Welcome to AI Update, where we explore the latest advancements in artificial intelligence with insights into Open AI, Google, Gemini, generative AI, machine learning, and more. Now let's learn together. I'm your host, Will Walden. So how is Google DeepMind making its robots smarter with Gemini? And what new methods are they using to train these robots?
Now imagine we're in a sci-fi movie like The Matrix, but instead of fighting machines, we're making them more intelligent and efficient, which is bad for us and for the humans. But Google DeepMind is leveraging its latest AI advancements to revolutionize robot training and efficiency so they can actually help us humans. Now they've introduced a cutting edge AI training method known as Gemini 1.5 Pro.
This approach uses vision through video tours to train robots, enhancing their ability to maintain, navigate, and complete various tasks. And by utilizing this method, robots can learn more effectively from real world scenarios, thereby improving their operational efficiency and adaptability. Another significant development by Google Deepmine is the introduction of Jest, the joint
example selection training. This method aims to optimize the AI training process, significantly reducing computing costs and also energy consumption. With reported 13 fold increase in performance and A10 fold improvement and power efficiency, Jest represents a major step forward in the economics of AI development. Now the innovation of Jest is particularly important given the ongoing discussions about the environmental impact and the high costs associated with AI data centers.
And by reducing these barriers just could facilitate broader access to AI tech and accelerate advancements, especially in areas like e-commerce and multilingual support. Now it's important for these evolving AI methods. Now, new training methods for large language models are essential due to the rapidly evolving nature of data and the increasing demand for models that can adapt on the fly to new
information and contexts. And since the inception of machine learning, AI training methods have evolved significantly. Traditional supervised learning methods relied on labeled data sets, while more recent approaches include unsupervised learning and reinforcement learning. These methods allow models to identify patterns in unlabeled data and learn through trial and
error. Now, the GIST method differs from traditional AI training techniques by focusing on entire branches of data rather than individual data points. Initially, it creates a smaller AI model to assess data quality from high quality sources and ranks the batches accordingly. These rankings are then compared to a larger, lower quality data set to determine the most suitable batches for training a
large model. Now, the innovative approach that's happening here ensures that the training process is more effective and more efficient. It reduces time and resources as well of the practical implications of just extend beyond general adaptability with potential applications in various specialized domains such as healthcare and also in finance. New methods are crucial for language models to respond accurately to questions about niche or about sensitive topics.
This includes areas dealing with highly sensitive information the traditional LLM training algorithms might not handle appropriately, and several emerging approaches and AI training could significantly impact online commerce. One such method is reinforcement learning from human feedback, or RLHF, which fine tunes models based on user interactions. This technique could enhance recommendation systems, providing more personalized and relevant product offerings.
Another promising technique is a parameter efficient fine tuning. It's PEFT, which allows AI models to adapt efficiently to specific tasks. Now, this method could be particularly beneficial for online retailers during peak sales periods, optimizing their algorithms for better performance and more money. You know, ensuring the language models can provide accurate responses in multiple languages is a critical aspect of AI development, especially for
global e-commerce. Many companies mistakenly believe their AI systems can effectively translate content across different languages, but this often leads to inaccuracies, particularly with industry specific jargon. To tackle the issue, though, some organizations are developing new multilingual AI training approaches. Language IO, for instance, has implemented a retrieval, augmented generation, or RAG process influenced by
multilingual strategies. This approach aims to enhance the accuracy of multilingual support in e-commerce settings. Now, advancements in AI training methods could transform online shopping by offering better product suggestions, improved customer support, and more efficient business operations. AI that comprehends multiple languages can help companies expand their footprint globally and improve customer satisfaction. So you would get repeat
customers. And faster AI training methods might lead to quicker deployment of AI for various business tasks, such as better inventory management and enhance customer service chat bots. This has enabled businesses to operate more smoothly and respond to customer needs more effectively. Now, with more accurate AI that can communicate in multiple languages, businesses could enter new markets more efficiently and provide local services without relying on human translators.
This capability could significantly enhance their global reach and customer service quality. Now, improved training approaches can enhance online commerce by enabling more accurate, context aware multilingual support. It leads to better customer experiences, reduced language barriers, and potentially increasing revenue. Now all that said, Google's DeepMind enhancements with Gemini AI and Jest are an important stride for AI and for commerce.
They promise to make AI systems more efficient, adaptable, and capable for multi legal support, potentially transforming e-commerce and various sectors in the near future. Hey, thank you so much for listening today. I really do appreciate your support. If you could take a second and hit this subscribe or the follow button on whatever podcast platform that you're listening on right now, I greatly appreciate it. It helps out the show tremendously and you'll never
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