Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Because LLMs have an ability to analyse large amounts of data quickly and generate coherent text, there is growing understanding of the potential to improve services across a range of sectors including healthcare, law, education and in financial services including banking, insurance and financial planning.

This report, which is the first to explore the adoption of LLMs across the finance ecosystem, shows that people working in this area have already begun to use LLMs to support a variety of internal processes, such as the review of regulations, and are assessing its potential for supporting external activity like the delivery of advisory and trading services.

Alongside a literature survey, researchers held a workshop of 43 professionals from major high street and investment banks, regulators, insurers, payment service providers, government and legal professions.

The majority of workshop participants (52%) are already using these models to enhance performance in information-orientated tasks, from the management of meeting notes to cyber security and compliance insight, while 29% use them to boost critical thinking skills, and another 16% employ them to break down complex tasks.

The sector is also already establishing systems to enhance productivity through rapid analysis of large amount of text to simplify decision making processes, risk profiling and to improve investment research and back-office operations.

When asked about the future of LLMs in the finance sector, participants felt that LLMs would be integrated into services like investment banking and venture capital strategy development within two years.

They also thought it likely that LLMs would be integrated to improve interactions between people and machines, for example dictation and embedded AI assistants could reduce the complexity of knowledge intensive tasks such as the review of regulations.

But participants also acknowledged that the technology poses risks which will limit its usage. Financial institutions are subject to extensive regulatory standards and obligations which limits their ability to use AI systems that they cannot explain and do not generate output predictably, consistently or without risk of error.

Based on their findings, the authors recommend that financial services professionals, regulators and policy makers collaborate across the sector to share and develop knowledge about implementing and using LLMs, particularly related to safety concerns. They also suggest that the growing interest in open-source models should be explored and could be used and maintained effectively, but that mitigating security and privacy concerns would be a high priority.

Professor Carsten Maple, lead author and Turing Fellow at The Alan Turing Institute, said: “Banks and other financial institutions have always been quick to adopt new technologies to make their operations more efficient and the emergence of LLMs is no different. By bringing together experts across the finance ecosystem, we have managed to create a common understanding of the use cases, risks, value and timeline for implementation of these technologies at scale.”

Professor Lukasz Szpruch, programme director for Finance and Economics at The Alan Turing Institute, said: “It’s really positive that the financial sector is benefiting from the emergence of large language models and their implementation into this highly regulated sector has the potential to provide best practices for other sectors. This study demonstrates the benefit of research institutes and industry working together to assess the vast opportunities as well as the practical and ethical challenges of new technologies to ensure they are implemented safely.”

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The extended partnership between Wipro and IBM combines the former’s extensive industry expertise with IBM’s leading AI innovations. The collaboration seeks to develop joint solutions that facilitate the implementation of robust, reliable, and enterprise-ready AI solutions.

The Wipro Enterprise AI-Ready Platform harnesses various components of the IBM watsonx suite, including watsonx.ai, watsonx.data, and watsonx.governance, alongside AI assistants. It offers clients a comprehensive suite of tools, large language models (LLMs), streamlined processes, and robust governance mechanisms, laying a solid foundation for the development of future industry-specific analytic solutions.

Jo Debecker, Managing Partner & Global Head of Wipro FullStride Cloud, said: “This expanded partnership with IBM combines our deep contextual cloud, AI, and industry expertise with IBM’s leading AI innovation capabilities.”

A key aspect of this collaboration is the establishment of the IBM TechHub@Wipro, a centralised tech hub aimed at supporting joint client pursuits. This initiative will bring together subject matter experts, engineers, assets, and processes to drive and support AI initiatives.

Kate Woolley, General Manager of IBM Ecosystem, commented: “We’re pleased to reach this new milestone in our 20-year partnership to support clients through the combination of Wipro’s and IBM’s joint expertise and technology, including watsonx.”

The Wipro Enterprise AI-Ready Platform offers infrastructure and core software for AI and generative AI workloads, enhancing automation, dynamic resource management, and operational efficiency in the enterprise. Moreover, it caters to specialised industry use cases, such as banking, retail, health, energy, and manufacturing, offering tailored solutions for customer support, marketing, feedback analysis, and more.

Nagendra Bandaru, Managing Partner and President of Wipro Enterprise Futuring, highlighted the flexibility of the platform, stating: “Wipro’s Enterprise AI-Ready Platform will allow clients to easily integrate and standardise multiple data sources augmenting AI- and GenAI-enabled transformation across business functions.”

In addition to facilitating AI governance through the AI lifecycle, the platform prioritises responsible AI practices, ensuring transparency, data protection, and compliance with relevant laws and regulations.

As part of this collaboration, Wipro associates will undergo training in IBM hybrid cloud, AI, and data analytics technologies, further enhancing their capabilities in developing joint solutions.

(Photo by Carson Masterson on Unsplash)

See also: Reddit is reportedly selling data for AI training

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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“I’m an economic historian, before I became a central banker. Economies adapt, jobs adapt, and we learn to work with it. And I think, you get a better result by people with machines than with machines on their own,” Bailey told the BBC.

Bailey’s comments come as the latest economic assessment shows that UK businesses investing in AI are expected to see gains in efficiency and output. Utilising AI is anticipated to provide productivity benefits across multiple sectors.  

However, Baroness Stowell of the House of Lords has cautioned that the UK risks “missing out on the AI goldrush” if it does not act quickly.

A report from the Lords’ Communications and Digital Committee honed in on large language models and tools like ChatGPT. The report called for updated copyright laws and urged the government to provide clarity on AI regulation—warning too much could hinder AI development in the country.

Both Bailey and the Lords committee seem to agree that the focus should be on harnessing the upsides of AI while managing legitimate risks. 

The financial services industry also stands to gain from responsible AI adoption.

“Generative AI brings potentially exciting benefits for financial institutions. When it comes to fighting financial crime, for example, AI can improve the accuracy and speed of detection by analysing large data sets,” said Dr Henry Balani, Head of Industry & Regulatory Affairs at Encompass Corporation.

Balani emphasised however that key roles like Know Your Customer (KYC) analysts are irreplaceable for now. “It will instead accelerate existing processes and augment the work of analysts, empowering them to detect financial crime risk more quickly and comprehensively,” he added.

“The maximum value of generative AI can only be realised if banks and financial institutions have already put in place robust digital and automated processes to optimise the quality of data collated and deliver deeper customer insights. By prioritising this now, banks will be well equipped to take advantage of this new technology as it continues to evolve and mature.”

Last month, research from EXL found that around 89 percent of insurance and banking firms in the UK have introduced AI solutions over the past year. However, issues with data optimisation are often hindering their benefits.

(Image Credit: Bank of England under CC BY-NC-ND 2.0 DEED license. Cropped from original for effect.)

See also: Experts from 30 nations will contribute to global AI safety report

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The researchers surveyed executives at top UK insurers and lenders about their AI strategies and found that 44 percent have deployed AI across eight or more business functions—especially in marketing, business development, and regulatory compliance. 

Nearly 9 in 10 financial services leaders reported investing upwards of £7.9 million in AI over their last fiscal year. Over a third invested £39 million or more, exemplifying the industry’s willingness to commit major capital to AI implementation.

Despite the positive strides in AI integration, the study suggests that organisations might be overlooking the importance of prioritising their data operations. Nearly half (47%) admitted their organisations are only “minimally data driven,” raising concerns about the effectiveness of AI implementation without a solid data foundation.

“It’s clear industry leaders recognise AI’s potential, but external pressures to implement quickly can lead to unchecked investment,” commented Kshitij Jain, EMEA Practice Head at EXL. “The risk is that ensuring operations are truly data driven gets deprioritised, which can prove very costly.”

The research also identified a group of “Strivers,” representing 45 percent of respondents, who are implementing AI more narrowly across around four functions. Their focused approach has allowed them to efficiently leverage AI for cost-cutting, outperforming early AI adopters by 23 percentage points.

Additionally, over half of respondents are investing more in AI specifically due to advancements in generative AI. However, 70 percent voiced deep concerns about risks related to generative AI like potential brand damage and inaccurate data outcomes.

“The key with any AI rollout is a measured, strategic approach—getting the data architecture right, testing solutions, and training employees,” Jain concluded. “For enterprise adoption to succeed, boards must buy into AI’s capabilities and ensure investment is being used effectively.”  

A full copy of the research can be found here (registration required)

(Photo by Alev Takil on Unsplash)

See also: NCSC: AI to significantly boost cyber threats over next two years

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week and Cyber Security & Cloud Expo.

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Dimon expressed his belief that AI has the potential to revolutionise every aspect of JPMorgan’s operations, from trading and hedging to research and error detection. He described AI as a “living, breathing thing,” capable of transforming traditional processes and augmenting human capabilities.

Dimon’s enthusiasm for AI is grounded in its current applications within JPMorgan. He revealed that AI is already extensively used in equity hedging, idea generation, and large language models.

Despite the ongoing debate about the impact of AI on employment, Dimon remains pragmatic. He acknowledged that AI will replace certain jobs, but he emphasised that technology has historically led to job displacement and this evolution is a natural part of progress.

One of Dimon’s main concerns about AI technology revolves around its potential misuse by malicious actors, especially in cyberspace. He stressed the importance of establishing legal safeguards to prevent the misuse of AI.

Despite these concerns, Dimon remains optimistic about the positive impact of AI on the workforce and society. He highlighted the benefits of other technological breakthroughs, many of which can be further enhanced using AI.

“Your children will live to 100 and not have cancer because of technology, and they’ll probably be working three days a week. So technology’s done unbelievable things for mankind,” said Dimon.

Dimon outlined JPMorgan’s proactive approach to potential job displacement caused by AI implementation. He expressed the firm’s commitment to supporting employees who might be affected, stating that they plan to redeploy displaced workers in local branches or different functions within the company.

Dimon’s forward-thinking approach highlights the transformative power of AI in shaping the future of finance and other industries. However, it also reiterates the need for consideration of how to minimise negative impacts such as job displacement.

(Image Credit: Stuart Isett/Fortune Global Forum under CC BY-NC-ND 2.0 DEED license)

See also: Cyber Security & Cloud Expo: The alarming potential of AI-powered cybercrime

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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Gensler, who has been vocal about the risks and challenges posed by the cryptocurrency industry, now believes that AI is the technology that “warrants the hype” and deserves greater attention from regulators.

Gensler’s interest in AI dates back to 1997 when he became intrigued by the technology after witnessing Russian chess grandmaster Garry Kasparov’s infamous loss to IBM’s supercomputer, Deep Blue.

As an MIT professor, Gensler delved deeper into the study of AI, co-authoring a significant paper in 2020 that highlighted the risks posed by deep learning in the financial system.

His concern over the potential implications of mass automation using AI in the finance sector has led him to reevaluate regulatory approaches. Gensler believes that while AI can bring immense benefits to financial firms and their clients through enhanced predictive capabilities, it also carries significant risks that need to be addressed.

“Mass automation can have cascading implications for trillions of dollars in assets that trade on markets overseen by the SEC,” warns Gensler.

One of Gensler’s key concerns is the potential use of AI to obscure responsibility and accountability when things go wrong. Coordinating AI models among major trading houses could lead to increased market volatility and instability, a phenomenon that current regulatory regimes might not be equipped to manage.

As a result, Gensler has taken a proactive step by proposing one of the first regulatory frameworks for AI in the finance industry. His proposal requires trading houses and money managers to carefully evaluate their use of AI and predictive data to identify any conflicts of interest, especially when the interests of clients clash with company profits.

However, this shift in focus does not mean the SEC is easing its crackdown on cryptocurrencies.

Under Gensler’s leadership, the SEC has actively pursued legal action against major crypto firms like Ripple, Binance, and Coinbase. Several lawsuits are currently pending, signalling that the SEC remains committed to enforcing its actions against cryptocurrency companies that engage in scams and fraudulent activities.

Gensler’s emphasis on AI comes at a crucial time when the technology is making rapid strides in automating various financial processes.

While AI holds tremendous promise in revolutionising the industry, its unchecked growth could also lead to unforeseen challenges. By directing the SEC’s attention towards AI, Gensler aims to strike a balance between promoting innovation and safeguarding market integrity and investor interests.

(Photo by Petri Heiskanen on Unsplash)

See also: AI Act: The power of open-source in guiding regulations

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While making some recovery in the past couple of weeks, the crypto market has suffered a horrid couple of months. Bitcoin crashed around 50 percent between November 2021 and the end of January 2022. As of writing, the largest cryptocurrency remains around 37 percent down while many “altcoins” have still lost over 50 percent of their value.

DynaSets combine AI algorithms with professional hedge fund traders in a bid to maximise profits and minimise losses in a notoriously volatile market (although the same could be said for the “stonk” market in recent weeks…)

Marcello Mari, CEO of SingularityDAO, said:

“I’m impressed by the preliminary results from the beta version of our DynaSets.

Over the next month, we’ll be further empowering our traders with more tools including the ability to short the market and execute trades with leverage.

We’ll also be launching real machine learning tools that have never been used in the crypto market before.”

Since the beta launch of DynaSets on 20 December 2021:

• Bitcoin DynaSet shows 10.3% better performance over just “hodling” Bitcoin
• Ethereum DynaSet shows 12.59% better performance over hodling Ethereum

“As we move closer to our 1.0 product offering, we will further improve on the performance we demonstrated with our beta. That is, deliver further and further automation with a constant focus on crypto asset protection and AI safety,” commented Chris Poulin, CTO.

The combination of SingularityDAO’s AI algorithms and professional traders were reportedly able to identify potential triggers for the crypto decline including the Fed’s hawkish policy shift, electricity price rise, and political instability of the world’s second-largest producer of bitcoin, Kazakhstan.

As of writing, two DynaSets have been launched for the two largest cryptocurrencies: Bitcoin and Ethereum.

In 2020, full-stack AI solution SingularityNET announced that it was collaborating with Cardano due to Ethereum’s issues.

Earlier this month, Goertzel announced HyperCycle—a lightweight layer 2 architecture designed to enable inexpensive, high-speed, large-scale on-chain execution of microservices and specifically designed to optimise AI-related processes.

HyperCycle leverages TODA/IP ledgerless protocol, SingularityNET’s Proof of Reputation, and Cardano’s EUTxO model and Hydra sidechain framework.

Cardano seems a prime candidate for one of the next DynaSets that SingularityDAO launches.

(Photo by Jared Schwitzke on Unsplash)

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