The main topic is the AI startup Elemental Cognition, founded by David Ferrucci, the former leader of the IBM Watson team.
Key points:
1. Elemental Cognition has raised nearly $60 million in funding.
2. The company aims to develop AI that "thinks before it talks" and offers chatbot products for various industries.
3. Elemental's standout offering is its hybrid AI platform that combines large language models with an AI-powered reasoning engine.
Main Topic: Elemental Cognition's AI startup, led by David Ferrucci, secures nearly $60 million in funding.
Key Points:
1. David Ferrucci, former IBM Watson team leader, has raised $59.95 million in equity sales from 17 investors.
2. Elemental Cognition aims to create AI with advanced reasoning capabilities and offers two chatbot solutions, Cogent and Cora, for various applications.
3. The company's hybrid AI platform integrates large language models with an AI-driven reasoning engine for precise and controlled responses.
IBM's consulting business could potentially benefit from artificial intelligence by using automation to reduce labor costs, marking a potential "golden age" for the industry, according to analysts at Melius Research.
Main topic: Investment strategy for generative AI startups
Key points:
1. Understanding the layers of the generative AI value stack to identify investment opportunities.
2. Data: The challenge of accuracy in generative AI and the potential for specialized models using proprietary data.
3. Middleware: The importance of infrastructure and tooling companies to ensure safety, accuracy, and privacy in generative AI applications.
IBM has announced Watsonx Code Assistant for Z, a generative AI-assisted product that will help accelerate the translation of COBOL to Java on IBM Z, with the goal of modernizing COBOL applications while preserving the performance, security, and resiliency capabilities of IBM Z.
Cloud computing vendor ServiceNow is taking a unique approach to AI by developing generative AI models tailored to address specific enterprise problems, focusing on selling productivity rather than language models directly. They have introduced case summarization and text-to-code capabilities powered by their generative AI models, while also partnering with Nvidia and Accenture to help enterprises develop their own generative AI capabilities. ServiceNow's strategy addresses concerns about data governance and aims to provide customized solutions for customers. However, cost remains a challenge for enterprises considering the adoption of generative AI models.
Main topic: Aily Labs, an AI-for-enterprise startup, raises €19m in funding to expand its team and further develop its AI models for productivity and efficiency in various industries.
Key points:
1. Aily Labs uses AI models to create products that increase productivity, efficiency, and cost-savings for clients, particularly in the pharmaceutical industry.
2. The startup differentiates itself by leveraging existing open-source AI models and utilizing a combination of machine learning approaches, including classification and regression models.
3. With the funding, Aily Labs plans to expand its GenAI team, diversify its client base beyond pharmaceutical companies, and enhance its capabilities in text generation and competitive intelligence.
IBM has launched an advertising campaign to promote its new enterprise-focused artificial intelligence platform, watsonx, by showcasing its transformative power and ability to accelerate business objectives.
Generative AI, a technology with the potential to significantly boost productivity and add trillions of dollars to the global economy, is still in the early stages of adoption and widespread use at many companies is still years away due to concerns about data security, accuracy, and economic implications.
MSCI is expanding its partnership with Google Cloud to utilize generative AI for investment advisory purposes, aiming to provide investors with enhanced decision-making capabilities, deep data-driven insights, and accelerated portfolio implementation in areas such as risk signals, conversational AI, and climate generative AI.
SAP and Google Cloud have expanded their partnership to bring generative AI-powered solutions to industries such as automotive and sustainability to help improve business decision-making and enhance sustainability performance.
Microsoft and Datadog are well positioned to benefit from the fast-growing demand for generative artificial intelligence (AI) software, with Microsoft's exclusive partnership with OpenAI and access to the GPT models on Azure and Datadog's leadership in observability software verticals and recent innovations in generative AI.
Generative artificial intelligence, particularly large language models, has the potential to revolutionize various industries and add trillions of dollars of value to the global economy, according to experts, as Chinese companies invest in developing their own AI models and promoting their commercial use.
AllianceBernstein has built a team focused on AI and data science, using machine learning and natural language processing to uncover potential risks, make investment decisions, and improve risk management, resulting in cost savings and increased efficiency.
Apple is investing heavily in artificial intelligence, with multiple AI models being developed across various teams, including a conversational AI unit called "Foundational Models" led by John Giannandrea, Apple's head of AI, and other teams working on image generation and multimodal AI.
McKinsey and Salesforce are collaborating to accelerate the adoption of generative AI in sales, marketing, commerce, and service, aiming to improve customer experiences, increase sales productivity, personalize digital marketing campaigns, and reduce call resolution time.
IBM has announced the release of a new family of foundation models called Granite that apply generative AI to both language and code, aiming to improve productivity and creativity in various business domains, such as summarization, question-answering, and classification. These models are trained on specialized datasets and undergo rigorous governance, risk, and compliance reviews to ensure trust and transparency. IBM also emphasizes the empowerment of organizations by allowing them to personalize their models and retain ownership of their data. The release of Granite models is just the beginning, with more models and complementary capabilities planned for the future.
The rise of generative AI is accelerating the adoption of artificial intelligence in enterprises, prompting CXOs to consider building systems of intelligence that complement existing systems of record and engagement. These systems leverage data, analytics, and AI technologies to generate insights, make informed decisions, and drive intelligent actions within organizations, ultimately improving operational efficiency, enhancing customer experiences, and driving innovation.
As generative AI continues to gain attention and interest, business leaders must also focus on other areas of artificial intelligence, machine learning, and automation to effectively lead and adapt to new challenges and opportunities.
Investors are focusing on the technology stack of generative AI, particularly the quality of data, in order to find startups with defensible advantages and potential for dominance.
IBM is positioned to take advantage of the AI revolution with its focus on enterprise solutions and potential for significant revenue growth, leading to a stock undervaluation of approximately 47%.
Microsoft and Google have introduced generative AI tools for the workplace, showing that the technology is most useful in enterprise first before broader consumer adoption, with features such as text generators, meeting summarizers, and email assistants.
McKinsey has launched an open-source ecosystem, offering tools such as Vizro and CausalNex to help users visualize data from AI models and build cause-and-effect models, enabling organizations to scale their AI projects and realize value from their AI portfolios more efficiently.
Meta Platforms showcased its new generative AI tools, including AI assistants, chatbots, and image generators, which could increase engagement with its apps and drive revenue for its messaging businesses, potentially propelling the company back into the $1 trillion club.
China-based tech giant Alibaba has unveiled its generative AI tools, including the Tongyi Qianwen chatbot, to enable businesses to develop their own AI solutions, and has open-sourced many of its models, positioning itself as a major player in the generative AI race.
Generative AI is transforming various industries, including telecommunications, banking, public safety, B2B sales, biopharmaceuticals, and creative agencies, by enhancing efficiency, improving decision-making, providing customer-centric solutions, ensuring safety and compliance, driving innovation, promoting adaptive learning, challenging the status quo, and offering holistic solutions.
Generative AI has the potential to transform various industries by revolutionizing enterprise knowledge sharing, simplifying finance operations, assisting small businesses, enhancing retail experiences, and improving travel planning.
Artificial intelligence (AI) startup Altana has launched its next-generation Atlas, which utilizes AI and a large language model to provide a comprehensive map of the global supply chain, enabling businesses, governments, and logistics firms to monitor and manage supply chain-related issues more effectively.
IBM, with its specialized AI applications and Watson system, is positioned to be a major player in the AI market and drive solid growth, particularly in its consulting business, according to analysts at Bank of America. With the potential for billions of dollars in revenue, AI could finally turn IBM's AI expertise into a profitable business.
Generative artificial intelligence (AI) is expected to face a reality check in 2024, as fading hype, rising costs, and calls for regulation indicate a slowdown in the technology's growth, according to analyst firm CCS Insight. The firm also predicts obstacles in EU AI regulation and the introduction of content warnings for AI-generated material by a search engine. Additionally, CCS Insight anticipates the first arrests for AI-based identity fraud to occur next year.
Generative AI can significantly reduce cloud migration efforts, with McKinsey reporting a 30-50% decrease in time, as the technology evolves and becomes more efficient, making the relationship between generative AI and the cloud "symbiotic," according to Bhargs Srivathsan of McKinsey. She also highlighted the key use cases for generative AI, such as content generation, customer engagement, synthetic data creation, and coding. However, Srivathsan emphasized the need for public cloud usage and the importance of guardrails to protect proprietary data and ensure compliance in regulated industries.
Generative AI start-ups, such as OpenAI, Anthropic, and Builder.ai, are attracting investments from tech giants like Microsoft, Amazon, and Alphabet, with the potential to drive significant economic growth and revolutionize industries.
Companies are focusing on learning how to effectively deploy AI tools, realizing that poorly crafted prompts and unspecialized models can lead to inaccuracies and inefficiencies, with some firms creating prompt libraries and in-house models to improve AI output. Specialist fine-tuning and the use of libraries of embeddings are becoming crucial for companies to personalize AI models and achieve better outcomes. While some believe the importance of prompts will decrease as AI becomes more intelligent, others argue that engineered prompts will still be needed for irregular tasks.
The University at Albany and IBM are collaborating on a $20 million investment to establish the Center for Emerging Artificial Intelligence Systems (CEAIS) and advance AI research goals, while creating a SUNY AI Research Group to lead future strategies in AI research, education, policy, and workforce development.
NetSuite, owned by Oracle, is adding generative AI capabilities to its finance software, allowing companies to automate tasks such as writing collection letters and analyzing financial data.
ExxonMobil's Senior IT Executive, Andrew Curry, states that high-quality data is crucial for successful AI and machine learning initiatives, highlighting the importance of having a strong data strategy in place to make the most of emerging technologies. While ExxonMobil is cautious about utilizing generative AI, the company plans to leverage ML and AI capabilities in areas such as finance and trading, supply chain management, and interpreting seismic surveys.
Accenture and SAP are collaborating to help organizations adopt generative AI and accelerate ERP transformation in the cloud, offering AI-enabled solutions and use cases to enhance business performance and employee productivity.
IBM and Amazon Web Services (AWS) are expanding their partnership to train 10,000 consultants in generative AI by the end of 2024 and deliver joint solutions and services to help clients integrate AI into their business and IT operations. The companies will focus on solutions such as contact center modernization, platform services, and supply chain ensemble, and will also integrate AWS generative AI services into IBM Consulting Cloud Accelerator. Additionally, IBM plans to make watsonx.data, watsonx.ai, and watsonx.governance available on AWS by 2024.
Generative AI, a type of artificial intelligence technology that can produce content based on user specifications, has gained significant attention and investment. DBRG, a digital infrastructure asset manager, is well-positioned for growth due to the increasing demand for computational power required by AI workloads. The company offers deeply discounted preferred shares that provide high yields and present a solid investment opportunity for income investors.
Microsoft CEO Satya Nadella has outlined plans to integrate artificial intelligence (AI) across the company's customer solutions and tech stacks, with a focus on natural language processing and generative AI, as well as incorporating AI Copilot into its most used products and experiences.
SAP is set to release AI-driven capabilities across its SuccessFactors HXM Suite, including a talent intelligence hub, generative AI use cases, and an AI copilot named Joule, all aimed at enhancing human experiences and supporting workforce development.
Lenovo and NVIDIA have expanded their partnership to offer hybrid solutions and engineering collaboration, enabling businesses to easily deploy generative AI applications using accelerated systems, AI software, and expert services. The collaboration aims to bring the power of generative AI to every enterprise and transform industries by deploying tailored AI models across all data creation locations, from the edge to the cloud.
The financial results of Alphabet and Microsoft show that new AI technologies are helping these companies grow their revenues, indicating strong market demand for software that runs off generative AI, which is good news for startups in the space.
SAP has announced new generative AI capabilities that leverage experiential and operational data to provide businesses with a holistic view of their customers, helping to automate tasks, analyze data, and deliver personalized experiences for customers. These capabilities include automating labor-intensive tasks, supercharging catalog management and product discovery, retrieving answers in natural language, and surfacing customer profile intelligence.
Generative AI is experiencing a moment of rapid adoption in the enterprise market, with the potential to fundamentally change the rules of the game and increase productivity, despite concerns about data protection and intellectual property.
