00:00Welcome back to the channel everyone. Today, we're diving deep into the world of AI. We're
00:09talking about the big trends, the game changers that are going to reshape the tech landscape
00:13in 2024. Remember when chat GPT dropped? It was like the whole world woke up to what AI
00:20could really do. Now, things are about to get really interesting. Let's break down
00:25the top 10 AI and machine learning trends you absolutely need to know about in 2024.
00:31All right, first up, let's talk about multimodal AI. Multimodal AI is all about breaking down
00:39barriers between different types of data. We humans experience the world through multiple
00:44senses, sight, sound, touch. Multimodal AI aims to do the same. It can process images,
00:51audio, text, even sensor data, all at the same time. Imagine showing an AI a picture
00:56of ingredients on a counter. It could recognize them, understand their properties, and suggest
01:01a recipe. OpenAI's GPT-4 is already dipping its toes into the multimodal pool. It can
01:07analyze images and respond to prompts that combine both text and visuals. We're talking
01:12about AI that can help doctors diagnose diseases, power robots to navigate complex environments,
01:18and even help you design a website. This technology has the potential to revolutionize
01:22entire industries. Now, if multimodal AI is about perception,
01:30agentic AI is all about action. We're talking about AI that doesn't just wait for instructions
01:35but can actually set its own goals and figure out how to achieve them. Imagine an AI assistant
01:40that's not just responding to your commands but is actively anticipating your needs. Need
01:45to book a flight? Your agentic AI has already scanned for the best deals and times. Got
01:50a deadline coming up. It's already prioritizing your tasks and clearing your schedule. But
01:55agentic AI goes way beyond personal assistance. We're talking about AI that can manage entire
02:00systems, optimize complex processes, and even make critical decisions in real time. This
02:06is the kind of AI that could help us solve some of the world's biggest challenges. The
02:10future of agentic AI is still being written, and it's up to us to make sure it's a future
02:15we can all be excited about. So far, we've talked about AI that can see
02:21and understand like us, AI that can act autonomously. Now, let's talk about who gets to build and
02:27use this technology. That's where open source AI comes in. Open source AI is changing the
02:33game by making powerful AI models available to the public. Take Metasllama 2, for example.
02:39It's a powerful language model that's completely open source. This means researchers, developers,
02:44and small businesses can now access cutting edge AI capabilities without having to build
02:48everything from scratch. Open source AI fosters innovation and promotes transparency and collaboration.
02:55Of course, there are challenges like the risk of misuse and sustainability. But overall,
03:01open source AI represents a fundamental shift in how we think about AI development.
03:10Retrieval Augmented Generation, or RAG, making AI smarter with external knowledge. Traditional
03:15language models rely solely on the data they've been trained on, which can lead to issues
03:19like hallucinations. RAG addresses this by giving AI models access to external knowledge
03:24bases. Instead of just relying on its internal memory, RAG allows AI to consult a vast library
03:31of information in real time. This means the AI can pull in relevant facts, figures, and
03:37context to provide more accurate and comprehensive responses. RAG improves the accuracy and reliability
03:44of AI-generated content. It allows AI models to stay up to date with the latest information,
03:50and it can help reduce the size and complexity of AI models. RAG offers a powerful approach
03:55to achieving more trustworthy and informative AI applications.
04:03Customized Enterprise Generative AI Models, tailoring AI to specific needs. Instead of
04:10relying on one-size-fits-all solutions, companies are realizing that they can get better results
04:15by building or fine-tuning AI models specifically for their industry, their data, and their
04:21workflows. By training AI models on their own data, businesses can create AI solutions
04:27that are more accurate, more efficient, and more aligned with their specific goals. This
04:32approach allows businesses to leverage their own data to create a competitive advantage.
04:38Customized AI models can be more cost-effective and offer greater control over data privacy
04:43and security. We're already seeing this trend play out across industries. Financial institutions
04:49are building custom AI models to detect fraud and assess risk. Healthcare providers are
04:54developing AI-powered diagnostic tools tailored to specific patient populations. The era of
04:59one-size-fits-all AI is giving way to a more nuanced approach.
05:06Chapter Six. The Demand for AI and Machine Learning Talent, the Skills You Need to Thrive
05:11in the AI Age. The demand for AI and machine learning talent is exploding. Businesses need
05:18data scientists, machine learning engineers, and AI ethicists. One of the most in-demand
05:24skills is AI programming, with Python being the go-to language. Data analysis is another
05:29crucial skill. MLOps, or machine learning operations, is about managing the entire lifecycle
05:35of AI models. It's not just about technical skills. Diverse perspectives are crucial for
05:40fair and unbiased AI development. Whether you're a seasoned developer or just curious
05:44about AI, there's never been a better time to dive in and learn new skills. The AI revolution
05:49is here, and it's creating opportunities for those who are ready to seize them.
05:56Chapter Seven. Shadow AI, the Unseen Risks of Unsanctioned AI Use. Shadow AI happens
06:01when employees use AI tools without the IT department's knowledge or approval. This can
06:07create security issues, as unsanctioned AI tools might not have the same level of security
06:11protocols. It can also lead to compliance issues, as different industries have different
06:17regulations around data privacy and usage, and it can create inconsistencies in workflows
06:23and make it difficult to track results. Companies need to educate their employees about the
06:28potential risks of using unsanctioned AI tools, and establish clear guidelines for AI adoption.
06:34It's about finding a balance between fostering innovation and ensuring responsible AI use.
06:40Companies need to create an environment where employees feel comfortable experimenting with
06:43new technologies, but also understand the importance of security, compliance, and ethical
06:49considerations. The rise of shadow AI highlights the need for proactive AI governance within
06:55organizations.
06:57Okay, let's be real for a second. There's a lot of hype around AI, but as companies
07:03move from the experimental phase to actually integrating AI into their core operations,
07:09they're encountering real-world challenges. One of the biggest hurdles is data quality.
07:13AI models are only as good as the data they're trained on. Another challenge is system integration.
07:18AI needs to seamlessly integrate with existing systems and workflows. Building, deploying,
07:24and managing AI systems requires specialized skills and knowledge. It's crucial for companies
07:29to set realistic expectations about what AI can and cannot do. By focusing on practical
07:36data quality and clear business value, companies can harness the true power of AI to drive
07:41meaningful results.
07:45As AI becomes more powerful and pervasive, it's more important than ever to address the
07:50ethical and security implications of this technology. One of the biggest concerns is
07:54AI bias. AI models are trained on data, and if that data reflects existing societal biases,
08:01the AI model will perpetuate those biases. Another concern is the lack of transparency
08:06in AI decision-making. Many AI models are often described as black boxes because it
08:11can be difficult to understand how they arrive at their decisions. And then there's the issue
08:15of AI security. Addressing these challenges requires developing ethical guidelines and
08:20principles for AI development and deployment. Promoting diversity and inclusion in the AI
08:26workforce is essential for identifying and mitigating potential biases. Building trust
08:31in AI is about ensuring that AI is developed and used in a way that is ethical, responsible,
08:37and aligned with human values.
08:40All right, we've talked about the technical trends and ethical considerations, but we
08:46can't forget about the legal landscape. As AI becomes more integrated into our lives,
08:51governments around the world are grappling with how to regulate this rapidly evolving
08:55technology. One of the most significant developments in AI regulation is the European Union's
09:00AI Act. It's a landmark piece of legislation that aims to create a comprehensive framework
09:06for AI governance. The AI Act categorizes AI systems into different risk levels, with
09:12higher risk systems subject to stricter requirements. While the AI Act is specific to the EU, it's
09:18likely to influence AI regulations globally. For businesses, navigating this evolving regulatory
09:24landscape can be challenging. It requires staying informed about new regulations and
09:29adapting practices to ensure compliance. AI regulation is essential for building trust
09:34in AI and ensuring its responsible development and deployment.
09:41So there you have it, the top 10 AI and machine learning trends that are shaping the future.
09:47From AI that can see and understand like us, to the ethical considerations and evolving
09:52regulations, it's clear that AI is no longer a futuristic concept. It's here, and it's
09:57transforming the world around us. Whether you're a tech enthusiast, a business leader,
10:02or just someone who's curious about the future, staying informed about these trends is crucial.
10:07AI is going to impact every aspect of our lives from the way we work and communicate
10:13to the way we solve global challenges. If you found this video helpful, don't forget
10:17to give it a thumbs up and subscribe to the channel for more insights into the world of
10:22AI and machine learning. And be sure to let me know in the comments which trend you found
10:26most interesting or surprising.
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