Understanding AI, Machine Learning, and Deep Learning
The Rise of Artificial Intelligence
Everybody’s talking about artificial intelligence these days. AI and machine learning are hot topics that often lead to questions about their differences. Are they the same? If not, what sets them apart? Additionally, deep learning plays a crucial role in this landscape.
Clarifying Concepts: AI vs. Machine Learning vs. Deep Learning
In a previous video, I discussed these three areas—artificial intelligence, machine learning, and deep learning—and received many comments. This article aims to address frequently asked questions to clear up some myths and misconceptions.
Generative AI: A New Frontier
Since the last video, we’ve witnessed an explosion in generative AI, particularly with the emergence of large language models and chatbots. This innovative technology seems to be everywhere, including applications like deep fakes, all falling under the AI umbrella. Let’s explore how these technologies fit together.
What is AI?
Artificial Intelligence (AI) aims to simulate human intelligence through computers. But what constitutes ‘intelligence’? Generally, it encompasses learning, reasoning, and inference. AI’s journey began in its infancy, often viewed as a research project and only gradually gaining public attention.
A Brief History of AI Development
Back in the early days, programming languages like Lisp and Prolog were used, leading to the development of expert systems. This laid the groundwork for the rise of machine learning in the 2010s, as AI technologies matured significantly.
Understanding Machine Learning
Machine learning allows computers to learn from vast amounts of data without explicit programming. It excels in identifying patterns and making predictions, which is particularly useful in areas like cybersecurity where outlier detection is critical.
Deep Learning Explained
Deep learning, a specialized field of machine learning, employs neural networks designed to mimic the human brain’s structure. This “deep” approach involves multiple layers, allowing for complex data processing, albeit with some unpredictability in outputs.
The Role of Foundation Models and Generative AI
Foundation models, like large language models, predict text sequences. These generative AI technologies can create new content, although they often rely on recombining existing information. A common analogy is music composition: while every note exists, new music emerges from rearranging those notes.
The Challenges and Opportunities of AI
Generative AI technology, including chatbots and deep fakes, presents both opportunities and challenges. While it has the potential to revolutionize content creation and information summarization, it also raises ethical concerns regarding misuse and authenticity.
The Adoption Curve of AI Technologies
AI adoption has accelerated significantly in recent years, particularly with the introduction of generative AI. Understanding where each technology fits in this evolving landscape is vital for harnessing its benefits effectively.
I'm sorry, but there is no such thing as AI. Intelligence comes from nature;only animals can be intelligent, all machines can do is imitate intelligence.
When he was an undergrad was in the 1950s? Did anyone else catch that?
I always look for Simple Understanding and Yes it is .. Big Yes It is
Wow.. this is what I was looking for. Very well explained
Very pleasant to listen & watch with the diagram.
Thanks so much.
Great summary… and subscribed
You explained it really well! It was a simple and concise touch to all facets you mentioned, appreciated!
I just transitioned into Tech, no prior knowledge in Tech and I enrolled for a certificate backend course online 3 months ago. I need you to pls advice me if I should proceed with it or switch to a course in AI
They should change the name to augmented intelligence because they are actually not learning. I learn it open up my brain up to a whole different level. Developing a technique to put what learn into motion is the real battle during the learning process.
7:21 Here is a common critical mistake. It may be categorized as an oversimplification, but for many people they misunderstand this limits of this analogy, to the point of being fundamentally wrong.
A better one may be memes. Learning all the musical notes is like learning all the meme formats. New meme format are being created all the time. If a model generated new memes based on it’s understanding of memes, you might call the things it generates memes. But they aren’t. The way memes are recombined into a new meme is a process with meaning. Yes today referencing is oft simply done for the sake of pointing to a known quantity, and that's all. But it can be done to use that specific meaning/symbol and creating new meaning in another context where it has novelty.
You could expect a modern model to not have the ability to understand that deeply, yet people will pooh-pooh criticism along these lines using analogies like that of musical notes.
A model could be trained to the point of a though understand of all the theory of western music, for example. It would no doubt be trail on a lot of real music. So it will have the ability to make references. Do you believe any modern model would be capable of understanding the meaning that a musician may specifically want to incorporate by using specific references? In complimentary ways? In deliberately contrasting ways?
This may sound harsh, but I frequently hear clear evidence of major blind spots. Again, I can’t say what this person really thinks when they are keeping things simple, but many people do have these blind spots that lead to other people, typically coming from the humanities, being dismissive, when the claim is made that these models have attained certain equivalencies.
I just banged this out and have no schooling (well grade school I do have), so I’m hoping this may be received by anyone as remotely coherent xD
So well explained. Please post more videos like this. Love the instruction. It was easy to understand.
Im new subscriber here.
Toate astea din graba mare de a evolua catre a afla raspunsurile, in detrimentul vietii de zi cu zi. Cu alte cuvinte, ne- am inrolat in aceasta situatie toti fara voia noastra, existenta omului nu mai are importanta, totul are scopul de a aduce profit si progres. Ma bucura faptul ca exista si spiritualistii care mai tin pe loc lucrurile altfel…… simteam graba asta mai aspru. Fain, intre timp toti ne relaxam in entertainment. Nu e chiar un plan global, libertate inca mai exista…sper doar sa 🎉 cunoasca globul mai toti oamenii la fel. Adica sa scada f mult preturile la zboruri astfel incat sa putem Toti sa vedem lumea. Multumim!❤
He got the time line of a user, not of a scientist. NN where a bigger thing in science around 1990 and all people working on it knew real world applications would need scaling and scaling would not work unless you are willing to wait 10 or more or Moore‘s cycle, more or less a life time.
Thank you my AI professor. So grateful for this amazing lecture – A lot of respect and prayers for you
"Skibiditoilet must be annexed."
– Deepfake Hitler in 2054, considered real
Wonderfull concept on AI Technology in simple understandable words, actually I have shared it on in my linkedIn for others to take advantage.
Thank you sir, great explanation
Humanity: doesn't fully understand how the brain works.
Also humanity: let's make an intelligent entity modelled off of the brain we don't understand. This could only lead to good and definitely predictable things
Please do not write on air 🙏🏼🙏🏼🙏🏼
Pretty sure I heard about the concept of neural networks in the 80s. My question is if neural networks and artificial intelligence developed separately, how did the cross-pollination come about?
I feel like the growth in many of these paradigm-shifting technologies has always been limited by hardware capacity (and not by conceptual development or software actualization). Whether the issue has been size of network, data delivery speed, bandwidth, processing power, volume of computers, and so on, the fulfillment of technological innovation has lagged decades behind its prophecy. I see the next "hardware challenge" as creation of energy sources (and delivery mechanisms) to drive technology. What interests me most, however, is what currently developing technology will dominate in the 2040s or 2050s (and how will be bring that technology to the forefront more quickly).
Does anyone know how he created this chalkboard set up to present?
Thanks for the explanation. Really didn't get the differences before this video, nice and simple.