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As businesses become ever more reliant on digital twins fed on their most sensitive information, they also leave themselves more vulnerable to being hacked. A well-targeted attack could, in theory, not only grant rogue actors access to a company’s deepest secrets, but also allow such data to be secretly manipulated—with real-world consequences. This is magic to be handled carefully.

Digital twins are making companies more efficient

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"The most succinct argument for AI risk, in my opinion, is the “second species” argument. Basically, it goes like this.

Premise 1: AGIs would be like a second advanced species on earth, more powerful than humans.

Conclusion: That’s scary."

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three things follow if a developer makes a model’s weights widely available:

A third party can customise outside the developer’s initial scope. Customisation techniques typically require significantly less technical knowledge, resources, and computing power than training a new model from scratch.

The developer gives up control over and visibility into its end users’ actions.

Users can perform computational inference (the process of running live data) using their computational resources, which may be on a local machine or bought from a cloud service. This localisability allows users to leverage models without sharing data with the model’s developers, which can be important for confidentiality and data protection (i.e. healthcare and finance industry)."

https://www.lexology.com/library/detail.aspx?g=2e7fad9d-d9c7-4fca-83e7-f40d27134927#:~:text=three%20things%20follow,and%20finance%20industry).

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"many AI tools today absolutely are not “ready for prime time,” in the sense that you could trust them with your job or your life. AI needs close supervision, sometimes requiring more effort than it would take to simply have done the task yourself. 

At the same time, talk to most software engineers today and they’ll tell you they already can’t imagine doing their jobs without the coding assistance that AI provides. Are they the exception to the rule — or just early in realizing the kind of productivity gains that will come to the rest of the economy over time?"

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Safeguarded AI’s goal is to build AI systems that can offer quantitative guarantees, such as a risk score, about their effect on the real world ... The project aims to build AI safety mechanisms by combining scientific world models, which are essentially simulations of the world, with mathematical proofs. These proofs would include explanations of the AI’s work, and humans would be tasked with verifying whether the AI model’s safety checks are correct. 

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"The primary design goal of most popular AI employment tools in China centres around boosting productivity and reducing costs, which is realized through four processes.

Workflow automation: This refers to a process of automating repetitive and manual tasks. Common functions include automatic information filling, data extraction from images, videos, audio and documents, as well as task assignment powered by algorithms.

Datafication: This process involves transforming various aspects of work activities and worker behaviour into quantifiable data for further analysis. It allows a thorough evaluation of detailed work and enhances the visibility and traceability of work processes.

Big data analytics: AI-backed systems distinguish themselves from traditional digital technologies by generating human-like insights rather than just collecting raw data. They can score workers, flag potential issues, identify trends, and make predictions and recommendations.

Platformization: Data are often collected and shared across various devices, networks and platforms associated with workers, facilitating a more comprehensive – and intrusive – profiling of employees."

https://www.chathamhouse.org/2024/07/workplace-ai-china/03-hiring-firing#:~:text=The%20primary%20design,g%20of%C2%A0employees.

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"LLMs are great. They aren't as knowledgeable as the best person in the world, but there are thousands, if not millions, of people who know the answers to any electronics question I might have. And so that means the language models probably have the answer too. And it's happy to spoon-feed me the answers to all my questions so I can have the fun I want without fighting with the details. And even though I probably could have put in just a little bit more work and got the answer by searching the internet, the sheer convenience of just asking the model to do it for me after having spent a day working on complex research code is just so relaxing."

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Lloyds hires Amazon Web Services executive as its new AI chief

Lloyds on Monday said it had hired Rohit Dhawan as its first group director of AI and advanced analytics...

The bank said Dhawan would be tasked with supervising the integration of AI into customer and operational processes as well as creating a new data and AI function within the bank. He would also oversee an “AI Centre of Excellence” comprised of experts in data science, behavioural science, machine learning engineering and AI ethics...

Lloyds said it had recruited 1,500 technology and data specialists this year and that it was trialling 50 AI use cases to help deliver quicker customer support, improve its chatbots and detect early warning signs of fraud.

The group said it uses machine learning algorithms to help triage and prioritise customers’ calls and for income verification when customers take out mortgages, which it said reduced the process from three weeks to a few seconds.

Lloyds, which also has an insurance and pensions arm, also used AI to register insurance claims following storms in January, which it said freed up time for urgent phone calls from customers.

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"Often, when we think about beneficial impact, we focus on abstract pillars like democracy, education, fairness, or the economy. However important, none of these are valuable intrinsically. We care about them because of how they affect our collective lived experience, over the short and long-term.... Human benefit ultimately must ground out in the lived experience of humans. We want to live happy, meaningful, healthy, full lives — and it’s not so difficult to imagine ways AI might assist in that aim. For example, the development of low-cost but proficient AI coaches, intelligent journals that help us to self-reflect, or apps that help us to find friends, romantic partners, or to connect with loved ones. "

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"it’s hard to internalize how quickly models have improved, and how capable they might become given several more years. Recall that GPT-2 — barely functional by today’s standards — was released only in 2019. If future models are much more capable than today’s, and competently engage with more of the world with greater autonomy, we can expect their entanglement with our lives and society to rachet skywards."

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As AI is commercialised and deployed in a range of fields, there is a growing need for reliable and specific benchmarks. Startups that specialise in providing ai benchmarks are starting to appear... to give researchers, regulators and academics the tools they need to assess the capabilities of AI models, good and bad. The days of ai labs marking their own homework could soon be over.

GPT, Claude, Llama? How to tell which AI model is best

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foundation model value chain, from page 9 of CMA's technical report

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Hundreds of millions of people have tried ChatGPT, but most of them haven’t been back. Every big company has done a pilot, but far fewer are in deployment. Some of this is just a matter of time. But LLMs might also be a trap: they look like products and they look magic, but they aren’t. Maybe we have to go through the slow, boring hunt for product-market fit after all

The AI summer — Benedict Evans

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...I place low odds on AI-related revenue expansion because I don't think the technology is, or will likely be, smart enough to make employees smarter. Even one of the most plausible use cases of AI, improving search functionality, is much more likely to enable employees to find information faster than enable them to find better information. And if AI’s benefits remain largely limited to efficiency improvements, that probably won’t lead to multiple expansion because cost savings just get arbitraged away. If a company can use a robot to improve efficiency, so can the company’s competitors. So, a company won’t be able to charge more or increase margins.

Jim Covello - Head of Global Equity Research at Goldman Sachs

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...The substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment (ROI). We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed

Jim Covello - Head of Global Equity Research at Goldman Sachs

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Every human invention should be celebrated, and generative AI is a true human invention. But too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high today for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and trouble-shooting capabilities that human capital provides.

Daron Acemoglu, Institute Professor at MIT