Vita B. Tolstikova
Articles. Ai Deep Learning
#Leadership #MBA #Leaders #Management #Partnership
#Aipassion
Articles. Lectures
How Ai works| Deep learning
#1. Focusing on #prompts and #data.
2. How #LLMs are trained (the “data” part)
The training has two main phases:
- Pre-training (the main learning phase):
- Fine-tuning (making it helpful and safe):
3. How prompts work (this is where the magic happens)
4, Summary:
- Data → Gives the model knowledge and language skills (during training)
- Prompt → Tells the model how to use that knowledge right now
- The model itself has no real understanding — just extremely good pattern matching
The bigger the model + better the data + smarter the prompt = smarter answers.
Fun fact: Grok 4 (#XAi) was trained on way more data and compute than earlier models, which is why Grok can handle longer contexts, better reasoning, and more complex tasks.
What is generative Ai| Deep learning
How generative Ai works| Deep learninig
How non gen Ai works| Deep learning
Ai Investment Consult | Deep learning
AI for #investments.
AI for #consult.
Human #experts in specific fields.
Expert Systems. Decision-making #abilities.
Investment consult #poweredAi for expert assessments.
#EAS powered Ai
Ai for Health
#AI in #healthcare is transforming the field by enhancing #diagnostics, #treatment, and #operational efficiency. Here’s a concise overview based on current trends and insights:
- Diagnostics and Imaging: AI algorithms, particularly deep learning models, analyze medical images (e.g., X-rays, MRIs, CT scans) to detect conditions like cancer, cardiovascular diseases, or neurological disorders with high accuracy.
- Predictive Analytics: AI uses patient data (e.g., electronic health records) to predict disease risks, such as heart attacks or diabetes, enabling preventive care. Machine learning models identify patterns in data to flag high-risk patients for early intervention.
- Personalized Medicine: AI tailor’s treatments to individual patients by analyzing genetic, lifestyle, and environmental data. For instance, AI helps oncologists select targeted cancer therapies based on a patient’s genetic profile.
- Drug Discovery: AI accelerates drug development by simulating molecular interactions and predicting drug efficacy. Companies like DeepMind and Insilico Medicine use AI to identify potential drug candidates, reducing development time and costs.
- Virtual Health Assistants and Chatbots: AI-powered tools provide 24/7 patient support, answering queries, triaging symptoms, and guiding patients to appropriate care. These systems improve access and reduce strain on healthcare providers.
- Administrative Efficiency
Mental Health.
Wearables and Remote Monitoring
#Decision making with #Ai
Decision making is the process of choosing a course of action from multiple options to achieve a desired outcome. It involves identifying a problem, gathering relevant information, evaluating alternatives, and selecting the best option based on criteria like goals, values, and constraints.
Key Components:
1. Problem
2. Identification.
3. Information gathering.
4, Option Generation.
5. Evaluation.
6, Choice.
7. Implementation.
8. Reflection.
Types of Decision Making.
1. Rational: Systematic.
2. Intuitive.
3. Collaborative.
4. Heuristic.
5. Factors Influencing Decisions.
6. Cognitive Biases.
7. Emotions.
8. Time Constraint
9. Information Overload.
10.Values and Goals: Personal or organizational priorities shape the process.
Models and Frameworks
Rational Decision-Making Model: Structured steps (define problem, evaluate options, choose, act).
Bounded Rationality: Acknowledges limits in time, info, or cognitive capacity, leading to “satisficing” (choosing a good-enough option).
Prospect Theory: People value gains and losses differently, often being risk-averse for gains and risk-seeking for losses.
SWOT Analysis: Evaluates Strengths, Weaknesses, Opportunities, and Threats for strategic decisions.
Decision making with AI involves using artificial intelligence to enhance, augment, or automate the process of selecting a course of action. AI leverages its ability to process vast amounts of data, identify patterns, predict outcomes, and optimize choices, making it valuable across fields like business, healthcare, finance, and policy. However, it also introduces challenges like biases, ethical concerns, and the need for human oversight.
More details in the video
@gen by #Grok3