GPT to improve Product Search

Prompt - Return attribute, value coreference for below query <Return all black shoes with prices less than 50 dollar>

• Attribute: Color, Value: Black
• Attribute: Price, Value: Less than 50 dollars
• Attribute: Product type, Value: Shoes

Return attribute, value coreference for below query <Cricket is interesting game, India is in Top 10 cricket playing countries>

• Attribute: Game, Value: Cricket
• Attribute: Rank, Value: Top 10
• Attribute: Country, Value: India

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Reduce your time to Read / Listen / Summarize / Save time

Reduce your time to Read / Listen / Summarize / Save time

1. Pick the video to summarize - Link

2. Get the transcript from - anthiago

3. Use GPT4 to get key summary

Extract top 10 key predictions from below text. Summarize each line in 10 words only

1.

2.

3.

The Results are

• By end of the next year, chat ChatGPT will be available on mobile phones without internet.
• The figure out mechanism is about making humans scale, akin to an 'analyst with a bad memory'.
• The future of AI is pushing internet intelligence to the edge for every person, country, culture, and company.
• Most popular language models in the world, gptj and neon x, are paving the way for deploying AI systems on-premises.
• Stable diffusion technology can reduce 100,000 gigabytes of images to a 2 gigabyte file that can generate anything.
• Transforming language into images and visuals; four of the top 10 App Store apps based on this premise.
• The second-generation model of this technology will produce photorealistic images and video.
• The trajectory indicates that in five years there will be no need for programmers.
• Open-source models will overtake proprietary models as they offer ownership and interpretation advantages.
• By next year, the capacity of chat GPT will increase, enabling the passage of a level 3 programmer exam

Points I tend to agree with, highlighted in a different color

Earlier I used to spend more time reading complete tech talks, and webinars. Now scanning a few mins and the view count convey intent to read. Passed the video link to generate a transcript. Leverage chatGPT to summarize key points. These five key points and my comments added

• Very soon - Edge-optimized offline LLM models ChatGPT will be available on mobile phones without the Internet. - Edge optimized / Like keyboard word suggestion sentence/reply recommendation possible
• Open-source models will overtake proprietary models as they offer ownership and interpretation advantages. - Now we see Google hosting an unlearning competition for models, More models more explainability/reasoning. The VGG moment is crossed. Resnet / RNN / Another transformer moment coming
• The trajectory indicates that in five years there will be no need for programmers. - This is scary. The need to memorize syntax is no longer needed. How we connect the dots, and consider aspects of the solution becomes key
• Stable diffusion technology can reduce 100,000 gigabytes of images to a 2 gigabyte file that can generate anything. - Right now vision / 3D is very early stages. Next / Coming years 1~2 will get realistic / production version quality with more data trained and larger models tuned to prompts

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Study - Wearables - Impact

 

Ref - Link

• We’ll monitor physiological parameters, such as heart rate, blood oxygen level, steps and calories using a smartwatch
• We’ll monitor the food intake using the Cronometer App.
• Environmental exposures, including both chemical and biological exposures, are monitored using a phone-sized device
• Metabolites and immune markers in the blood are monitored as well.

"We believe consumer health technologies, apps, wearables, and self-diagnosis tools have the potential to strengthen the patient-physician connection and improve health outcomes."
~Dr. Glen Stream, Chairman, Family Medicine for America’s Health#wearables #healthtech pic.twitter.com/6a86LfIqMX

— Nextwear Technologies (@Nextweartech) August 22, 2022

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Debate about AI vs Make Money that just debating :)

We can keep debating ChatGPT good bad, You can see already startups making moves :)

All NLP features available templatized

Words = Business is money

Startup - Jasper AI 

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AI vs Natural Intelligence

AI - any device that does things that associate with human intelligence
Natural Intelligence - Imagination, creativity, fantasy, intuition, problem-solving. AI and natural intelligence are two different things, each supplies a lack of other 
Human Interpretation is leaps ahead of Machine Interpretation

Right Use Case

With the Right context / matured model, Tesla Safety Stats - Q3 2022 - In the 3rd quarter, we recorded one crash for every 6.26 million miles driven in which drivers used Autopilot technology.

Purposeful Bound to Fail 

In some cases, where machines can fail, for example in a classification problem

Selecting the Right Use Cases

ML is more of a collaborative effort. Business needs - You spot it, and you can demonstrate competency to get a buy-in to implement. Alignment is critical to understand the short-term/long-term impact. You may not be immediately right but eventually, you may be right

Spotting what others miss needs all three views - domain, data, and ML opportunities.

Datasets Search

• https://datasetsearch.research.google.com/
• https://github.com/awesomedata/awesome-public-datasets
• https://archive.ics.uci.edu/ml/index.php
• https://www.kaggle.com/datasets
• https://msropendata.com/

• Customer related
• Operations related
• Risk Avoidance

Ref - Link

Ref - Link

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ChatGPT is 'not particularly innovative'

Good Read - Post1

• ChatGPT is 'not particularly innovative,' and 'nothing revolutionary', says Meta's chief AI scientist.
• The ChatGPT program is a case less of scientific breakthroughs than it is an instance of decent engineering, said LeCun

My Views

• Yes it is a gradual evolution from CNN - RNN - Transformer - Large Language Model
• Earlier we had next character prediction, Now we have sentence level 2048 token
• Alexa is a simple Q&A Bot vs Boston Dynamic Robots. Solutions need to solve the problem no need to be revolutionary.
• Facebook had data but never came up with this LLM :)
• GPT is lead to hybrid variations of chatbots, Alexa, Edge Devices, and Personalized Assistants.

Using GPT for Facts / Data related note retrieval is much better than asking it to solve a maths problem. Remember GIGO (Garbage In, Garbage Out), It may not be able to reason or solve but it can give you templates, formulas for you to get started, We do not read to remember the syntax but focus on solutions.

ChatGPT passes MBA exam given by a Wharton professor

• Amazing job at basic operations management and process analysis questions including those that are based on case studies
• Remarkably good at modifying its answers in response to human hints
• Remarkable ability to automate some of the skills of highly compensated knowledge workers in general and specifically the knowledge workers in the jobs held by MBA graduates including analysts, managers, and consultants

Why ChatGPT was able to create impact ?

• Solving a Niche problem
• Unparalleled user experience
• Plug and Play infra - API
• Human in Loop 
• Reinforcement learning along with Transformers

"Original Thinking / Looking at different way hybrid ideas not just one way" matters

"Perfect vs Good Enough Solution"

Ref  - Link

Good one, 

We can become better writers with ChatGPT

• Knowledge pointers
• Blending creative thought 
• Add your own perspective for the insight
• #Google democratized information. With #ChatGPT #Reasoning / #knowledge / #Facts is getting democratized. 

Why didn't DeepMind build GPT3?

• Organisational-level risk taking involved in building GPT3 - Brand Impact
• Build systems that appear to have intelligence when interacted with.

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Models at Different Stages of Customer Lifecycle

 

Stage I: Reach

Reach is the initial stage of the buyer’s journey. The key is to attract more visitors and provide an engaging experience that will lead to a purchase.

1. Smart Content Curation 

2. Programmatic Media Buying

3. AI Generated Content 

4. Voice Search 

Stage II: Act

The second stage of the consumer journey is intended to draw the consumer in and to make them aware of your products and services.

5. Propensity Modeling 

6. Ad Targeting 

7. Predictive Analytics 

8. Lead Scoring 

Stage III: Convert

This is the important stage of moving a consumer from being an interested prospect to being a customer or member.

9. Dynamic Pricing 

10. Re-Targeting 

11. Web & App Personalization 

12. Chatbots 

Stage IV: Engage

Once a purchase is made, it is important to continuously build engagement and loyalty with the intention of expanding the relationship and potentially generating referral business.

13. Predictive Customer Service 

14. Marketing Automation 

15. 1:1 Dynamic Emails 

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Deep Learning Markets - Use cases

 Combination of Vision, Forecast, Recommendation, Anamoly Detection, Optimization

Ref - Link

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Vision across domains - Tools vs Solutions

Solutions vs Familiarity vs Awareness vs Skills

You will observe a bunch of startups in each domain/category. Learning the tools vs solving a problem both require different levels of skills. Learn the tool, and master the problem, techniques, and alternatives/solutions.

• Vision in healthcare - Xray, MRI, Scan images
• Vision for BPO Automation - Automated invoice processing
• Vision for Monitoring / Inspection - Quality Assessment
• Vision for Safety - Industrial safety
• Vision for Fruit Freshness - Automated pricing on quality

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AI - Education Opportunities

Paper #1 - Strengthening e-Education in India using Machine Learning

Key Notes

Applying different data mining algorithms on the data of the person and suggesting which course is appropriate for him based on his background knowledge

Paper #2 - Personalized Education in the AI Era: What to Expect Next?

Key Notes

Content summarization and question generation Multi-modal content understanding: Human-in-the-loop content design

More Reads

Teaching Machine Learning in K–12 Computing Education: Potential and Pitfalls

Estimating returns to special education: combining machine learning and text analysis to address confounding

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