Yomi Tejumola is the Founder and CEO of Algomarketing, a company focused on helping enterprise teams integrate AI-proficient talent to drive transformation and unlock growth. With a background as a data scientist and marketing technologist, Tejumola spent over a decade working with leading global enterprises to optimize operations, automate workflows, and generate data-driven insights. Over […]
Agentic artificial intelligence (AI) represents the next frontier of AI, promising to go beyond even the capabilities of generative AI (GenAI). Unlike most GenAI systems, which rely on human prompts or oversight, agentic AI is proactive because it doesn’t require user input to solve complex, multi-step problems. By leveraging a digital ecosystem of large language
In the shifting world of artificial intelligence, one of the most profound transformations is occurring in how we search for and discover information. AI-driven platforms, especially generative AI models like OpenAI’s ChatGPT, Google’s Gemini, and Perplexity AI, are redefining what it means to be “searchable.” As these tools reshape search behavior, the ripple effects are
A groundbreaking new study from computer vision startup Voxel51 suggests that the traditional data annotation model is about to be upended. In research released today, the company reports that its new auto-labeling system achieves up to 95% of human-level accuracy while being 5,000x faster and up to 100,000x cheaper than manual labeling. The study benchmarked
“In projecting language back as the model for thought, we lose sight of the tacit embodied understanding that undergirds our intelligence.” –Terry Winograd The recent successes of generative AI models have convinced some that AGI is imminent. While these models appear to capture the essence of human intelligence, they defy even our most basic intuitions
[{“model”: “blogsurvey.survey”, “pk”: 9, “fields”: {“name”: “AA – Google AI product use – I/O”, “survey_id”: “aa-google-ai-product-use-io_250519”, “scroll_depth_trigger”: 50, “previous_survey”: null, “display_rate”: 75, “thank_message”: “Thank You!”, “thank_emoji”: “✅”, “questions”: “[{\”id\”: \”e83606c3-7746-41ea-b405-439129885ead\”, \”type\”: \”simple_question\”, \”value\”: {\”question\”: \”How often do you use Google AI tools like Gemini and NotebookLM?\”, \”responses\”: [{\”id\”: \”32ecfe11-9171-405a-a9d3-785cca201a75\”, \”type\”: \”item\”, \”value\”: \”Daily\”}, {\”id\”: \”29b253e9-e318-4677-a2b3-03364e48a6e7\”,
Are action-based preferences necessary? One of the key factors of ACT is that the contrastive pairs highlight differences between conversational actions. In “ACT w/ Random Actions”, we additionally examine the importance of action selection by randomly sampling both the winning and losing action when constructing the preference pair, and observe this underperforms normal ACT. Do
There’s a strange loop taking over social media right now. Scroll through TikTok, YouTube Live, or Instagram, and you’ll see a parade of “digital marketing experts” promoting their latest PDF guide, online course, or coaching program. What’s it about? Digital marketing. But not the kind that helps actual businesses improve performance, it’s a course on
Making these algorithms work for LLMs If we run these algorithms “out-of-the-box” for LLMs, things go badly. So, we came up with optimizations to the algorithms that fix the key issues with running them “out-of-the-box”. For ELS, we had to go from example-level DP guarantees to user-level DP guarantees. We found that previous work was
Efficient and grounded models: Contributing to AI Mode in Search As LLMs grow larger and demand increases, our ability to improve model efficiency while maintaining and even elevating their quality determines our success in democratizing access to these high-performing models. Google Research has made breakthroughs in efficiency that have become industry standards, for example, our
