reports:
This week's featured report covers a piece titled “Red Teaming for Generative AI: Silver Bullet or Security Theatre”. The paper by Michael Feffer, Anusha Sinha, Wesley H. Deng, Zachary C. Lipton, and Hoda Heidari from Carnegie Mellon University critically examines the practice of AI red-teaming and tt highlights the increasing use of red-teaming to identify and mitigate risks in generative AI systems but points out significant inconsistencies in its implementation and effectiveness. The authors argue that while red-teaming is valuable, it is often vague and may verge on "security theatre" if not properly structured and complemented by other evaluation methods. They propose a detailed framework and a question bank to guide future AI red-teaming practices, emphasising the need for clearer definitions, comprehensive evaluation methods, and transparent reporting of results. The paper calls for more robust guidelines to ensure that red-teaming genuinely enhances the safety and trustworthiness of AI systems.
It is certainly worth a read as red-teaming isn’t a new construct having been used extensively during the DevOps/DevSecOps movement. However, it’s application to the world of LLM’s and generative AI certainly is valid and should be considered by organisations who are keen to avoid bias in their generative AI systems.?
?? Regulatory & Ethics Watch:
?? Monetary Authority of Singapore: Emerging Risks and Opportunities of Generative AI for Banks
Generative AI, often heralded as the next frontier in artificial intelligence, offers transformative potential for the banking sector. Project MindForge, a collaboration among Singapore's leading financial institutions and tech giants like Google and Microsoft, aims to navigate this complex landscape. Their white paper, "Emerging Risks and Opportunities of Generative AI for Banks," provides a detailed analysis of the promises and pitfalls of this technology.
Opportunities
According to the paper, Generative AI can revolutionise banking operations by enhancing customer satisfaction, boosting employee productivity, and streamlining decision-making processes. The ability to generate text, images, and other content through large language models (LLMs) opens new avenues for innovation. Imagine AI-driven chatbots providing personalised customer service or sophisticated fraud detection systems preemptively identifying threats. These advancements could significantly reduce costs and improve service efficiency.
Risks
However, the promise of generative AI is tempered by substantial risks. The white paper highlights several key concerns:
1. Bias and Fairness: AI systems can perpetuate existing biases present in training data, leading to unfair outcomes.
2. Ethical Accountability: Ensuring AI decisions align with ethical standards is challenging, especially with third-party systems.
3. Transparency: The "black box" nature of AI makes it difficult to explain decisions, eroding trust.
4. Legal and Regulatory Issues: Data privacy and intellectual property concerns are paramount, requiring stringent compliance measures.
5. Security: The expansive data requirements and potential vulnerabilities of AI systems necessitate robust cybersecurity protocols.
Technological Considerations
The document suggests a robust framework for adopting generative AI, emphasising the need for continuous monitoring and human oversight. A platform-agnostic reference architecture is proposed to ensure secure deployment, highlighting the importance of integrating guardrails to prevent misuse.
The Way Forward
Project MindForge's forward-looking approach underscores the necessity of industry use cases to understand AI's real-world impact. The consortium plans to extend its research beyond banking, aiming to develop comprehensive guidelines that other financial services sectors can adopt.
It would seem that generative AI represents a double-edged sword for banks. While its potential to drive efficiency and innovation is immense, the risks it introduces cannot be ignored. As the financial sector navigates this new era, the balance between leveraging AI's capabilities and safeguarding against its dangers will be crucial. Responsible adoption, guided by thorough risk assessments and stringent ethical standards, will determine whether generative AI becomes a boon or a bane for banking.
?? Careers & Opportunities:
Head over to Otta to take a look at these 5 fantastic data, ML & AI opportunities in the UK across mid-senior level leadership roles.?
? Job Highlight #1: Data Scientist: Iwoca ???- Apply Here.
? Job Highlight #2: Data Science Lead: Abatable ?? - Apply Here.?
? Job Highlight #3: BI Engineer: Go Cardless ?? - Apply Here
? Job Highlight #4: Senior Machine Learning Engineer: Ovo ??- Apply Here
? Job Highlight #5: Senior Product Analyst: Cleo ??- Apply Here
?? Connect With Me!
Engage with me further on these topics and more by connecting on LinkedIn and scheduling a discussion via Calendly.?
?? Like & Share
Enjoyed this newsletter? Hit like and share it within your network to help others stay on top of the latest in AI and data! Also, if you have any feedback on other bits to include, then let me know! Thanks as ever for reading!?
Thank you,?
Ben @ The AI & Data Digest Team
AI & ML Innovator | Transforming Data into Revenue | Expert in Building Scalable ML Solutions | Ex-Microsoft
5 个月The AI & Data Digest looks like a great read this week! Im excited to see updates from OpenAI, Google DeepMind, and Microsoft. Your articles on building an AI business case and applying generative AI to software delivery are especially relevant. Combining knowledge graphs and RAG is a smart approach, as highlighted by Anthony Alcaraz.It's fascinating to see how AI is being adopted at places like the NYSE, Klarna, and AT&T. The discussions on regulation and ethics are also crucial as we navigate AIs growth. Thanks for sharing thislooking forward to diving in!