Blaise K. Jacholkowski, MBA

#𝐂𝐡𝐚𝐭𝐆𝐏𝐓-4𝐨 𝐎𝐮𝐭𝐬𝐡𝐢𝐧𝐞𝐬 #𝐆𝐞𝐦𝐢𝐧𝐢-1.5 𝐏𝐫𝐨 𝐢𝐧 𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠! I just read an enlightening article comparing two AI giants, #ChatGPT-4o and #Gemini-1.5 Pro, and the results were astounding! I have taken two aspects of comparison here -Code Generation and Reasoning. #𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 In the world of AI, the ability to generate code is a significant benchmark. It’s not just about writing any code, but about writing 𝐜𝐨𝐫𝐫𝐞𝐜𝐭 𝐚𝐧𝐝 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐜𝐨𝐝𝐞. In the comparison, ChatGPT 4o was tasked with creating a Python game. Not only did it accomplish this task, but it did so within seconds, demonstrating its proficiency and speed. On the other hand, Gemini 1.5 Pro struggled with this task. It was unable to generate the correct code, indicating a gap in its coding capabilities. This difference highlights the superior code generation capabilities of ChatGPT 4o, making it a valuable tool for developers and programmers. #𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐀𝐛𝐢𝐥𝐢𝐭𝐲  Reasoning is another critical aspect of AI. It’s about understanding the context, applying logic, and arriving at a conclusion. In the ‘𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞 𝐃𝐫𝐲𝐢𝐧𝐠 𝐓𝐢𝐦𝐞’ test, both AI models were presented with a trick question. ChatGPT 4o demonstrated its superior reasoning skills by correctly answering the question. It understood the trick in the question and applied logic to arrive at the correct conclusion. In contrast, Gemini 1.5 Pro struggled with this task. It failed to understand the context of the question and arrived at the wrong conclusion. This difference underscores the advanced reasoning capabilities of ChatGPT 4o. #AI #ChatGPT4o #Gemini15Pro #CodeGeneration #Reasoning

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🔍⚖️ Practitioners Guide To Responsible AI - Part 3 Building on our previous discussions (https://lnkd.in/ekchutB4), today we delve deeper into the first layer of our Responsible AI framework: Model Training & Data Security. In the healthcare and life sciences sectors, the integrity and security of data are paramount. This layer addresses key concerns such as: 1/ Data Privacy: Ensuring patient data is protected and compliant with regulations. 2/ Bias Mitigation: Implementing comprehensive strategies to prevent bias in AI models, ensuring equitable outcomes across different demographics. 3/ Data Integrity: Maintaining the accuracy and consistency of data throughout its lifecycle. and 4/ Secure Data Handling: Safeguarding data during storage and transfer to prevent unauthorized access. So, what does this mean in practice? Let’s look at some examples: If your organization handles unique patient data, it's crucial to ensure that your AI models do not inadvertently favor specific races or genders. But when you want to do this at scale, you will need integrated tools to continuously validate, test, and audit both the data and the model through their lifecycle. Another example, when sharing data across lines of business, the data and its metadata should be self-contained and easily interpretable to prevent misinterpretation. Again, you will need tools to do this at scale – accounting for discovery, authentication and authorization. Well, it's easier said than done! How would one go about supporting these responsible AI practices at scale? AWS, for example, offers a range of integrated tools that allow for organizations to manage these issues at scale. Amazon SageMaker Clarify helps detect and mitigate bias during data preparation, model training, and deployment, providing insights into model predictions for greater transparency. Amazon DataZone facilitates governed data sharing and metadata management, ensuring data privacy and integrity across business lines. Additionally, AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS) ensure secure data handling by managing access and encryption. These tools collectively enable healthcare and life sciences organizations to implement robust data governance and responsible AI practices effectively. In my next post, I will dive deeper and share insights on the next layer of our Responsible AI framework: Model Usage, Infrastructure, and Deployment. 💬 How are you integrating Responsible AI practices and tools into your operations at scale? 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #aws #ResponsibleAI #AIinLifeSciences #HealthcareAI #AIEthics #AIGovernance #AISecurity

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🌟 📈 Organizational Change Management: Empowering Generative AI Adoption in the Business Landscape Healthcare and Life Sciences firms must focus on developing robust change management capabilities to help their organizations adapt and thrive in the face of the disruption and transformation opportunities presented by Generative AI. By educating line-of-business leaders on NLP's (Natural Language Processing) potential and partnering with technology teams, GSIs, ISVs, and top consulting firms, organizations can harness the power of generative AI to optimize business processes and maximize ROI. Key considerations for effective organizational change management in the context of generative AI adoption include: 1. 🎓 Education and Awareness: Equip line-of-business leaders with the knowledge of NLP and its impact on their respective functions, enabling them to identify opportunities and risks associated with AI-driven transformation. The current Generative AI powered by Large Language Models (LLMs) represents just one application of NLP technology. Future enhancements and innovations in this field are expected to expand beyond text, encompassing vision-based capabilities for processing images, live videos, and task based actions as well. 2. 🤝 Partnership and Collaboration: Encourage cross-functional collaboration between business leaders, technology teams, ISVs, and consulting partners to develop a comprehensive playbook and roadmap for AI adoption. By partnering with Independent Software Vendors (ISVs) that specialize in generative AI solutions for the industry, organizations can accelerate adoption and promote faster time-to-market for generative AI-driven innovations. 3. 💰 ROI and Prioritization: Large organizations often accumulate a patchwork of back-office processes over time, resulting in complex, multi-layered systems. Many of these legacy workflows are prime candidates for optimization through the application of generative AI technologies and could save organizations millions of dollars! Effective organizational change management is crucial for businesses looking to capitalize on the immense potential of generative AI. By fostering a culture of collaboration, continuous learning, and strategic alignment, organizations can pave the way for a successful and impactful AI-driven future. 💬 Share your experiences and insights on navigating the organizational change management process in the context of generative AI adoption below! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #OrganizationalChangeManagement #OCM #NLP #BusinessTransformation #GenerativeAI #HealthcareInnovation #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #lifesciences #healthcare #patientcare #ai #clinicalresearch #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess

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✅ Generative AI Adoption: A Guide to Establishing a Strong PMO Office (Part 2) 🚀   In our ongoing series on how to establish a strong Project Management Office (PMO) office for scaling Generative AI (GenAI) adoption (Part 1 - https://lnkd.in/escgxDFb), we continue to draw on real-world experiences to highlight best practices for consideration:   Align with your AI Council for prioritization and intervention. Establish a regular cadence with your AI Council to discuss project priorities, address challenges, and seek guidance on strategic decisions. This alignment will ensure your PMO remains focused on high-impact GenAI initiatives, and is not having to field requests from the larger org and managing expectations endlessly. PMO’s mandate is to deliver what has been committed. Let the AI Council, field the triaging, probing, discovery, and prioritization of the most high impact use cases for you.   From the get-go, build your communication strategy and share regular updates with your steering committee (or AI Council). Schedule periodic meetings with your steering committee to share progress updates, discuss any roadblocks, and gather feedback on your GenAI adoption journey. Develop risk management strategies and ethical guidelines to address potential challenges and ensure responsible GenAI implementation. This includes mitigating data privacy concerns, monitoring AI models for potential biases, and complying with relevant regulations. By openly communicating updates to the AI Council, the PMO will help support Responsible & Ethical AI adherence for the enterprise.   Capture and report impact to business! Regularly reporting on the business value and return on investment (ROI) achieved through GenAI pilots will help build confidence with c-suite and encourage them to scale adoption by providing additional resources and helping prioritize a AI-first mindset across the enterprise. Have your exec sponsors relay regular win notes to the larger organization and rally support for GenAI adoption. Why is this important? Because every firm will have early adopters, early followers, late adopters, and naysayers. For an organization to successfully transform, you need to bring everyone to the finish line...   Finally, foster a culture of continuous improvement & innovation, by recognizing and celebrating the contributions of team members and stakeholders who have been instrumental in driving success. I have often seen organizations get so busy in doing the work, they forget to recognize those who are doing the heavy lifting. By focusing on the people aspect of this transformation journey, you will help motivate and inspire your employees to embrace the change.   💬 Do share your GenAI experiences and learnings on setting up your PMO office in the comments below! Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #pmo #aws

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Tim Fitzpatrick these are great observations and albeit the choppy IPO market in healthcare (and a kind of parallel in early stage venture funding) I think we might look at this through a different lens as well: There are service businesses that are more clinical in nature and are especially well suited structurally for a roll-up approach, then gain scale and then move to exit. One exit is the public markets. The public markets find it easier to understand and digest a simpler story around a tangible business, a recognizable recurring service (in the #kidneyverse think “dialysis clinics” for example), and a cash flow that is organically growing due to economics, demographics and steady and dependable reimbursements (with or without VBC and in this example, tilting towards Medicare and aging demographics, with all of the accompanying government mandates and also slow-moving deep water [inertia]). The other flavor of this simple model is where Fintech and HealthTech overlap, and it is where billing, payment and rev cycle management come into play, and where again there is steady recurring revenue that is relatively immune from VBC or other structural shifts in the making (no matter the payer or provider). Other businesses in healthcare and HealthTech innovation are process improvements, involving access, equity, underserved markets, extended care, and more. These are a bit trickier as some of them are nascent, in terms of business model, unit economics and Go-to-market path. Even as these grow, there may be more hesitancy now due to the hype cycle around AI and what it may bring precisely in these areas. I have spoken to more than a few innovators who have received feedback that reflects a fear of being “featurized overnight” by one of the big tech companies, AI companies, EHR companies or others now deeply entrenched in genAI exploration for their core as well as emerging processes or businesses. This may mean that the first tranches you point out are done, and now it’s a more careful (and perhaps overanalyzed) pause or slower pace. That said, great companies abound, and I am bullish on their potential and we will eventually see great exits for them, via the public markets or acquisition. For innovators, it’s still the best time to build!!! #goodwork #innovation #genAI #healthtech #healthcare #fintech #access #equity #reimbursement #VBC

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