Eric Watson’s Post

Machine learning vs. AI: What's the difference? 🧠 Machine learning: A subfield of artificial intelligence. Instead of computer scientists having to explicitly program an app to do something, they develop algorithms that let it analyze massive datasets, learn from that data, and then make decisions based on it. 💡 AI: Most of the time when we're discussing AI, we're using it as the nebulous term for machines that can, to some degree or another, "think." But when we're comparing AI to machine learning, it's the scientific field of study we're interested in. Learn more on the blog ⤵️

There are a thousand places you can go to find out what's happening in Artificial Intelligence today. That's why my new Forward AI newsletter looks at what is yet to come. Is AI truly intelligent? If not, will it become so? What does that mean for you and your organization? What jobs and skill sets will be replaced by AI? What is the biggest reason you should have your own in-house AI program right now? What does the future look like where we have AGI? What are the 3 most important strategic positions I should have in my AI organization? I invite you to subscribe and take steps now to prepare for what is to come.

the knowledge that I want to know in my first year. All of us have heard about AI but you as AI engineer have you heard about mindset of AI's engineer?  why it's that mindset is very important? the way that you look to any intelligent machine or agent will to be more realistic as we say "you will see what the behind the see." and to have these mindsets we have to ask our self some of questions.  1.why we are in the age of AI.  2. why are there conflicts between countries in it  3. if I want to apply AI in any field how are that work? first AI and the concept to make a machine mimes the behavior of human and their tasks has existed since 1980 and the problem was the ability and when i say that i mean that there are two main problems. 1.the power of computation (CPU, GPU,TPU) wasn't powerful as today. 2.second the large of big data wasn't too much to create a need to process these data then why we in AI? after improvements in computation power and the availability of large of data.  here we can say we have the ability to make that concept come to real life now let's move to what that concept???? simply AI comes to make three beautiful things  1.reduce human factor  2.increase the efficiency  3.increase safety  4.increase productivity  5.decreas the cost and when we are talking about these, this advantage represents treasures for any startups and company (think why OpenAI compare with google in searching and the number of users that converting from google to ChatGPT )  so here, now we can say that we are in age of AI. and here all of us want how ?  we want to go through about how can these scientists execute this in real world? the secrete is "ask the human how can you do " then think how to transfer these to machine.  so we want to make a machine do tasks that human does so how human can do these tasks ? the answer is he has some intelligent--> about he has some of conscious --> come from learning and experience -->discover the styles  and are put in environment. then we want to machine discover the styles and are put in environment so, the answer of these was.  machine learning --how can ml does? through  1.supervising and unsupervised learn through the styles   2.reinforcment learn through experiment deep learning -->I want to Mimis the structure of brain to adapt with complex learning.  here you have the base to make any ai system. and the input of these ML or deep learning is numbers.  so how can we convert from our output to machine's input? the answer according to input there is a field like  our text -->NLP our image and videos -->computer vision  wait next part to understand more how AI engineer develop these algorithm and apply them in real world

I keep hearing about AI in the past year but can’t help but wonder how much is actually AI. AI in my view implies machine learning and self improvement. Majority of these “AI” launches are just improved software. So how many have actual self improvement functionality based on user experience? What’s your experience and view? I did do a short research and found an interesting white paper on AI from IBM. My key takeaway from it are the four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting: Human approach: - Systems that think like humans - Systems that act like humans Ideal approach: - Systems that think rationally - Systems that act rationally https://lnkd.in/d7TwpCdH

AI and Machine Learning (ML) can play a significant role in enhancing data quality controls and processes. However, it's important to first establish a robust foundation of data quality measures. Go to article https://lnkd.in/guj9SjkQ 

📏 New blog post — Measuring Effectiveness in Machine Learning Classification Models ⚠ ML models can inadvertently encode societal biases, but mitigating this discrimination often has the side effect of reducing model accuracy. ⚖ The Fairea approach benchmarks bias mitigation techniques to find the best fairness-accuracy balance, allowing us to assess the effectiveness of mitigator models. 🔢 In this step-by-step tutorial, we demonstrate how the technique can be applied. 🔗 Read the full article here: https://lnkd.in/eSgEePa8 #AI #MachineLearning #AIBias #ResponsibleAI #ML

Managing in 2024: How generative AI will scale insight - SiliconANGLE: This was possible through large language models, which used deep learning technology to plow through masses of structured and unstructured data, ... #bigdata #cdo #cto

How To Invest In Artificial Intelligence: From Development To Deployment | The Color Of Markets: However, it wasn't until the popularity of technologies like GPT-3 that AI caught the public's attention. This article aims to examine the best ...

Artificial Intelligence (AI) has been the hot cake for worldwide media for a couple of years now and it skyrocketed late 2022 with the release of Chat-GPT. But AI is more than just Chat-GPT. That's why I wrote an article that extensively discusses: ✔️What is AI ✔️What is Machine Learning ✔️ The "wiring" behind machine learning. This article is beginner-friendly and incorporates storytelling. You can read it here and share your thoughts/feedback🙏🏽. https://lnkd.in/dAMb-FEp

"The three dominant forms of AI involve logic-based systems (machine reasoning), statistical approaches (machine learning), and Large Language Models (LLMs)" #AI #software #technology #machinelearning #ChatGPT