I share my learning journey into Data Science with my amazing LinkedIn friends, follow me and let's grow together!

For downloadable ML/ AI/ Data Science learning materials, please check my previous posts.

** Opinions are my own and not the views of my employer **

Alex Wang

Data Scientist

Generative AI

Our goal is to empower everyone, from beginners to leaders, with personalized, AI-native learning experiences. We aim to provide the skills and knowledge needed to thrive in an AI-powered world.

Head of Education

AI4Diversity

AI 4 Diversity (AI4D) is a nonprofit global initiative that engages and educates diverse communities about AI to benefit global society.

Member of Operation Team

Quantium

Global Market

Deloitte Australia

Business Algorithms

University of Technology Sydney

Casual Academic

Hackmakers

Pactera

Data Analyst

I share my learning journey into AI and Data Science here, click 'follow' and let's grow together!

For open access resources, productive tools, and learning materials in AI/ ML/ Data Science, please check my previous posts.

** Opinions are my own and not the views of my employer **

Member of Global Committee

It is voluntary role helping the global management team. 
AI 4 Diversity (AI4D) is a nonprofit global initiative that engages and educates diverse communities about AI to benefit global society.

Amazon is upgrading Alexa by replacing its in-house AI with the Claude model from Anthropic.

I've always been a fan of Claude AI, particularly for its conversational and engaging responses, and its stronger emphasis on ethical features. It seems like a great fit for powering conversational smart assistants like Alexa.

Originally, Alexa has always been powered by Amazon's own AI model, but testing has shown that it takes longer to respond to users than expected. The Claude model apparently performed significantly better in this regard.

This collaboration between the two tech companies is not sudden. Last year, Amazon invested $1.25 billion in Anthropic, and earlier this year, they added an additional $2.75 billion investment.

Amazon promises that the upgraded Alexa will offer more complex, context-aware interactions and multi-step task handling.

The new version sounds exciting, but it comes with a subscription fee of around $5-10 per month.

Alexa has always been free to use for a long time, and the enhanced features of this new version might justify the cost for frequent users. However it's always tricky to ask casual users to start paying for a service that was initially free.

Would you be willing to pay for it? What do you usually use Alexa for?  
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For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #innovation #bussiness

All those awesome courses are free; I put them into two learning paths with recommended sequences: 

(Short version from Microsoft)
𝐆𝐞𝐧𝐀𝐈 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 
https://lnkd.in/gYUD93r9

𝐀𝐈 𝐟𝐨𝐫 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 
https://lnkd.in/dHeHqtAA 

𝐔𝐬𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐞𝐯𝐞𝐫𝐲𝐝𝐚𝐲 𝐰𝐨𝐫𝐤
https://lnkd.in/gUY-aVvd

(Long version from Google, Databricks, DeeplearningAI and LinkedIn)
𝐀𝐈 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 
https://lnkd.in/dtFpAQkg 

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬
https://lnkd.in/g7jpSq7H

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 
https://lnkd.in/ghk5eygZ 

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐀𝐈 
https://lnkd.in/ghfQiMmP 

𝐄𝐭𝐡𝐢𝐜𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐀𝐠𝐞 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐥 
https://lnkd.in/dD63DHUs 

*About learning AI:

AI is an extremely broad field with intersecting and interacting subfields; spend some time understanding the general landscape. Trust me, worth it.

If you're not building AI, don't jump into technical details right away. Instead, focus on:

- What AI is and its value proposition

- The risks and challenges associated with AI

- How AI can enhance your work and organization, what are the required resources

- When it comes to applications, is it AI-native? Can you achieve the same results without AI?
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For more on AI & learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #datascience

David Q. L.

Uthman Ali

What does a data scientist's role look like in real life?

What you might think (also what I thought in school): 
Business - Insights - Decision

It is not incorrect, but the real workflow is more aligned with this:

Idea - Data - Research - Analysis - Production - Send it out to stakeholders

The responsibilities of a data science team can vary depends on organization and project, but the team structure typically goes in two main ways:

Team with Embedded Data Scientists - Focus on one area or serve a single purpose, working alongside other team members like product managers and software engineers.

Data Scientists as In-House Consultants - Assigned to projects as needed, not necessarily specialists in one domain.

Some teams blend elements of both structures, to varying degrees. I've been fortunate to experience all three, each with its pros and cons.

Consultant style allows you to work on a variety of projects, but your work also depends more heavily on the cooperation from other teams.

One thing I learned and always reminded myself is to invest time in building relationships first. 

Tech work is never just about tech; trust will greatly help your work in the following collaboration.

Also, two great and free online courses by Harvard University, in data science:

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞: 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (8 weeks)
https://lnkd.in/gUNVYgGB

𝐇𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (4 weeks)
Link: https://lnkd.in/gv9RV9Zc
 
Image via Daniel Lee
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For more on AI and learning materials, please check my previous posts. Alex Wang

#machinelearning #datascience #python #dataanalysis

David Uhlmann

I used Coursera, but you can also find all those courses on other platforms. 

As a bonus, here's an awesome AI webinar coming up (LLMs in Finance), free as well
0) https://bit.ly/3WYndpT

Courses link
1) https://bit.ly/3xGUMCJ

2) https://bit.ly/4d5MtR5

3) https://bit.ly/3QbvbZ9

4) https://bit.ly/3U9KsuR

5) https://bit.ly/3JMdL1P

6) https://bit.ly/3Q8Qjz2

7) https://bit.ly/3w4b8oD

8) https://bit.ly/3UsEJBH

9) https://bit.ly/446cQT6

Also, Coursera provides two special offers for 𝐈𝐍𝐃𝐈𝐀 

1- India Independence Month 
INR 7999 for Coursera Plus Annual (7/29 - 8/23)

2- India ‘End of Summer’ 
INR 8499 for Coursera Plus Annual (8/29 - 9/30)

➡️Learn more: imp.i384100.net/5goYn2

*Both offers are time-limited, so be sure to secure your spot before the deadlines if you decide to get it. 

If you're new to Coursera, there's a 7-day free trial you can take advantage of before you decide!
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I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #datascience #bussiness

AI is not limited to LLMs. There’s a high chance most of us already use AI more than we realize.

Imagine a day like this, and see how many activities you knew you were interacting with AI:

1) Your morning starts with checking your spam folder for an email you've been waiting for - 𝐞𝐦𝐚𝐢𝐥 𝐬𝐩𝐚𝐦 𝐟𝐢𝐥𝐭𝐞𝐫.

2) During lunch break, you book tickets for your next vacation and chat with a customer support bot when you encounter a payment error - 𝐜𝐡𝐚𝐭𝐛𝐨𝐭.

3) At 6 PM, back home, you do some exercise with your smartwatch and put on a suggested playlist - 𝐟𝐢𝐭𝐧𝐞𝐬𝐬 𝐭𝐫𝐚𝐜𝐤𝐞𝐫/ 𝐩𝐥𝐚𝐲𝐥𝐢𝐬𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧.

4) Your neighbor comes by and rings your camera doorbell - 𝐬𝐦𝐚𝐫𝐭 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐜𝐚𝐦𝐞𝐫𝐚.

5) Open Uber Eats to order food delivery - 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐫𝐨𝐮𝐭𝐞𝐬 𝐚𝐧𝐝 𝐞𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐚𝐫𝐫𝐢𝐯𝐚𝐥 𝐭𝐢𝐦𝐞𝐬.

6) Later, with your kids, you browse recommended shows on Netflix - 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬.

*Bonus: Live webinar on 'Build a Recommendation System', practical and free to join (starts in 12 hours)
https://bit.ly/4fP7tgu

7) Before sleep, you open Facebook and see you've been tagged in photos your friends shared – 𝐢𝐦𝐚𝐠𝐞 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧.

Those are all applications of AI. How many did you recognize?

Not every single application seems exciting, right? Actually, about 90% of AI solutions for real-world projects are still based ML algorithms, not LLMs or other super-advanced algorithms.

Another reason might be that many things have been incorporated into AI over time, but most of them operate in the background.

This trend has been ongoing for over a decade and has literally been integrated into every aspect of our lives. 

This includes decision-making processes, such as which political ads we see, what jobs we qualify for, whether we qualify for loans, etc.

I enjoy the benefits of technology, but I also like to keep myself mindful of its influence and how it may shape my decisions and experiences.

Image: Google Intro to AI 
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I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #machinelearning #datascience #technology

I recently joined the world's largest AI community, Generative AI , as the 'Head of Education'.

It's an incredible community with over 7 million followers across its social media platforms.

The team behind Generative AI is exceptional. I've had the pleasure of working with some of the most passionate and innovative minds in the field.

We've been developing this educational project for some time now, and we're excited to be nearing its launch!

Our goal is to empower everyone, from beginners to leaders, with personalized, AI-native learning experiences. We aim to provide the skills and knowledge needed to thrive in an AI-powered world.

In short, hype away, let AI be on your side.

There's more information on our website, so be sure to check it out.  

Join us on this exciting journey to build the world's largest AI community: https://lnkd.in/gWvRCbYQ

#artificialintelligence #machinelearning #datascience #technology #opensource

Janson Lim

Top Skills in the Data Field

It's interesting to see the most required skills for different roles by looking at the screenshot I selected, where I chose 'all.'

According to the website's data, Python, SQL, AWS Cloud, Spark, and Express are the top skills across various categories.

*For SQL upskilling, here's one great workshop is coming, live event, free to join
https://bit.ly/4cBBH3S

This ranking is based on over 2.7 million job postings globally from Google searches and up to date.

McKinsey's report, Trend in AI shows that the most required jobs in applied AI and industrializing ML are still data scientists, software engineers, data engineers, and software developers.

Image: datanerd tech
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For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #machinelearning #datascience #technology #opensource

Awesome open-source project explains everything about LLM Transformer Models!

Just came across this interactive website today, and it has been super fun to play with - provides a detailed, visual explanation of how those models work.

A great resource for anyone looking to gain a deeper understanding of how Transformer-based AI models like GPT work, including:

- Self-attention mechanisms
- Encoder-decoder architecture
- Positional encoding
- Multi-head attention

Website: https://lnkd.in/gfKiHSXQ

*The website is completely free to use and hosted on GitHub Pages, allowing for modification, and distribution. 

Have fun!
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For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #python

Kittivit Kanokwanvimol

Are LLMs politically biased? How bad it is?

A recent study investigated the political preferences of 24 popular LLMs by putting them through 11 political orientation tests.

The result? Most LLMs have left-leaning views!

Take, for instance, the political coordinates test (as in the figure), most LLMs ended up in the left-liberal quadrant. 

In another political compass test, all the LLMs evaluated fell into left-libertarian.

One possible explanation is that the guidelines for LLMs often stress the importance of generating 'unoffensive' outputs, which may result in more cautious language use and an overall leftward lean.

This is not just a random guess, one of the study's findings supports this assumption: the models that had undergone supervised fine-tuning displayed more extreme political preferences.

*The paper is not very lengthy and easy to go through, worth reading https://lnkd.in/gXgFmmZa

ps. SPT can help with the bias. But should we? Especially when the motivation is to make them 'less offensive'。

More questions need us to think, who defines what is 'offensive' or 'acceptable'? Should AI models reflect societal norms or promote a specific ideology?

Bias vs. fairness, free speech and diversity...

Would love to hear your thoughts on this!
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For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #llm #generativeai

Are we underestimating how soon the future will arrive?

Sakana AI has created the AI Scientist, not just a fancy name, but a fully automated open-ended scientific discovery system.

The system uses foundation models to perform the entire research process independently, making new scientific discoveries.

The report tested the system for discoveries in machine learning research. While there are still some occasional flaws in this very first version as mentioned in the report, the results are impressive.

The potential is crazy. 

When it can do self-improvement, the compound effect would be exponential.

Let's think even bigger: what would happen if we add agents to it? It would make scientific discoveries on its own, and agents would test the assumptions in the physical world, then add the results back into the system's understanding, and continue.

When deployed at scale, the contribution to science discovery would be unimaginable.

Am I too optimistic about AI? Perhaps. But I wouldn't have thought AI tools like GPT-4o were possible just five years ago.

The future might be closer than we think.

Add: About the cost, which is shockingly affordable - 
'Each idea is implemented and developed into a full paper at a cost of less than $15 per paper. '

Paper https://lnkd.in/gApjmccc
Project https://lnkd.in/gfcUwHiC

ps. I'm very optimistic about AI development and believe in its huge potential. But I also believe that around 90% of AI solutions for real-world projects are still based on ML algorithms.

Riding a bike to the café doesn't conflict with the existence of high-performance cars like the 1,914 hp Nevera.
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For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #science

The courses I share are from Coursera, but you can also find them on other platforms by searching for the course name.

The recommended sequence for each category is from left to right.

Limited-time offer from Coursera: Get 30% off their annual Coursera Plus subscription
➡️imp.i384100.net/rQVNXR

- Unlimited access to over 7,000 courses 

- Professional Certificates from top industry leaders like Google, Meta, Microsoft

- career-defining skills you can get, including high-growth fields like UX design, data analytics, and cybersecurity

*The offer ends at 30 September, so be sure to secure your spot before the deadlines if you decide to get it. 

If you're new to Coursera, there's a 7-day free trial you can take advantage of before you decide!
__________________

I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #productivity #bussiness

You haven't heard of this company. But you probably talked to their AI and thought it was a human.

Bland AI is the company behind all the AI Phone Calls we've heard about. Until today, no one knew who they were. Over the past year, they've quietly partnered with the world's largest enterprises to automate all their calls. 

Here's how their AI works:

1. Tell it your use case and how to behave. It can behave like any sales, customer support, or other phone employee.

2. Pick a voice and language. You can clone anyone on the planet.

3. Integrate with any tool. Your AI can accept payment over the phone, schedule meetings, update CRMs, etc.

After this, enterprises are able to send or receive millions of calls simultaneously, 24/7. 

It's a must-have for enterprises to stay competitive, but now, no one ever knows if they're speaking to an AI.

Where do you think all of this is going? Call it and tell me what you think: http://bland.ai/

#innovation #productivity #bussiness #artificialintelligence #technology

The courses I share are from Coursera, but you can also find them on other platforms by searching for the course name.

The recommended sequence for each category is from left to right.

Limited-time offer from Coursera: Get 30% off their annual Coursera Plus subscription
➡️imp.i384100.net/rQVNXR

- Unlimited access to over 7,000 courses

- Professional Certificates from top industry leaders like Google, Meta, Microsoft

- career-defining skills you can get, including high-growth fields like UX design, data analytics, and cybersecurity

*The offer ends at 30 September, so be sure to secure your spot before the deadlines if you decide to get it.

If you're new to Coursera, there's a 7-day free trial you can take advantage of before you decide!
__________________

I share my learning journey here. Join me and let's grow together.

For more on AI and learning materials, please check my previous posts. Alex Wang

#artificialintelligence #technology #machinelearning #productivity #bussiness

You can create AI internal tools without a single line of code! 

StackAI is a no-code platform for developing LLM applications. MIT PhDs created it with over a decade of experience building AI applications.

Sign up for free https://bit.ly/3XbTTeP

This is a game-changer for operations and enterprise teams! You can build AI applications for:

 ✅ Automating support desks
 ✅ Auditing compliance of documents
 ✅ Chatting with your CRM
 ✅ Training new employees
 ✅Performing financial due diligence

And many more use cases!

With Stack AI, you can:

 ☑ Build end-to-end, production-ready AI assistants using a drag-and-drop interface
 ☑ Save up to 80% on your AI journey compared to having an in-house AI team
 ☑Increase your time-to-market for your AI journey (weeks instead of months)
 ☑Ensure compliance with SOC 2 Type II, HIPAA, GDPR, and on-premise deployment

Give it a try, and let me know how it goes!

#productivity #technology #bussiness #artificialintelligence #nocode

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