Autonomous car startup raises $1 billion
Sharvesh Premkumar
Business Intelligence Engineer | Freelance Data analyst | BI Consultant | Freelance Business Intelligence Specialist | Analytical Engineer | Data Engineer
Belated labour day wishes, everyone! AI’s impact on the job market, it's evident that amidst the challenges lie significant opportunities for those willing to adapt and embrace change. Despite the shifting dynamics, there's a burgeoning demand for skilled professionals in AI, presenting avenues for lucrative career prospects even for those with limited experience. In today's competitive environment, the key lies not only in acquiring technical skills but also in strategically positioning oneself to capitalize on emerging trends and technologies. One such avenue is text analytics, a field ripe with opportunities as organizations seek to derive insights from vast amounts of textual data. Additionally, the concept of Gen AI, which focuses on the integration of AI technologies with human capabilities, opens doors to innovative roles across various industries. To embark on this journey, leveraging the plethora of free courses offered by tech giants like Google, IBM, and Microsoft can provide a solid foundation, equipping aspiring AI professionals with the knowledge and skills needed to ride the wave of AI-driven innovation.
Here are the 4 interesting AI things that I learned and enjoyed this week.
4 AI Things
Quantum computing could play a huge role in tackling climate change by handling complex problems like optimizing energy use and enhancing renewable energy storage. It offers a way to develop new materials and improve agricultural practices, potentially reducing deforestation and emissions from industrial processes. This technology could also revolutionize climate modeling and prediction, leading to more precise weather forecasting and better preparedness for climate-related events. Essentially, quantum computing could accelerate the development of clean energy technologies and optimize resources to combat climate change effectively.
Wayve, a UK based company, just got over $1 billion to boost its self-driving tech. They're using AI to make cars and robots drive themselves, planning to sell this tech to different car companies. Their system learns by watching real drivers, which is different from other self-driving tech that follows strict rules. They're also making their system work not just in cars but in other robots too. This huge investment is a big deal in the U.K. and shows that Wayve's tech is promising.
This week we gonna talk about decision Trees. Decision trees ask you yes/ no questions about data until they narrow down information well enough to make a prediction. Imagine each decision as a branch in a tree, starting from the root and moving outwards to the leaves, where each leaf represents a final decision or classification. This branching structure helps in breaking down a complex decision-making process into simpler, more manageable steps. The more complex the situation becomes the deeper and wider the tree becomes. It is crucial to note that not all decision trees are balanced. This means the model might perform well on the training data but poorly on new data, because it has learned the noise rather than the underlying patterns. They are also versatile, being applicable to both classification tasks (like determining if an email is spam or not) and regression tasks (like predicting house prices).
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Researchers have pulled off what sounds like sci-fi: they've managed to teleport quantum states nearly perfectly despite environmental noise. Quantum teleportation involves transferring a quantum state from one location to another without moving the physical object. This experiment used a complex method that lets quantum bits (qubits) interact in a way that bypasses usual disruptions like noise. The breakthrough could lead to advances in quantum computing and secure communications, proving we're getting closer to practical uses of quantum mechanics.
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