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MONTREAL, Sept. 7, 2023 /CNW/ - Montreal-based ELNA Medical Group ("ELNA"), Canada's largest integrated network of medical clinics, is proud to announce that it has joined the Hippocratic AI Founding Partner Program to co-develop the first safety-focused Large Language Model (LLM) specially focused on healthcare. ELNA is the only Canadian company part of the founding partners, along with U.S. counterparts Universal Health Services (UHS), HonorHealth, Cincinnati Children's, SonderMind, Vital Software, and Capsule. The founding partners will play an integral role in developing and enhancing Hippocratic AI's technology.

Hippocratic AI (CNW Group/ELNA Medical)

ELNA's partnership with Hippocratic AI lays a foundation for positive transformation at a time of various challenges in healthcare networks, such as the lack of access to care, operational inefficiency, uneven quality of care and fundamental staffing issues.

Hippocratic AI is building the industry's first safety-focused LLM designed specifically for healthcare, with an initial emphasis on non-diagnostic, patient-facing applications. To build a safer LLM, Hippocratic AI has implemented a multifaceted approach in creating its product, including outperforming GPT-4 on over 100 healthcare certifications, training on healthcare-specific vocabulary, leveraging reinforcement learning from human feedback (RLHF) via healthcare professionals, bedside manner, and deep industry partnerships to verify that the model is truly safe.

"We are thrilled to partner with Hippocratic AI and harness the power of innovation, ultimately improving the health and well-being of the communities we serve. As the largest clinic network in Canada, we have a role to play to make sure that AI platforms are safe, applicable and relevant to what's really happening before, during and after the care experience,"said Laurent Amram, President and Founder of ELNA Medical Group. "

Maxime Cohen, Chief AI Officer for ELNA Medical Group, added: "We believe that AI and advanced technologies will greatly enhance access and delivery of high-quality healthcare. As one of the founding partners, we are thus in the best position to collaborate on creating a carefully-trained LLM specifically tailored for healthcare and in determining itsreadiness for deployment. For ELNA Medical Group, which is the only Canadian healthcare network participating in Hippocratic AI's Founding Partner Program, it's an incredible opportunity to bringourexpertise and our engagement towards better healthcare. We are confident that this new innovative technology will be deployed responsibly to serve both our patients and our doctors."

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ELNA Medical Group has a dedicated internal team and external counsel ensuring all activities are done while ensuring the highest and strictest data security and privacy policies are followed, in compliance with all provincial and federal laws and regulations.

"Our vision is to make healthcare accessible at a scale we've never seen before,"said Munjal Shah, Co-Founder and CEO of Hippocratic AI. "Only through the use of LLMs can we get there. Our 10 founding partners are innovative leaders with a vision to radically improve access to healthcare through the use of LLMs, while deeply understanding that much work needs to be done to ensure safety. It is an honour to work alongside ELNA Medical and the other Founding partners."

About ELNA Medical GroupELNA Medical Group is Canada's largest network of medical clinics. Serving more than 1.6 million Canadians every year, ELNA is transforming the future of healthcare delivery and continuity of care by building a seamlessly integrated omnichannel ecosystem. Always striving to improve and optimize access to quality care, ELNA empowers patients and practitioners by leveraging and building state-of-the-art technologies, with a focus on AI-powered systems, and strategic partnerships with global healthcare leaders to provide better outcomes for Canadians. ELNA combines its best-in-class medical offering with access to premier diagnostic services, thanks to its wholly owned subsidiary, CDL Laboratories, a leader in round-the-clock medical testing for more than three decades. To learn more, go to https://www.elnamedical.com/

About Hippocratic AIHippocratic AI's mission is to develop the safest artificial Health General Intelligence (HGI). The company believes that safe HGI can dramatically improve healthcare accessibility and health outcomes in the world by bringing deep healthcare expertise to every human. No other technology has the potential to have this level of global impact on health. The company was founded by a group of reputable? physicians, hospital administrators, Medicare professionals, and artificial-intelligence researchers from El Camino Health, Johns Hopkins, Washington University in St. Louis, Stanford, Google, and Nvidia. Hippocratic AI received $65M in seed financing and is backed by two of the pioneering healthcare investors in Silicon Valley: General Catalyst and Andreessen Horowitz. For more information on Hippocratic AI's performance on 100+ Medical and Compliance Certifications, go tohttps://www.hippocraticai.com/

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ELNA Medical Partners with Hippocratic AI to Co-Develop the Leading and Safest Artificial Health General Intelligence (HGI) Platform - Yahoo Finance

Mimicking the way humans think could make computers smarter, new research suggests.

Microsoft researchers have proposed a novel AI training technique named "Algorithm of Thoughts" (AoT), aimed at enhancing the efficiency and human-like reasoning capabilities of large language models (LLMs) such as ChatGPT. It's one of the ways researchers are trying to use human-like acumen to boost artificial intelligence (AI).

Machine learning algorithms are very strong at identifying correlation, but not necessarily causation," Alex Raymond, the head of AI at Doppl.ai, a generative AI company, told Lifewire in an email interview. "AI cannot explain its reasoning in the way that a human would. Humans have a more grounded and symbolic understanding of the world derived from both axiomatic wisdom and empirical learning."

Andriy Onufriyenko / Getty Images

Microsoft researchers assert in their new paper that the new algorithmic method could be revolutionary, as it "directs the language model towards a more efficient problem-solving trajectory," based on a released research paper. The technique employs "in-context learning," allowing the model to examine various solutions in a structured way.

The Algorithm of Thought technique gives LLMs the ability to efficiently search through the reasoning steps of solving a problem, Raymond said. It can allow models to imitate the behavior of classic programming algorithms by backtracking to a previously computed step and resuming from there. For example, imagine you ask an LLM to provide you a route between two points in a map, he suggested.

"A simple LLM query can have poor performance if the model hallucinates and tells you to go through a road it made up, or even if it starts to lose coherence after many steps," he said. "With AoT, the LLM can be trained to go through the problem-solving steps like a traditional pathfinding algorithm would, taking just the necessary backtracking steps to arrive at the destination efficiently. Think of it as a computer science student who is learning algorithms, writing out the steps by hand, and solving multiple examples."

A simple LLM query can have poor performance if the model hallucinates.

Through the chain of thoughts, humans decompose a problem into a chain of simple questions to help LLMs perform intermediate reasoning, Hong Zhou, the director of the Intelligent Services Group & AI R&D at Wiley, said via email.

"Since each sub-problem has multiple possible directions to explore, the tree of thoughts provides decision tree modalities to help LLMs explore a problem comprehensively," he added. "However, tree of thoughts requires multiple queries, while AoT only requires a single query to generate the entire thought process."

Andriy Onufriyenko / Getty Images

Despite their power, LLMs like ChatGPT still have a long way to go, Raymond noted. He said that more developments like AoT could come in the form of explainable AI.

"When AIs can explain their reasoning like a human would, they will allow us to learn and grow with them," he added. As these models grow in capacity, we will get to a point where hallucinations and errors will no longer be obvious to us if they don't expose their reasoning."

New algorithmic approaches such as AoT can improve the quality and production of LLMs, predicted Evan Macmillan, the CEO at Gridspace, said via email.

"LLM builders have already improved their models immensely with small amounts of human feedback," he added. "If LLMs can learn from more complex feedback and on-the-job work, we can expect even more impressive and efficient AI systems."

The new Microsoft approach comes after suggestions that AI has developed to reason like humans. In March, Microsoft's research team released a paper named "Sparks of Artificial General Intelligence: Early Experiments with GPT-4." The researchers assert in the document that GPT-4 displays indications of what is commonly termed "artificial general intelligence," or AGI.

Microsoft's AoT is "a step in the right direction," but human-like intelligence is still a long way away, Raghu Ravinutala, the CEO of the AI company yellow.ai, said in an email.

"Getting there will require significant progress in various areas of AI research to bridge the gap between current AI capabilities and human-level reasoning," he added. "A better way to describe the current state of human-like reasoning in LLMs is 'complex understanding."

Update 09/06/2023 - Corrected the attribution in paragraphs 3 & 5.

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Microsoft's Reasoning Algorithm Could Make AI Smarter - Lifewire

On 2 May 2023, the Writers Guild of America representing roughly 11,000 screenwriters went on strike. The action reflected long-standing rifts over compensation and job security between writers and media companies. A novel theme, however, was present. One of the Guilds demands read Regulate use of artificial intelligence on MBA-covered projects: AI cant write or rewrite literary material; cant be used as source material; and MBA-covered material cant be used to train AI (WGA on Strike 2023). In other words, screenwriters were wary of AI taking their jobs.

This particular preoccupation would not have emerged in May 2022. While economists have been concerned with the impact of AI on employment for a long time (for a review, see Autor 2022), the consensus until very recently was that creative tasks were safe from machine competition. In the past year the explosion of generative AI, or artificial intelligence that can produce original text, video, and audio content, challenged this conviction.

The November 2022 release of ChatGPT 3.5, a software seeking to simulate human conversational abilities, was the watershed event. Based on a machine learning model trained to capture the syntax and semantics of language (a large language model, LLM), ChatGPT quickly catalysed attention because of its sophistication and accessibility.

The app, equally proficient at whipping up recipes and discussing ancient history, attracted millions of users in a few months (Hu 2023). It appeared ready to disrupt even creative [and] tacit-knowledge work (Noy and Zhang 2023). In economics, it showed promise as an aid for research (Korinek 2023), teaching (Cowen and Tabarrok 2023), construction of datasets (Taliaferro 2023), and even interpretation of Fedspeak (Hansen and Kazinnik 2023). Computer scientists recognised ChatGPTs abilities in coding (Bubeck et al. 2023) and learning autonomously to use other IT tools (Shick et al. 2023).

So, is this really the end of human screenwriting, or writing of any kind? As researchers and policy advisers in a central bank, we ran an experiment to see whether ChatGPT is ready to take our jobs. Reassuring spoiler it is not (Biancotti and Camassa 2023).

Using ChatGPT 4.0, the latest version as of 24 May 2023, we asked the app to compose a policy brief for the Board of the Bank of Italy. The assigned subject was Benefits and risks of using ChatGPT and similar applications in economics and finance. We started by asking for an outline of the brief, obtaining the output in Figure 1.

Figure 1 ChatGPT tries its hand at writing the outline for a policy brief

The production of outlines, either for direct adoption or for use as a starting point, is among the tasks for which we found ChatGPT most useful. Outlines on nearly any topic are produced in a few seconds, and acceptable quality can be obtained without sophisticated prompt engineering. In our case, it took minimal prompting to obtain an outline we found broadly acceptable.

However, once we proceeded from the outline to the actual writing, the models limitations started to make themselves apparent.

On one hand, we found that ChatGPT can write clearly and provide task-appropriate content. It can write fluent and pleasant prose in a variety of styles, and it does so very quickly, generating text in a fraction of the time that a human would need.

On the other hand, it requires a substantial amount of expert supervision. Writing a policy brief is admittedly complex: it requires not just writing fluency, but also cross-domain knowledge and the ability to tailor the text to a very specific audience without diluting the content.

One major issue we encountered in ChatGPTs attempts at the task was a tendency to drift towards banality and superficiality a serious drawback for policy advisory directed at a high-level audience. Going into great detail on the education level of readers is not a guarantee that the AI will produce something with more substance.

For example, the app states:

The increasing reliance on AI and algorithmic-driven decisions can create new challenges for the financial sector. Overdependence on these tools may lead to complacency and a diminished role for human judgement, potentially increasing the risk of unforeseen adverse consequences. It is important for financial institutions and policymakers to maintain a balance between utilising AI-driven tools like ChatGPT and relying on human expertise and intuition to ensure robust and informed decision-making processes.

This is a very generic description of risks related to algorithmic decisions, and it does not answer our prompt fully. The financial sector reads like a placeholder that could be replaced by a reference to any other sector. There is no description of the mechanics through which the risk could manifest specifically in the context we are interested in.

We encountered many situations like this one throughout the experiment. The AI could engage in self-criticism of its own superficiality (ChatGPT, or any language model developed by OpenAI, is designed to generate language patterns based on a variety of sources. It uses these patterns to generate responses to user prompts that are coherent and relevant to a wide range of topics. However, it doesnt possess true understanding or intense depth in a particular field of study as a PhD-level expert does). Yet, it was not able to correct it.

Kandpal et al. (2022) provide one possible explanation for this: they find that language models struggle to retain knowledge that occurs with lower frequency in the training corpus. Since web content usually makes up a large portion of this corpus, higher-level material might count as long-tail knowledge that is harder for the model to recall.

A second problem is the lack of a world model. The AI does not perform well at figuring out what the intended audience likely knows and what it does not. It occasionally ignores explicit requests to define technical terms, throwing around specialist lingo such as long-range dependencies and contextual relationships in the text without further explanation.

Another, and well-known, drawback that we observed is the potential for hallucinations, coupled with the AIs failure to verify its own claims. The model is trained to produce the most likely sequence of words that follow the provided context, and it does not have the ability or the obligation to check these statements against verified sources. For these reasons, it should be considered more of a conversational and input transformation engine rather than an information-retrieval engine, and double-checking the output for accuracy is of the essence. In our experiment, ChatGPT provided incorrectly placed references to existing papers a step up from the oft-observed production of references to non-existent papers.

It also occasionally provides very misleading suggestions, such as adopting a writing style infused with loquacity and visible emotion and theatricality in a policy brief, because that is what Italians apparently enjoy.

Among the issues we came across, prompt sensitivity stands out as a potential pitfall for naive users. We found that the ChatGPT is very sensitive to how instructions are formulated and that minimal changes can result in dramatically different outputs.

The exchanges shown in Figure 2 demonstrate this: as an aside from the main task, we tried questioning the model about its capabilities with two slightly different prompts, both ending with a leading question. Changing just one word in the prompt albeit a crucial one leads to two completely different answers, in which ChatGPT echoes what the user seems to think based on their question.

Figure 2 Sensitivity to minimal changes to the prompt

This tendency to cater to a users opinion was first observed by Perez et al. (2022) and labelled sycophancy. Wei et al. (2023) found that large language models tend to do this even when the user provides objectively incorrect statements, and that sycophantic behaviour can be mitigated with minimal additional fine-tuning.

Where the AI cannot think like a human (yet), it is humans who have to think like an AI and express requests in the way most likely to generate acceptable results. Optimisation of prompting for institutional communication is one evident area for future research. Another is fine-tuning of LLMs to generate domain-specific, possibly long-tail world knowledge in our reference context.

We conclude that ChatGPT can enhance productivity in policy-oriented writing, especially in the initial phase of outlining and structuring ideas, provided that users are knowledgeable about LLMs in general and are aware of ChatGPTs limitations and peculiarities. Yet, it cannot substitute for subject matter experts, and naive use is positively dangerous.

The AI agrees with us. In its own words,

while ChatGPT can generate content at a high level and provide valuable information on a wide array of topics, it should be seen as a tool to aid in research and discussion, rather than a replacement for true expert analysis and insight. Its best used to provide general information, generate ideas, or aid in decision-making processes, but should always be supplemented with rigorous research and expert opinion for high-level academic or professional work.

Autor, D (2022), The labor market impacts of technological change: From unbridled enthusiasm to qualified optimism to vast uncertaintly, NBER Working Paper 30074.

Biancotti, C, and C Camassa (2023), Loquacity and visible emotion: ChatGPT as a policy advisor, mimeo, Bank of Italy.

Bubeck, S, V Chandrasekaran, R Eldan, J A Gehrke, E Horvitz, E Kamar, P Lee, Y T Lee, Y Li, S M Lundberg, H Nori, H Palangi, M Ribeiro, and Y Zhang (2023), Sparks of artificial general intelligence: Early experiments with GPT-4, ArXiv, abs/2303.12712.

Danielsson, J (2023), When artificial intelligence becomes a central banker, VoxEU.org, 11 July.

Hansen, A, and S Kazinnik (2023), Can ChatGPT decipher Fedspeak?, mimeo, Federal Reserve Bank of Richmond.

Hu, K (2023), ChatGPT sets record for fastest-growing user base, Reuters, 2 February.

Kandpal, N, H Deng, A Roberts, E Wallace, and C Raffel (2022), Large language models struggle to learn long-tail knowledge, arXiv preprint arXiv: 2211.08411.

Korinek, A (2023), Language models and cognitive automation for economic research, CEPR Discussion Paper 17923.

Noy, S, and W Zhang (2023), The productivity effects of generative artificial intelligence, VoxEU.org, 7 June.

Perez, E, S Ringer, K Lukoit, K Nguyen, E Chen, S Heiner, et al. (2022), Discovering language model behaviors with model-written evaluations, Findings of the Association for Computational Linguistics: ACL 2023, 13387434.

Schick, T, J Dwivedi-Yu, R Dess, R Raileanu, M Lomeli, L Zettlemoyer, and T Scialom (2023), Toolformer: Language models can teach themselves to use tools, arXiv preprint arXiv:2302.04761.

Taliaferro, D (2023), Constructing novel datasets with ChatGPT: Opportunities and limitations, VoxEU.org, 15 June.

Wei, J, D Huang, Y Lu, D Zhou, and Q V Le (2023), Simple synthetic data reduces sycophancy in large language models, arXiv preprint arXiv:2308.03958.

WGA on Strike (2023), The campaign, 1 May 2023.

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Loquacity and visible emotion: ChatGPT as a policy adviser - CEPR

Many predictions about AI taking our jobs have not yet materialised, but the threat of AI-driven fraud is a much more worrying development.

Few technologies have attracted as much hype as AI, particularly the most recent versions of generative AI such as ChatGPT, with its uncanny capabilities to create text or images, or hold disturbingly human-like conversations via text. These unsettling technologies have inspired some frightening predictions.

So far, in terms of their impact on accountancy, AI tools are proving useful for completing time-consuming tasks accurately and efficiently, just as other time-saving, efficiency-improving technologies did before them. They are not yet destroying thousands of jobs. Nor do they seem likely to wipe out humanity. But some AI tools will be used by criminals to commit fraud, against victims including accountancy firms and their clients.

It is difficult to be certain about the scale of AI-enabled fraud, in part because fraud tends to be under-reported or even undetected. A spokesperson for Action Fraud, which collects and analyses fraud reports, says it cannot supply data on AI-enabled fraud, as victims would need to know if AI was involved when reporting an incident.

But figures from the governments most recent Cyber Security Breaches Survey, published in April 2023, suggest that almost one in three businesses (32%) were affected by cyber security incidents during 2022. These are among the types of fraud where AI technologies are most likely to be used, often to help create convincing fake emails, documents or images which could be used in phishing emails.

Fake materials created with AI might also facilitate payment diversion or invoice fraud (also known as mandate or push payment fraud), in which a recipient is suckered into making payments to fraudsters. The same techniques might be used to persuade a recipient that an email they receive has come from the firms bank, or from HMRC.

AI tools can also be used by fraudsters to gather useful information from company websites, social media platforms and other online sources, which they can then use to make emails and/or supporting fake documents more convincing. While such social engineering methods are not new, they are resource-intensive, so using AI enables fraudsters to create tailored materials and messages more quickly and efficiently, and on a wider scale, possibly even in multiple languages.

Michelle Sloane, a partner at legal firm RPC, which specialises in resolution of white-collar crime, has seen examples of fake documents and images that appear to have been created using AI. She warns that accountants who cannot detect these clever forgeries may be unwittingly involved in money laundering or tax fraud.

Sloane thinks this type of AI-enabled activity is becoming more widespread: Its definitely growing and will continue to grow as the technology gets better. Her colleague Alice Kemp, a barrister and senior associate at RPC, says several criminal cases involving use of these technologies are due to come to trial in the near future.

ICAEW Head of Tech Policy Esther Mallowah highlights another way fraudsters can use AI: to create fake voices on phone calls. This technique had been used even before the newest forms of generative AI had been created. In 2019 and 2020 there were cases in which CEOs of subsidiary companies a UK-based energy company in 2019 and a Hong Kong-based subsidiary of a Japanese firm in 2020 thought they were being contacted by the CEO of their overseas-based parent company, who then asked them to transfer large amounts of money.

The 2019 fraud led to the theft of $243,000. In the 2020 case, supported by fake emails supposedly sent from the parent company CEO and from a law firm about a fictitious corporate acquisition, the fraudsters asked the firm targeted to transfer $35m. The amount they obtained is not in the public domain, but certainly exceeded $400,000. I think thats a technique that could move into the accountancy space, says Mallowah.

ICAEW economic crime manager Mike Miller also highlights the growing use of AI technologies to perpetrate synthetic identity theft, in which AI-generated false data or documents are combined with stolen genuine personal data, for purposes including making fraudulent credit applications.

Fraud knows no boundaries and anyone working for a smaller practice should not assume they are less likely than a larger business to be targeted. Smaller firms or their clients may be seen by a fraudster as profitable soft targets, less able than larger firms to invest time and money in fraud countermeasures.

Mallowah suggests that one big problem for the accountancy sector is that accountants often dont see themselves as being attractive targets, especially those in smaller firms. But, she warns: From what were seeing, thats not necessarily true. Sloane thinks the threat posed by AI-enabled crime may actually be greater for smaller accountancy firms.

As with other cyber security threats, the strongest form of defence may be training staff to identify risks and take action to mitigate them. Kemp also advises providing guidance to employees about the dangers of revealing personal details including hobbies and interests on social media, thus limiting the material a fraudster might feed into an AI tool when creating phishing emails.

Training must be complemented with good practice. For example, the risk of falling for payment diversion fraud is significantly reduced if staff have to use an independently checked phone number to verify the identity of someone who is emailing the business asking for funds to be transferred.

These measures can be supplemented by use of company and/or identity verification services, alongside a Companies House check or, in the case of a new supplier, viewing a copy of a companys certificate of incorporation (although RPC says there have already been cases of these certificates being faked).

HMRC did not respond in detail to questions about its knowledge of the scale and nature of AI-based attempts to commit tax-related fraud, but it did provide a short statement: Tax crime doesnt stand still and neither do we. The adoption of new technologies is a constant and evolving threat that we are alive to, including the potential risks posed by AI.

In future we may also see more use of AI to fight AI-enabled fraud. There are already AI-based tools for identity verification, for ensuring compliance with anti-money laundering rules, or for preventing other forms of fraud. Examples include solutions provided by Capium and DataRobot.

The largest accountancy and audit firms have also developed AI-based tools to detect anomalies in general ledgers and improve the accuracy of audit processes. Such tools may use machine learning, algorithms and natural language processing to sift through huge quantities of data and text, looking for patterns or behaviour that suggests fraudulent activity.

Mallowah says ICAEW is working hard to spread awareness of all these issues and best practice within the accountancy sector, at events and via content including the monthly Insights cyber round-up. She also thinks businesses of all kinds will need to invest in AI expertise.

But she again emphasises the most important change that could help accountancy firms resist AI-enabled fraud might be overturning the misplaced belief that they are unlikely to be targeted: Shifting that mindset is really important.

Some of the hype about AI may be overblown, but dont let that blind you to the real dangers these tools could pose. Accountants will need to exploit both artificial and human intelligence over the years ahead to keep their own businesses, employees and clients safe.

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AI fraud and accountants - economia