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![Transformative
• The model.
• Lots of jargon about dual layer, each
with 2 sub-layers, that feed on each
other…
• If you really, really, want to
know:
[1706.03762] Attention Is All You
Need (arxiv.org)](https://image.slidesharecdn.com/2023-04-20aidotned-230601130025-3d02bc48/75/A-leap-around-AI-56-2048.jpg)
Uploaded byDennis Vroegop
A leap around AI
The document discusses the historical development and current impact of artificial intelligence (AI) and machine learning (ML), emphasizing the rapid growth of these technologies and their projected market value exceeding $60 billion by 2025. It highlights the potential of AI in various applications, such as traffic safety, cancer detection, and workplace automation, alongside the concerns about job displacement. The author also outlines the foundational concepts of neural networks and provides insights into machine learning workflows and generative AI models.
A leap around AI
- 1. AI, ML.Net, GPT, Stable- Diffusion Aleap around AI with Dennis Vroegop
- 2. Don’t be afraidAI will steal your job… Be afraid the other guy/girl who knows about AI will steal it
- 3. A bit ofhistory Just adding some context
- 4. 1956 At Dartmouth Conference,John McCarthy claims the term “AI” AI is born!
- 5. 1980-1990 Expansion due togood hardware, enabled neural networks
- 6. 1990-2000 The rise ofthe internet enabled huge scale up of resources.
- 7. 2000-2010 Deep learning andmachine learning makes AI much more powerful
- 8. 2018 - Today OpenAIhugely improves communications, AI for the masses.
- 9. But… why? What arewe going to do with it?
- 10. 2 billion cameras connectedto AI • (NVIDIA said that by 2025 there will be 2 billion video cameras in cities around the world, they want to connect those to gather data about flow of traffic, crime and so on)
- 11. 25% of thecompanies will dedicate workers to oversee AI systems • (Gartner said, “AI continues to drive change in how businesses and governments interact with customers and constituents”. AI is already heavily used in spotting breast cancer, in such a way that this can be detected a year earlier than it would have been normally diagnosed)
- 12. AI Market willbe over $60 billion in 2025 • (Research company Tractica says the AI market will skyrocket from $1.4 billion in 2016 to nearly $60 billion in 2025, but markets related to AI will grow as well. For instance, Machine Learning as a Service will be worth $19.9 billion by the same year)
- 13. Smart Voice Assistantswill be in 15 billion devices in 2025. • (RBC Capital Markets predicts Alexa alone could be worth $20 billion 2025, both from sales and spending on the Amazon AI platform)
- 14. AI in driverlesscars could save 300,000 lives in America each decade • (NVIDIA, Alphabet’s Waymo and Tesla are already using computer vision and deep learning technologies to allow vehicles to both react to what they see and learn from their experiences. We’ll see a huge decline in number of traffic fatalities on the road)
- 15. 30% of companiescould have their revenues reduced • (Gartner predicts that companies that perform software services right now could become irrelevant, or a least less useful, once machines start figuring out how to do things better and faster than they can. AI will cannibalize revenues from some current market leaders)
- 16. 38% of jobsin the U.S. could be vulnerable to AI by the early 2030s. • (Price Waterhouse Coopers says that the most at-risk jobs are in transportation and storage, manufacturing and wholesale and retail)
- 17.
- 18. Meet Morris… Our 9 yearold Labradoodle, who is afraid of water.
- 19. Rain in mm andoutside time in minutes
- 20. Other factors • HaveI already had my steps today? • What time of day is it?
- 21. Quantization Rain (in mm)< 5 5-10 > 10 1 2 3 Steps done < 5000 5000-10000 >10000 1 2 3 Time of day Morning Afternoon Night 1 3 2
- 22. Input A Input B InputC Output
- 23. A B C Input A Input B InputC Output
- 24. A B C Input A Input B InputC Output
- 25. A 1 B 2 C 3 Input A Input B InputC Output 1 2 3 4 3 5 7 6 8 9 1 2
- 26.
- 27.
- 28. End results.. Walking time5 – 10 minutes 10 – 20 minutes > 20 minutes < -1.5 -1.5 – 0.8 > 0.8
- 29.
- 30.
- 31. How does itwork? I mean, internally…
- 32.
- 33. Introducing the neuron Thehero of our story
- 34.
- 35. But a wholelot of them… • This simple model has: • 8 inputs • 3 hidden layers, each having 9 neuros • 4 outputs • 270 lines, thus weights • 27 bias values • In total 297 parameters…
- 36. What??? • Input: thenumber from your model • Bias: just a number to add to it (weight) • Activation Function: a simple math function to apply • Next Layer: do it all over again, or use it as output 𝑆 = 𝑖=0 𝑛 𝑤
- 37. Activation functions • Binarystep: 0, if X < 0, or 1 if X > 0 • Linear Activation: f(x) = x • Non-linear Activation: f(x) = sin(x) • ReLU (Rectified Lineair Unit): f(x) = max(0,x) • Parametric (ReLU): f(x) = max(ax, x) • And so on…
- 38. Workflow… (part 1) • Createa model • Set up number of input neurons • Define number of layers • Define number of neurons per layer • Define number of output neurons • Define initial weight / activation functions • Define optimization model (more on this later)
- 39. Workflow… (part 2) • Feeddata into system • Run through all layers • Check outcome with known-results • Tweak weights in neurons • Do it all over again
- 40. Optimization models • There arehundreds of models • Pick one that is ‘good enough’ • Each scenario requires its own
- 42. Machine learning: it’s a process,not a tool!
- 43.
- 44. ASK THE RIGHT QUESTION • Rephraseyour question to match ML paradigm • Make it obvious • Think of the genie in the lamp
- 45. FIND THE RIGHT DATA •Big data is the most-used source • Use sample data or pre- selected data • OpenData initiative • In NLD: CBS data • Google for ML Datasets and be amazed
- 46. CLEAN UP THE DATA •Clean up missing values • Substitute • Remove rows • Calculate medians / averages / and so on • Remove outrages values • Clip data
- 47. FIND THE RIGHT MODEL • Anomalydetection • Regression • Clustering • Two-class classification • Multi-class classification
- 48. Let’s build something … andmake it good!
- 50. Cats and dogs... "Helloworld" for AI Image Classification
- 51. Demo time! Show methe code
- 52. ChatGPT … or: thenext step?
- 53. ChatGPT • Chat… it’sabout, uhm, chatting? • Generative: it generates something new • Pre-trained: it has been trained before • Transformative: the model behind it all
- 54. Generative • The systemgenerates something new. • Other options are: • Predictive • Classification • … well, we’ve talked about those
- 55. Pre-Trained • It hasbeen trained on a large dataset • Training was unsupervised • Then finetuned on known samples • Other options: • Supervised learning: labeled data • Unsupervised. Used for clustering • And others…
- 56. Transformative • The model. •Lots of jargon about dual layer, each with 2 sub-layers, that feed on each other… • If you really, really, want to know: [1706.03762] Attention Is All You Need (arxiv.org)
- 57. Some numbers • Trainedon 45 terabytes of text • 1.5 billion parameters • Since released, answered over 1 billion questions • Backed by Microsoft for over $10.000.000.000 • A single training-run costs $12.000.000 • Hit 1 million users in the first 5 days….
- 58.
- 59. Stable Diffusion Reverse AI,making new art
- 60. How it works: •Generate non-uniform random noise • Feed the keywords in a reverse neural network • Work back to see if parts of the image fire certain regions • Use an AI system to de-noise the image • Repeat de-noising until satisfied.
- 61.
- 62. Then we look for boundaries. (thatsatisfy our neurons)
- 63.
- 64.
- 65.
- 66. (Yes, I have oversimplified itmassively…)
- 67. Try it out! •Mage.Space. https://mage.space • MidJourney Discord, https://www.midjourney.com • Stable-Diffusion running locally. https://github.com/AUTOMATIC 1111/stable-diffusion-webui • Dall-E (works slightly different, same results) https://labs.openai.com/
- 68. Conclusion And thanks forlistening! Reach me at dennis@vroegop.org