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As enterprise technical decision-makers from the artificial intelligence (AI) and data community get ready for VentureBeat’s flagship enterprise AI event, VB Transform, we are excited to announce the nominees for the fifth annual AI Innovation Awards.
The winners will be announced during VentureBeat CEO Matt Marshall’s closing remarks on the main stage of Transform on July 12 in San Francisco. The remarks will also be live-streamed on our homepage.
Transform will be a two-day in-person event, July 11 and 12, featuring industry experts and peers coming together to provide comprehensive insights and best practices on enterprises’ data journeys. As a bonus, participants will have numerous opportunities to forge meaningful connections and expand their networks.
At the July 12 in-person event at San Francisco’s Marriott Marquis, VentureBeat will recognize and award various forms of excellence in AI through our fifth annual VB AI Innovation Awards. The winners will be in the following categories: Generative AI Innovator of the Year, Best Enterprise Implementation of Generative AI, Most Promising Generative AI Startup, Generative AI Visionary, Generative AI Diversity and Inclusion, and Generative AI Open Source Contribution.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
The nominees are drawn from our daily editorial coverage and the expertise, knowledge and experience of our nominating committee members. Prepare to witness the trailblazers and game-changers in the realm of generative AI take center stage as we recognize their outstanding contributions.
Thank you to our nominating committee members for their guidance, insights and recommendations:
Matei Zaharia, co-founder and CTO at Databricks
Tonya Custis, director of AI research, Autodesk
Di Mayze, global head of data and AI, WPP
Prem Natarajan, chief scientist, head of enterprise AI at Capital One
Kalyan Veeramachaneni, principal research scientist at MIT College of Computing
Here are the nominees for each category:
Generative AI Innovator of the Year
This award will go to the company that has pushed the boundaries of generative AI the furthest in the past year and demonstrated the most innovative use of the technology. The winner will have created an application, platform or service that showcases the vast potential of generative AI in a creative, impactful way.
DeepMind
Google in April acquired the London-based AI and machine learning (ML) research lab DeepMind with plans to take on the competitive threat posed by OpenAI and its game-changing ChatGPT. DeepMind in June released AlphaDev, a specialized version of AlphaZero, which has made a huge breakthrough by uncovering faster sorting and hashing algorithms. According to a recent report, a new system called Gemini will combine LLM technology with reinforcement learning techniques used in AlphaGo, with the goal of giving it new planning and problem-solving capabilities.
Anthropic is an AI safety and research company whose recently-debuted generative AI model Claude is considered a competitor for OpenAI’s ChatGPT. In May Anthropic announced that it had raised $450 million in series C funding led by Spark Capital, a venture capital firm that has backed companies like Twitter, Slack and Coinbase.
Best Enterprise Implementation of Generative AI
This award will highlight the top enterprise company that has implemented generative AI technology in a truly transformative way.
Microsoft has been working on incorporating generative AI into its Microsoft Dynamics and Power platforms with the goal of enabling enterprise applications with the power of generative AI. The company has also deployed its Copilot generative AI assistant across the Microsoft 365 suite of business productivity and collaboration apps. Recently Microsoft announced a partnership with Moody’s. Moody’s is using the Microsoft Azure OpenAI service as an engine that helps unlock research information and risk assessment capabilities.
Google has released its own competitor to ChatGPT, Bard. The company has been testing new uses for generative AI with its campaign creation tools and in efforts to make paid search ads more relevant to queries. Google introduced automatically created assets (ACAs) that analyze a landing page and ads to produce fresh headlines and descriptors for search. Google in April acquired the London-based AI and ML research lab DeepMind with plans to take on the competitive threat posed by OpenAI and ChatGPT.
The multinational retail corporation Walmart has been making forays into using generative AI for internal use with employees. The company has also been advancing conversational AI capabilities using OpenAI’s GPT-4. Walmart is using GPT-4 to go further in natural language understanding. This includes boosting existing offerings like text to shop, which allows customers to add Walmart products to their cart by texting or speaking the names of the items they need. Recently Walmart announced its new Generative AI Playground, a platform the company describes as an “early-stage internal GenAI tool where associates can explore and learn about this new technology, while keeping our company and its data safe.”
Salesforce has launched Einstein GPT, generative AI CRM technology that delivers AI-created content across every sales, service, marketing, commerce and IT interaction at hyper-scale. The company also recently introduced new generative AI workflow tools for Sales Cloud and Service Cloud to simplify workflow and customer engagement for sales and service teams respectively. Recently, Salesforce also announced the launch of AI Cloud, an enterprise AI solution aimed at boosting productivity across all Salesforce applications. The new open platform integrates various Salesforce technologies like Einstein, Data Cloud, Tableau, Flow and MuleSoft, offering real-time generative AI capabilities that can be easily incorporated into business operations.
Most Promising Generative AI Startup
This award will go to the most promising startup that has developed an innovative generative AI application and demonstrated high growth potential.
Midjourney
Midjourney is an independent research lab. Midjourney generates images from natural language descriptions called “prompts” similar to OpenAI’s DALL·E and Stable Diffusion. Midjourney stands out because the AI bot can only be accessed via the voice-over internet protocol, instant messaging social platform Discord — rather than via its own website or mobile app.
MosaicML, a generative AI startup, was recently acquired by Databricks. MosaicML is known for its generative AI capabilities and its ability to generate images from natural language descriptions (“prompts”), similar to Midjourney, OpenAI’s DALL·E and Stable Diffusion. The company provides software tools geared to making AI work cheaper. MosaicML is also working on improving neural network algorithmic methods that deliver speed, boost quality and reduce costs for enterprises.
Typeface is a generative AI application for creating enterprise content. The company helps enterprises create content at scale using AI-generated text and images, with ML training that has been customized to an organization’s content. Recognizing the limitations of generalized LLMs in meeting specific brands’ requirements, the company seeks to bridge the gap. The company recently announced it has raised $100 million in new funding to help expand its go-to-market efforts. Earlier in June Typeface expanded its customized generative AI approach with a Google Cloud partnership. The company has also added partnerships with Microsoft and Salesforce recently, further expanding its reach.
Runway is a generative AI startup focused on bringing state-of-the-art multimodal AI systems to the market with its innovative text-to-image video tool, Gen-2. The company offers a platform for artists to use ML tools in intuitive ways without coding experience for media such as video, audio and text. Runway’s tool was used by one of the film editors for the Oscar-winning film “Everything Everywhere All at Once.” The company recently announced a fresh round of funding, adding $141 million in a series C from Google, Nvidia and Salesforce Ventures, among other investors.
Synthesis AI
Synthesis AI is a startup specializing in the use of synthetic data to build more capable and ethical computer vision models. It offers a data generation platform designed to train sophisticated vision models. The platform brings together generative AI models and evolves technologies from the CGI world with an expanded set of pixel-labels, allowing users to build newer and better models. The company recently announced that it has developed a new way to create realistic 3D digital humans from text prompts, which can be used for various applications such as gaming, virtual reality, film and simulation.
Generative AI Visionary
This award will go to an individual who has made significant contributions to the field of generative AI through their thought leadership, research, or work building foundational technologies. The winner would be judged based on the novelty and influence of their contributions, as evidenced by publications, patents, or products developed.
Jensen Huang
Huang is the cofounder and CEO of Nvidia.
Ilya Sutskever
Sutskever is the cofounder and chief scientist of OpenAI.
Jacob Devlin
Devlin is a top Google AI researcher who resigned after he warned Alphabet CEO Sundar Pichai and other top executives that Bard was allegedly being trained on data from OpenAI’s chatbot. He was the lead author of a 2018 research paper on training machine learning models for search accuracy that helped initiate the AI boom.
Dario Amodei
Amodei is the cofounder and CEO of Anthropic.
Ian Goodfellow
Goodfellow is an American computer scientist, engineer and executive, most noted for his work on artificial neural networks and deep learning. He was previously employed as a research scientist at Google Brain and director of machine learning at Apple, and has made several important contributions to the field of deep learning, including the invention of the generative adversarial network (GAN).
Karén Simonyan
Simonyan is the cofounder and chief scientist of Inflection AI. He is a leading researcher on deep learning.
Generative AI Diversity and Inclusion
This award will recognize the company, organization or individual that has done the most to promote diversity and inclusion in the generative AI field. This could include advancing AI ethics, making AI technologies more accessible, providing opportunities and support for underrepresented groups, or using AI in a way that reduces bias and promotes social justice.
Sara Hooker
Hooker is head of Cohere for AI, a nonprofit research lab aiming to solve complex ML problems. She is also the founder of Delta Analytics, a nonprofit organization that provides data science education and consulting to social impact organizations. Hooker has been researching and developing generative AI models that are more efficient, robust, interpretable and fair. Hooker has also been advocating for diversity and inclusion in the generative AI field through her mentorship, teaching and outreach activities.
Timnit Gebru
Gebru is a former co-leader of Google’s Ethical Artificial Intelligence Team and a cofounder of Black in AI, a nonprofit organization that aims to increase the presence and inclusion of Black people in the field of artificial intelligence. Gebru has been conducting groundbreaking research on generative AI and its social implications, such as exposing bias in facial recognition systems, developing methods for auditing large-scale language models, and proposing frameworks for data sheets and model cards to document datasets and models. Gebru has also been speaking out against the harms and risks of generative AI, such as misinformation, discrimination and censorship.
Bars Juhasz
Juhasz is a cofounder, AI programmer and designer at Undetectable AI, an AI writing tool. Juhasz has been using generative AI to create jobs for people who are disadvantaged, such as refugees, immigrants and people with disabilities. He has also been making generative AI more accessible and inclusive by providing a low-cost and easy-to-use platform that helps users create content in various languages and domains.
Jean-Michel Caye
Caye is a senior partner at BCG and a co-author of the book Generative Leadership: The New Way of Leading. Caye has been promoting diversity and inclusion in the generative AI field by providing tools and frameworks for leaders to harness the power of generative AI for positive impact. Caye has also been supporting initiatives such as the Global AI Action Alliance (GAIA) and the Centre for the New Economy and Society to advance AI ethics and governance.
Fei-Fei Li
Li is the Sequoia Professor of Computer Science at Stanford University and co-director of Stanford’s Human-Centered AI Institute. Li has been leading the development of cognitively inspired AI, machine learning, deep learning, computer vision and AI+healthcare, especially ambient intelligent systems for healthcare delivery. Li has also been a national leading voice for advocating diversity in STEM and AI. She is cofounder and chairperson of the national non-profit AI4ALL aimed at increasing inclusion and diversity in AI education. She has also been working with policymakers nationally and locally to ensure the responsible use of generative AI, such as testifying before Congress, serving on the California Future of Work Commission, and being a member of the National Artificial Intelligence Research Resource Task Force.
Renee Cummings
Cummings is the first data activist-in-residence at the University of Virginia’s School of Data Science where was named professor of practice in data science. She is also an AI ethicist, criminologist, Columbia University community scholar and founder of Urban AI. Cummings has been using generative AI to address social justice issues, such as reducing bias in criminal justice and policing. She has also been educating and empowering underrepresented groups in the AI field, such as women and people of color, through her mentorship, teaching and advocacy.
Generative AI Open Source Contribution
This award highlights the person, team or company that has made the most significant contribution to open-source tools, datasets or other resources to help advance generative AI.
Hugging Face
Hugging Face is an AI company that enables AI and data science professionals to communicate with each other with a tool specifically designed for them. It makes publishing datasets and AI models, both trained and untrained, easier. The company is known for its open-source contributions to the ML community and for natural language processing (NLP) and computer vision (CV) applications.
Stability AI
Stability AI is a company that has developed open-source AI models for imaging, language, code, audio, video 3D content, design, biotech and scientific research. Stability AI recently released StableStudio, an open-source version of DreamStudio, its commercial interface for generating Stable Diffusion images. The company said the launch “marks a fresh chapter” for the interface and “showcases Stability AI’s dedication to advancing open-source development within the AI ecosystem.”
TensorFlow is an open-source technology effort, led by Google, that provides ML tools to help developers build and train models. TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for ML applications such as neural networks. It was developed by the Google Brain team and is used by several other Google teams and researchers for machine learning and deep learning research. Google recently rolled out a series of open-source updates for growing the TensorFlow ecosystem.
PyTorch is an open-source ML library based on the Torch library. It is used for applications such as NLP. It was developed by Facebook’s artificial intelligence research group and is used by several other Facebook teams and researchers for machine learning research.
Meta-LLaMA
Meta has publicly released Large Language Model Meta AI (LLaMA), an LLM designed to help researchers advance their work in this subfield of AI. Smaller models like LLaMA require far less computing power and resources to test new approaches, validate others’ work and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning a variety of tasks.
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