Cognition Labs AI Software Engineer: Diffusion Design using Ideogram 1.0

Introducing Devin – The First AI Software Engineer by Cognition Labs

Introduction

Cognition Labs has unveiled Devin, a groundbreaking AI software engineer, marking a significant advancement in AI’s application in software development. Devin is designed to function as a fully autonomous engineering teammate, capable of executing complex engineering tasks with precision and efficiency.

Devin’s Capabilities

  • Autonomous Engineering: Devin can independently plan and execute intricate engineering tasks, making thousands of decisions accurately, recalling relevant context, learning over time, and correcting mistakes.
  • Developer Tools Integration: Equipped with common developer tools within a sandboxed compute environment, Devin operates similarly to a human engineer, using a shell, code editor, and browser to perform tasks.
  • Real-Time Collaboration: Devin actively collaborates with users, reporting progress in real-time, accepting feedback, and assisting in design decisions as needed.

Performance Highlights

  • Learning and Adaptability: Devin can quickly learn to use new technologies, demonstrated by running ControlNet on Modal to produce images with concealed messages and building and deploying interactive websites.
  • Debugging and Development: It autonomously finds and fixes bugs in codebases, contributes to mature production repositories, and can even train and fine-tune AI models.
  • SWE-bench Benchmark: Devin has set a new state of the art on the SWE-bench coding benchmark, resolving 13.86% of real-world GitHub issues end-to-end, a significant leap from the previous best of 1.96%.

About Cognition Labs

Cognition Labs, an applied AI lab focusing on reasoning, aims to build AI teammates that surpass today’s AI capabilities. With a vision to unlock new possibilities across various disciplines, Cognition Labs is at the forefront of developing AI that can reason and learn, starting with code as its initial application area.

Early Access and Future Plans

  • Devin is currently available in early access as Cognition Labs ramps up capacity. Interested parties are encouraged to reach out for an opportunity to utilize Devin for engineering work.
  • A detailed technical report on Devin’s development and performance is forthcoming, promising to provide deeper insights into the technological advancements that underpin Devin’s capabilities.

Conclusion

Devin represents a paradigm shift in software engineering, offering a glimpse into a future where AI can autonomously tackle complex engineering challenges. By enabling engineers to focus on more significant problems and empowering teams to achieve ambitious goals, Devin is poised to revolutionize the software development landscape.

Other AI News

  • Elon Musk’s xAI to Open Source Grok Chatbot, Challenging OpenAI’s Closed-Source Approach

Elon Musk’s AI startup, xAI, is set to open source its chatbot Grok, positioning it as a competitor to ChatGPT, later this week. This announcement comes shortly after Musk initiated legal action against OpenAI, accusing the organization of straying from its original open-source ethos. Grok, which was launched last year to xAI’s premium subscribers, is distinguished by its ability to access real-time information and its disregard for politically correct constraints, available at a subscription fee of $16 per month. Musk’s move to open source Grok aligns with his long-standing advocacy for open-source projects, as evidenced by Tesla’s history of open-sourcing its patents and X (formerly Twitter) open-sourcing some of its algorithms.

The lawsuit against OpenAI has sparked a wider debate within the tech community regarding the value and implications of open-source AI. Critics and supporters alike have weighed in, with notable figures like Vinod Khosla and Marc Andreessen expressing divergent views on the matter. Musk’s legal challenge underscores his critique of OpenAI’s partnership with Microsoft, alleging that it has transformed OpenAI into a closed-source entity primarily focused on profit. By open-sourcing Grok, Musk aims to contribute to the growing list of companies, including Meta and Mistral, that have made their chatbot codes publicly available, reinforcing his commitment to the open-source movement.

  • Reddit’s IPO Valuation Leverages AI Data Licensing for Competitive Edge

Reddit has announced its initial public offering (IPO) price range of $31 to $34 per share, according to a recent S-1 filing. This pricing strategy values the company between $4.93 billion and $5.4 billion, potentially reaching or exceeding $6 billion when including options held by employees and others. This valuation is based on an expected 158.98 million shares outstanding. The company’s IPO plans have drawn significant attention, with its valuation anticipated to be at or above the $5 billion mark, a figure that has been a point of interest given Reddit’s secondary-market trading activity prior to the IPO filing. With $804.0 million in revenue for 2023, Reddit’s valuation multiples range from 6.9x to 8x its revenue, which could increase if investors are willing to pay more than the $34-per-share high-end range after the company’s roadshow.

A key factor in Reddit’s ambitious pricing is its strategy around artificial intelligence (AI), which differentiates it from other social media companies. Reddit has already generated $203 million from licensing its data to AI companies, with contracts allowing third-party access to its vast and constantly growing data pool. This revenue is expected to contribute significantly to its financials, with at least $66.4 million recognized in the year ending December 31, 2024. Reddit’s rich data, derived from its extensive user interactions and content creation, is highly valuable for training large language models (LLMs), making it an attractive asset for AI companies. Despite some user concerns about AI, Reddit’s ability to monetize its data positions it as a potential side bet on the AI industry itself, suggesting a promising future for the company.

  • Nijta: Pioneering Voice Anonymization for AI Privacy Compliance in Europe

Nijta, a French startup based in Lille, is addressing the growing concern over voice privacy in the era of artificial intelligence (AI) with its AI-powered speech anonymization technology. Recognizing that voice recordings can reveal personally identifiable information, including emotional state and potential health issues, Nijta aims to assist companies in complying with stringent privacy laws like Europe’s GDPR. This is particularly relevant as many organizations seek to leverage voice data for AI development but must first strip away biometric details to protect individual privacy. Founded by Indian CEO Brij Srivastava, Nijta emerged from the Inria Startup Studio program and has secured €2 million in funding from sources including Elaia and Finovam Gestion. The startup focuses on the European market due to the robust data privacy regulations enforced by GDPR.

Nijta’s technology finds applications across various sectors, notably in call centers handling health data and educational technology where children’s voices need anonymization. One of its early projects, OkyDoky, aimed at improving medical emergency call handling, underscores the necessity of voice anonymization in sensitive AI applications. The startup also emphasizes the security of its content through watermarking and claims its voice protection is irreversible, addressing the inadequacies of some media outlets’ attempts at anonymizing voices. While initially focusing on B2B clients in Europe to mitigate the risk of GDPR fines, Nijta plans to expand into B2C markets, exploring real-time anonymization for secure communication. Despite its small team, Nijta is supported by Business France and the Hauts-de-France region, facilitating its international expansion efforts and reinforcing its commitment to voice privacy in AI use cases.

  • Applied Intuition Secures $250M Series E, Valued at $6B for AI-Driven Autonomous Vehicle Tech

Applied Intuition, a company specializing in autonomous vehicle software, has successfully raised $250 million in a Series E funding round, bringing its valuation to $6 billion. This significant investment underscores the growing interest in artificial intelligence (AI) applications across various sectors, including automotive, defense, construction, and agriculture. The funding round was led by Lux Capital, investor Elad Gil, and Porsche Investments Management, with participation from Andreessen Horowitz, Bond, and Formula 1 world champion Nico Rosberg, among others. This influx of capital is earmarked for the company’s most ambitious projects, aiming to advance without compromising its culture.

Founded in 2017, Applied Intuition offers software solutions that aid in the development of autonomous vehicles (AVs) by providing simulations for testing perception and vehicle behavior systems, as well as managing the extensive data involved in AV development. The company’s CEO, Qasar Younis, emphasizes their goal to be the first call for companies facing software or AI challenges in the AV space. Applied Intuition boasts collaborations with 18 of the top 20 automakers, including General Motors, Toyota, and Volkswagen, as well as partnerships with autonomous vehicle startups and defense contracts. This new funding round arrives amid heightened scrutiny of autonomous vehicle development but reflects the undiminished enthusiasm for AI’s potential to revolutionize vehicle production and safety.

  • Tavus Secures $18M for AI-Driven Personalized Video Creation, Expanding Face and Voice Cloning Capabilities

Tavus, a generative AI startup, has secured $18 million in Series A funding to expand its technology that enables companies to create digital replicas of individuals for personalized video campaigns. This round was led by Scale Venture Partners, with contributions from Sequoia, Y Combinator (YC), and HubSpot. Tavus’ technology allows for the cloning of voices and faces, offering a scalable solution for sales and marketing teams to send personalized videos to prospects or for product teams to create customized onboarding videos. The startup is now opening its platform to third-party integrations, allowing companies to automate personalized video creation by connecting Tavus with systems like Salesforce or Mailchimp.

Tavus distinguishes itself in the generative AI space by focusing on video, a medium poised for significant growth with advancements in AI technology. The company works with notable clients, including Salesforce and Meta, to enhance B2B customer engagement through personalized demo videos. Tavus operates as a SaaS application, where customers can create AI video templates. The process involves recording a short video, which is then used to train the AI for creating personalized content. With the new funding, Tavus aims to further develop its technology, including a new model called “Phoenix” for creating more realistic digital replicas from minimal training data. This development underscores the potential for generative AI to revolutionize video content creation, despite the challenges of ensuring ethical use and preventing misuse in the creation of deepfakes.

  • Pienso Raises $10M Series A to Democratize AI Model Training with No-Code Platform

Pienso, a platform developed to simplify AI deployment by enabling users to build and deploy models without coding, is gaining attention in the tech industry. Founded in 2016 by MIT alumni Birago Jones and Karthik Dinakar, Pienso emerged from the founders’ research efforts to create tools that would allow subject-matter experts to effectively train AI models. This initiative was sparked by a project aimed at moderating social media content, which highlighted the importance of training AI with accurately labeled data. Pienso is designed for non-technical professionals such as researchers, marketers, and customer support teams, providing them with the tools to structure and analyze large datasets for AI training.

Pienso’s platform guides users through the process of annotating training data for both open source and custom AI models, offering a no-code interface that can be deployed in the cloud or on-premises. It integrates with enterprise systems through APIs but can also function independently to ensure data security. Notable users include Sky, the U.K. broadcaster, and an unnamed U.S. government agency. Pienso operates on a yearly licensing model based on the number of AI models deployed, encouraging experimentation and adaptation to specific business needs. Having raised $10 million in a Series A funding round, bringing its total funding to $17 million, Pienso plans to expand its sales, marketing, and customer success teams, recruit engineering talent, and develop new platform features. This approach to democratizing AI, by empowering domain experts to leverage their data, positions Pienso as a promising tool for building smarter, application-specific AI models.

  • Deepgram Launches Aura: Real-Time Text-to-Speech API for Conversational AI Agents

Deepgram, known for its expertise in voice recognition, has introduced Aura, a new real-time text-to-speech API designed to empower developers to create conversational AI agents. These agents, supported by large language models (LLMs), are intended to function as customer service representatives in various customer interaction scenarios. Aura distinguishes itself by offering highly realistic voice models that operate with exceptionally low latency, achieving human-like voice outputs in less than half a second, all at a competitive price point.

Scott Stephenson, co-founder and CEO of Deepgram, emphasized the challenge of balancing high-quality voice models with the need for rapid response times and cost-effectiveness. Aura aims to meet these demands by providing accuracy, low latency, and affordability, which are crucial for businesses, especially considering the costs associated with accessing LLMs. With pricing set at $0.015 per 1,000 characters, Aura is positioned as a cost-effective solution compared to similar offerings from Google and Amazon. The platform currently offers around a dozen voice models, all developed in-house and trained with datasets created in collaboration with voice actors. Deepgram’s focus on building a robust underlying infrastructure over four years has culminated in the release of Aura, showcasing the company’s commitment to delivering high-speed, accurate, and affordable voice AI solutions.

  • Empathy Raises $47M to Enhance Bereavement Support with AI and Human Guidance

Empathy, a startup offering a platform to assist individuals through the bereavement process, has secured $47 million in Series B funding to expand its services. The platform, which serves around 40 million users, combines AI and human guidance to help users manage the numerous tasks associated with the death of a loved one, from funeral arrangements to financial settlements. The funding round was led by Index Ventures, with participation from several major insurance companies, including MassMutual Ventures, MetLife, New York Life, Securian, and Sumitomo. Empathy primarily operates through a B2B2C model, offering its services via employers or insurers, which account for 99% of its business.

Empathy’s platform provides a mix of counseling services, AI for writing obituaries, and tools to automate the closure of the deceased’s online accounts and manage complex financial affairs. The company plans to use the new funding to enhance its tools and further its mission to redefine bereavement care. Founded in Israel and focusing on the U.S. market, Empathy has raised a total of $90 million to date. The company’s valuation is approaching $400 million, according to sources close to the company. With the pandemic highlighting the importance of bereavement services, Empathy aims to leverage AI to streamline the practical aspects of bereavement, maintaining a human team for emotional support while using technology to handle organizational tasks more efficiently.

  • UG Labs Secures $7M to Bring Conversational AI to Children’s Gaming

UG Labs, based in Tel Aviv, Israel, has successfully raised $7 million in funding to integrate conversational AI and voice interactivity into children’s games. This funding round, led by Amiti Venture and MoreVC, with contributions from Mediatek and private investors, aims to revolutionize game design by incorporating proprietary algorithms and data for voice interactivity and conversational AI. UG Labs’ technology, embedded within its game engine, facilitates open-ended conversations with non-player characters, dynamically influencing gameplay and offering a more immersive and engaging experience for young users and their families.

The core of UG Labs’ innovation lies in its automated speech recognition (ASR) and large language models (LLMs), designed to improve over time and become more adept at understanding speech, particularly in children whose speech patterns and language command are still developing. This personalized approach to speech recognition represents a significant advancement in inclusivity, ensuring that children who are typically less understood by conventional models will now have a better interaction experience. By focusing on narrative safety and understanding the nuances of children’s conversations, UG Labs is setting a new standard in creating safe, engaging, and interactive gaming experiences for kids.

  • DoorDash Enhances Platform Safety with AI-Driven ‘SafeChat+’ to Combat Verbal Abuse

DoorDash has introduced “SafeChat+,” an AI-powered feature designed to mitigate verbal abuse and inappropriate interactions within its platform. This new tool automatically screens in-app conversations between customers and delivery personnel (Dashers) for offensive language, offering mechanisms for reporting and addressing incidents of harassment. SafeChat+ enables customers to report abuse and contact DoorDash’s support team, while Dashers can cancel orders without affecting their ratings if they encounter abuse. The AI system sends warnings to users who use inappropriate language, analyzing over 1,400 messages per minute across multiple languages, including English, French, Spanish, Portuguese, and Mandarin. All incidents identified by the AI are investigated by DoorDash team members.

This feature builds on the existing SafeChat tool, enhancing DoorDash’s Trust & Safety team’s capabilities to detect subtle nuances and threats beyond specific keywords. DoorDash aims to significantly reduce the number of safety incidents on its platform with SafeChat+, noting that verbal abuse or harassment is the most common type of safety incident. The company reports that over 99.99% of deliveries are completed without safety-related incidents. Additionally, DoorDash offers “SafeDash,” an in-app security toolkit connecting Dashers with ADT agents who can assist in emergencies by sharing location and other relevant information with 911 services.

  • Locus Robotics: Mastering Warehouse Automation with a Software-First Approach

Locus Robotics, known for its autonomous mobile robots (AMRs) that are a staple in warehouses, emphasizes its identity as a software company at heart, according to CEO Rick Faulk. Despite the hardware’s visibility, it’s the software, particularly the fleet management software and the newly announced LocusHub Engine, that Faulk believes truly distinguishes Locus in the competitive field of warehouse automation. LocusHub Engine, unveiled at the Modex supply chain show, aims to optimize warehouse operations by analyzing data collected by the robots to predict and improve workflow efficiency.

The company’s journey began as a response to Amazon’s acquisition of Kiva Systems, which left many companies, including Quiet Logistics (Locus’s predecessor), seeking alternative robotic solutions for warehouse automation. Locus has since established itself as a market leader, not by diversifying its product line but by refining and expanding upon its core offering of tote-transporting AMRs. This focus has allowed Locus to thrive, especially during the pandemic when the demand for warehouse automation surged. Despite a recent minor staff reduction, Locus continues to invest in research and development, exploring technologies to further automate warehouse operations without immediately jumping on trends like humanoid robots, which Faulk views as years away from practical application.

Locus Robotics’ success story underscores the importance of focusing on what works and meeting the evolving needs of its clients. By concentrating on enhancing its software capabilities and gradually improving its robotic fleet, Locus has managed to stay ahead in a rapidly growing and changing industry.

  • Apple’s Multimodal AI Breakthroughs Signal Major Advances in Integrating Text and Image Processing

Apple researchers have unveiled significant advancements in multimodal artificial intelligence (AI), focusing on training large language models (LLMs) to understand both text and images. Detailed in their research paper, “MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training,” these breakthroughs enable the creation of more powerful and flexible AI systems. By integrating a diverse mix of data, including image-caption pairs, interleaved image-text data, and text-only information, the MM1 models have achieved state-of-the-art performance in various AI tasks such as image captioning, visual question answering, and natural language inference. This development underscores the importance of combining different types of training data and model architectures to enhance AI capabilities.

Amidst increasing investments in AI, Apple is ramping up its efforts to integrate generative AI technologies into its products and services, including Siri, Messages, and Apple Music. The company is reportedly allocating $1 billion annually towards AI development, with projects like the “Ajax” large language model framework and an internal chatbot known as “Apple GPT” in the works. These initiatives are part of Apple’s broader strategy to stay competitive in the fast-evolving AI landscape, highlighting the integration of AI and machine learning as core components across its product range. With the tech giant traditionally following rather than leading major technology shifts, its latest research in multimodal AI signals a strong commitment to shaping the future of AI and maintaining its competitive edge in the industry.

  • OpenAI-Enhanced Figure Robot Demonstrates Human-Like Chores in Breakthrough Demo

Figure, a robotics startup valued at $2.6 billion and founded by alumni from Boston Dynamics, Tesla, Google DeepMind, and Archer Aviation, has unveiled its first project in collaboration with OpenAI, the creators of ChatGPT. The demonstration showcased Figure’s humanoid robot, Figure 01, performing tasks such as handing an apple to a human, picking up trash, and putting away dishes. This demonstration of the robot’s ability to interact with humans and its environment, understand commands, and execute tasks autonomously marks a significant advancement in the field of robotics. The robot’s actions are powered by a large vision-language model (VLM) trained by OpenAI, although specifics about whether this is a new model or a version of GPT-4 were not disclosed.

The demonstration video, which was not sped up or controlled remotely, highlights the robot’s smooth interaction and task execution, suggesting a leap forward in humanoid, general-purpose robotics. Figure’s co-founder and CEO, Brett Adcock, has ambitious plans for the company, aiming to operate humanoid robots at the billion-unit level and develop robots that can eliminate the need for unsafe and undesirable jobs. Despite acknowledging the high risk and low chances of success, Adcock’s vision for Figure is to positively impact humanity and improve life for future generations, explicitly stating that the robots will not be used in military or defense applications or in roles that require inflicting harm on humans.

  • Pika Revolutionizes AI Video Creation with New Generative Sound Effects Feature

Pika, a leading player in the video AI space, has introduced a groundbreaking feature on its web platform, allowing users to automatically generate sound effects for their AI-created videos. This enhancement significantly enriches the user experience by adding a new dimension to AI-generated videos, which previously lacked sound unless manually added by the user through external editing software. Now, with Pika’s latest update, users can seamlessly integrate sound effects directly within the app, eliminating the need to source audio files separately. This feature, combined with Pika’s recent introduction of lip-syncing capabilities, positions the platform as one of the first major all-in-one generative AI video creation tools, enabling users to produce videos with AI-generated sound effects, voiceovers, and visuals all in one place.

Pika’s innovative approach to video creation simplifies the filmmaking process, potentially reducing the need for traditional roles such as cinematographers, videographers, and sound designers. By allowing users to input prompts and generate comprehensive audiovisual content directly from their imagination, Pika streamlines content creation and opens up new possibilities for individual creators and enterprises alike. Currently, this capability is available to participants in Pika’s super-collaborators program or those subscribed to its Pro plan at $58/month, with plans to eventually extend access to all users. As Pika continues to evolve and expand its offerings, it stands out in the competitive AI video space, challenging other platforms by providing an integrated solution for generating both video and sound content.

  • Snowflake Partners with Landing AI to Enhance Computer Vision in Data Cloud

Snowflake, the data warehousing giant, has announced a strategic partnership and investment with Landing AI, a computer vision startup founded by AI pioneer Andrew Ng. This collaboration aims to integrate Landing AI’s advanced computer vision technology into Snowflake’s Data Cloud, offering new opportunities for businesses to leverage the vast amounts of unstructured visual data that make up a significant portion of the world’s data. The partnership is poised to benefit industries ranging from manufacturing and retail to healthcare and finance, by incorporating state-of-the-art computer vision capabilities within Snowflake’s secure and governed data ecosystem.

The integration of Landing AI’s platform with Snowflake’s Data Cloud will enable users to connect image data stored in Snowflake, create computer vision models, and deploy AI models either within Snowflake Container Services or to edge devices. This collaboration not only enhances the utility of visual data but also addresses the challenges associated with training accurate computer vision models using limited datasets. By leveraging Snowflake’s robust data security and governance alongside Landing AI’s innovative technology, the partnership is set to transform how enterprises across various sectors utilize visual data to drive innovation, efficiency, and growth.

  • Databricks Partners with Mistral to Enhance AI Model Integration on Its Data Platform

Databricks, a leading data infrastructure company, has announced a strategic partnership and investment in Mistral, a Paris-based startup known for its high-performance large language models (LLMs), many of which are open-sourced. This collaboration aims to bring select Mistral LLMs to Databricks’ data intelligence platform, facilitating direct integration that allows enterprise users to leverage these models with their data for generative AI applications. This integration promises to maintain the security, privacy, and governance standards of the Databricks platform, offering a seamless experience for users looking to harness the power of generative AI without compromising on data integrity.

The partnership marks a significant step in Mistral’s journey, adding Databricks to its list of notable distribution partners, which already includes Snowflake and Microsoft. By integrating Mistral’s text-generation models, specifically the Mistral 7B and Mixtral 8x7B, into its platform, Databricks enhances its offerings, enabling users to experiment with and deploy optimized model endpoints or customize them for specific use cases. This move not only strengthens Databricks’ position in the AI and data analytics space but also underscores the growing importance of collaborative efforts in advancing AI technology and making it accessible to a broader range of industries and applications.

  • Apple Bolsters Manufacturing and AI Capabilities with DarwinAI Acquisition

Apple has recently acquired DarwinAI, a Canadian AI startup known for its vision-based technology aimed at improving manufacturing efficiency by monitoring components. This acquisition, reported by Bloomberg, has not been officially announced by either company, but several DarwinAI team members have transitioned to Apple’s machine learning teams, as indicated by their LinkedIn profiles. DarwinAI, which had raised over $15 million from investors including BDC Capital’s Deep Tech Venture Fund, Honeywell Ventures, Obvious Ventures, and Inovia Capital, specializes in making AI models smaller and faster, a capability that could enhance on-device generative AI features Apple plans to introduce.

This strategic acquisition aligns with Apple’s broader efforts to integrate generative AI (GenAI) features into its products, as the tech giant seeks to catch up with competitors like OpenAI, Google, Meta, and Microsoft. Tim Cook, Apple’s CEO, has previously indicated plans to unveil GenAI-powered features “later this year,” emphasizing the company’s investment in generative AI efforts. The addition of DarwinAI’s expertise could play a crucial role in Apple’s push to infuse AI across various products and services, including Siri, developer tools, and customer support, marking a significant step in Apple’s ongoing work in the AI space.

  • Cohere Unveils ‘Command-R’ Language Model, Targeting Enterprise AI Applications

Cohere, an artificial intelligence startup, has launched a new language model named Command-R, marking a significant advancement in the company’s technology offerings. This release comes as Cohere is engaged in a fundraising round that could potentially bring in up to $1 billion in fresh capital. Command-R is designed to excel in retrieval augmented generation (RAG) and tool use, featuring longer context windows of up to 128,000 tokens and offering more affordable pricing options. According to Cohere President & COO Martin Kon, Command-R is optimized for large-scale production workloads and is particularly effective when used in conjunction with Cohere’s Embed and Rerank models, facilitating enterprises in moving beyond the proof of concept stage.

The introduction of Command-R is timely, positioning Cohere in direct competition with other AI startups like OpenAI and Anthropic. Founded in 2019 by ex-Google researchers, Cohere has focused on developing powerful language models tailored for enterprise applications, differentiating itself by working closely with business customers to meet their specific needs. This strategic focus has allowed Cohere to operate more cost-efficiently compared to competitors targeting broader consumer applications. With the launch of Command-R, Cohere aims to enable customers to scale up quickly and transition into large-scale production, highlighting partnerships with notable companies such as Oracle, Notion, Scale AI, Accenture, and McKinsey.

About The Author

Bogdan Iancu

Bogdan Iancu is a seasoned entrepreneur and strategic leader with over 25 years of experience in diverse industrial and commercial fields. His passion for AI, Machine Learning, and Generative AI is underpinned by a deep understanding of advanced calculus, enabling him to leverage these technologies to drive innovation and growth. As a Non-Executive Director, Bogdan brings a wealth of experience and a unique perspective to the boardroom, contributing to robust strategic decisions. With a proven track record of assisting clients worldwide, Bogdan is committed to harnessing the power of AI to transform businesses and create sustainable growth in the digital age.