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IBM enables responsible Enterprise AI with watsonx.governance

Cairo, Egypt - IBM announced the general availability (GA) of watsonx.governance in early December to help businesses automate AI lifecycle governance and proactively manage risk and compliance. This expands IBM’s industry-leading AI governance capabilities to help clients govern machine learning and generative AI tools, applications, and models in one place. Watsonx.governance is one of three software products in the IBM watsonx AI and data platform, along with a set of AI assistants designed to help enterprises scale and accelerate the impact of AI with trusted data. The platform also comprises the watsonx.ai next-generation enterprise studio for AI builders and the watsonx.data open, hybrid, and governed data store.   In the first release of watsonx.governance, clients using LLM models within watsonx.ai — including IBM-developed and various third-party models, such as Llama 2 and those from the Hugging Face community — will be able to govern them on the cloud. Consistent with IBM’s approach to open AI, IBM is expected to expand these capabilities in 1Q24 to allow clients to govern third-party AI models from any vendor — on cloud or on-premises — to orchestrate governance processes across their entire organizations. Watsonx.governance also can help clients facilitate compliance with internal policies, industry standards, current and future regulation. IBM believes in regulation at the use case level, with responsibilities on companies to operate trustworthy AI. To do this, there needs to be an understanding of how and with what data models are trained, how they arrive at their recommendations, and whether they are routinely screened for harmful bias. These priorities — transparency, explainability, safety and fairness — are the foundation of multiple proposals advancing worldwide for the regulation of AI. IBM watsonx.governance is designed to help clients manage their AI and prepare to meet those regulatory requirements head on.  IBM offers automated capabilities that cover all three major pillars of AI governance — lifecycle governance, risk management, and compliance. Expanded capabilities for LLMs include:  Monitor new LLM Metrics: Monitor and alert in both inputs and outputs of LLMs when pre-set thresholds are breached for quality metrics and drift, instances of toxic language — including hate, abuse, and profanity — and Personal Identifiable Information (PII).  Visibility into LLM development: Automatically collect information about the model building process, while explaining decisions to mitigate hallucinations and other new risks.  Transparency of AI Lifecycle for LLMs: Automatically document model facts across all stages of the lifecycle, monitor for drift for text models, and track health details such as data size, latency, and throughput to identify bottlenecks and compute intensive workloads.  Validation Tools for LLMs: Enable prompt engineers to map LLM outputs to provided context/reference data for Q&A use cases to determine whether the LLM is appropriately influenced by the reference data to help ensure it is relevant to the output.  IBM fosters open innovation and collaboration to help clients deploy AI in a transparent and responsible way. IBM Consulting helps clients scale responsible AI with both automated AI model governance and organizational AI governance that encompasses people, process and technology from IBM and strategic partners. Our consultants have deep skills in establishing organizational culture and accountability, AI ethics boards, training, regulatory and risk management and mitigating cybersecurity threats, all using human-centric design.IBM’s commitment to trust and transparency forms the foundation for its products. Recently, it announced intellectual property protection for its IBM-developed watsonx models. Watsonx.governance is a continued investment in helping to foster responsible AI practices across various business domains and industries.  
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Amazon trains massive AI model 'Olympus' to rival OpenAI, Alphabet

Amazon has formed a dedicated team to train a massive AI model called "Olympus," according to a Reuters report.Amazon is making a significant financial investment in this large language model (LLM) with the hope that it can compete with leading models developed by OpenAI and Alphabet, according to insider information.The "Olympus" model is said to possess a staggering 2 trillion parameters, potentially making it one of the largest models under development. In comparison, OpenAI's renowned GPT-4 model is reported to have one trillion parameters.Rohit Prasad, the former head of Alexa, is leading the team responsible for "Olympus" and now reports directly to CEO Andy Jassy. In his role as the head scientist for general artificial intelligence (AGI) at Amazon, Prasad has brought in researchers who were previously engaged with Alexa AI and the Amazon science team to collaborate on model training, thus consolidating AI efforts across the company with dedicated resources.Already Amazon has trained smaller models, such as Titan, and has also established partnerships with AI model startups like Anthropic and AI21 Labs, making these models available to users of Amazon Web Services (AWS).The insiders indicate that there is no specific timeline for the release of the new model.LLMs serve as the foundational technology for AI tools that learn from extensive datasets to generate responses akin to those of humans. Training larger AI models is a costly endeavor due to the substantial computing power required. In an earnings call in April, Amazon executives expressed their intention to increase investments in LLMs and generative AI while reducing expenditures in their retail business related to fulfillment and transportation.
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AI has exceeded executives' expectations: Bain & Company

 Seventy-five percent of the more than 570 executives recently surveyed by Bain & Company said AI has already met or exceeded their expectations. According to Bain’s fourth annual Global Technology Report released today, the current generation of AI tools and models could help companies speed up 20% of worker tasks without loss in quality.While the ease of access to large language model (LLM) application programming interfaces (APIs) has made it relatively easy to demonstrate new AI-powered products, Bain’s survey found that 89% of software companies are already using AI to differentiate their products—15 percentage points higher than other sectors. Early adopters of AI are already seeing results and productivity gains as companies explore new ways to use AI for their businesses, Bain found.“Innovation is happening quickly, and we are still in early days,” said David Crawford, global head of Bain’s Technology practice. “Three out of four software companies we surveyed believe that early movers will have a sustained advantage that will not level off. Software leaders expect the technology to generate significant opportunities to increase topline growth and customer retention. Our research shows that, in this fast-moving environment, companies that take a wait-and-see approach in terms of AI are at risk of being left behind.”Oliver Bittner, partner and head of Enterprise Technology & Digital practice at Bain & Company Middle East, said: “With AI transforming nearly every industry worldwide, the Middle East is making significant investments, particularly at a time when the region is accelerating rapidly. These innovative technologies, if employed correctly, can not only change the way we work and live but also present challenges that could disrupt industries in the future. This highlights the need for careful planning to ensure the AI is being utilized effectively.”Software companies also need to address how the adoption of generative AI by their customersand competitors can impact their core business. Many customer concerns around data protection and access, personally identifiable information, audit trails, prompt grounding with proprietary data, and integration with other machine learning (ML) and automation technologies are served in platform layers, beyond the LLM. The report highlighted that this is the area that software companies can differentiate themselves, leveraging established positions in customer architectures.Generative AI talent implicationsAs customers introduce AI into their own processes, job roles are expected to change. Engineering and sales and marketing are among the functions most likely to benefit from AI over the next 18 months. Companies will need more engineering talent for AI and ML, particularly with experience building or integrating LLMs.Generative AI will change the way companies market and sell their products and services as it enables significant automation across every step of the customer life cycle. In particular, demand and lead generation, digital self-service sales, customer success, and other support activities all have the potential to benefit from the types of automation that generative AI enables.When it comes to investor appetite, Bain’s report showed that most investors agree that AI will have a significant effect on the tech sector. In fact, investors’ enthusiasm for AI is high, with AI and ML investments leading venture growth in the first half of 2023. However, most investors think that the evolution of the competitive landscape remains to be seen.To avoid disruption risks, investors must consider both disruption potential and structural barriers in the market. They must also consider whether or not companies own proprietary data could enrich generative AI applications.“Top funds are not waiting to see how generative AI changes this space. They are biasing toward action to capitalize on the potential of their incumbent software assets,” said Crawford.Investor perspectives: A buyers’ market is coming for tech assetsInvestor sentiment on the broader tech sector, on the other hand, has been lackluster since the third quarter of 2022. With deal volumes and exit values down, a growing backlog of deals, including more than $700 billion of tech assets purchased between 2018 and 2021, has led to longer hold times of tech portfolio companies.In 2023, nearly half of tech portfolio companies have been held for more than four years, and for the first time since 2012, more than 40% of tech portfolio companies are being held for more than four years. This backlog of long-held portfolio assets is growing more quickly than the record level of dry powder that is holding steady, which will create a buyer’s market when activity picks up.Investors reward tech companies differently based on a company’s context and point in the life cycle, according to Bain. Some investors are attracted to young, disruptive companies based on their growth potential. As companies and their markets mature, investors expect a mix of growth and returns. Mature companies with a proven track record in stable markets can expect slower growth while their investors are closely focused on profitability.To maximize value, investor relations strategies of tech companies should change over time as markets mature. Understanding the role between market maturity, investor expectations, and sources of total shareholder returns are essential to deliver shareholder value at every step of the journey.Post-globalization: Tech manufacturers diversify supply chains and R&D locationsIn another chapter of the tech report, Bain predicts that the global footprint of the technology value chain is likely to look very different, a decade from now. Macroeconomic shocks in the last few years have prompted tech manufacturers to build resilience into their supply chains, primarily by expanding their geographic footprints to new locations in Asia, Europe, and North America, creating more flexibility within their talent pools. This initially led companies to align their supply chains processes, but now these businesses are safeguarding critical business aspects and are getting closer to end markets through diversifying R&D, talent, and innovation hubs.Some of the other topics discussed in this year’s report include AI and cybersecurity, digital innovation, and intelligent edge.Brahaim Laaidi, a partner at Bain & Company Middle East, said: “At a time where technology is constantly at our fingertips, Bain & Company’s latest annual Global Technology Report shows the impact of these innovative tools on our daily lives and how they are shaping our business practices. There is no doubt that technology will continue to advance, and we can anticipate the introduction of new technologies in the near future. Therefore, it is important for businesses to not only stay one step ahead and be prepared to embrace these tools but also understand how they will impact their day-to-day operations.”