This week, AI software: Unlocking the value of data to generate stunning new insights.
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What is a Quantum Computer?
Quantum computer is an emerging technology that will have a major impact
Quantum computing and AI can be combined to have an even greater impact. The goal of this blog post is to introduce basic concepts of quantum computers and try to demystify them. Quantum computers use properties of matter observed at the micro (sub-atomic) level to perform computations and solve problems.
In contrast, current computers (since the 1950s) use properties of matter at the macro (semi-conductor element) level. Progress in quantum computers will have significant implications in the near future. It could render current encryption standard of the Internet useless, reduce drug development time, transform AI and much more. Quantum computers have already demonstrated (e.g., Shor’s algorithm) exponential speedup compared to their non-quantum (aka classical computer) counterparts. This post covers the following at a high level.
- Physics behind quantum computing
- How/why it will outpace silicon based computing
- What problems will it solve better
Cultural Intelligence (CQ) Is An Important Predictor Of Success. Here’s How To Boost Your CQ
What is cultural intelligence, and why does it matter?
I work with brands from all around the world, and I’ve noticed that organizations are – thankfully, finally – becoming much more diverse. Perhaps you’ve noticed the same thing. Perhaps your own colleagues and clients increasingly represent a broader range of cultures, ethnicities, economic statuses, and so on.
It makes sense, then, that cultural intelligence is fast becoming a highly desirable attribute in the workplace. But what exactly is cultural intelligence, why does it matter for workplace success, and how can you become more culturally intelligent? Read on to find out.
What If Consumers Do Not Need The Metaverse?
Big tech and the emerging tech scene has a lot at stake to try and make the metaverse and metatech a success – whether consumers have a need for it remains to be seen.
In the countless numbers of articles written about the metaverse ever since Facebook rebranded itself as Meta, we have learned a lot about what the metaverse and metatech can offer. But one question has remained unanswered to date, which will largely dictate the metaverse’s success – or its failure: what consumer needs does it address?
Searching For The Metaverse’s ‘Killer Feature’
The metaverse and metatech are certainly eye-catching on paper and attract an outsized lot of attention, not the least because of its economic potential. But do consumers actually care? Is there even a need specific to what the metaverse has to offer? Many ground-breaking technologies eventually fail to make it to mass market due to a lack of customer interest, and the fact that they do not address consumer needs.
What is reinforcement learning? How AI trains itself
What are some useful open-source options for reinforcement learning?
Machine learning (ML) might be considered the core subset of artificial intelligence (AI), and reinforcement learning may be the quintessential subset of ML that people imagine when they think of AI.
Reinforcement learning is the process by which a machine learning algorithm, robot, etc. can be programmed to respond to complex, real-time and real-world environments to optimally reach a desired target or outcome. Think of the challenge posed by self-driving cars.
3 Reasons Why You Need Lowcode Platforms For DataScience Solutions
Low-code ML applications help address the challenges of model maintenance, time-to-market, and talent shortage
Organizations across industries are turning to data and analytics to solve business challenges. A survey by New Vantage Partners found that 91 percent of enterprises have invested in AI. However, the same study found that just 26 percent of these firms have AI in widespread production.
Organizations are struggling to solve business challenges with AI. They find that building machine learning (ML) applications takes time and requires expensive maintenance and talent that’s in short supply. Leaders say that over 70% of data science projects report minimal or zero business impact.
Here’s how low-code ML platforms can help tackle these challenges.
Artificial Intelligence (AI) everywhere has the potential to transform every business and improve the life of every person on the planet. In fact, every day we hear about AI breaking new ground, from detecting cancer and playing Minecraft, to creating “sentient” chatbots and generating compelling art. The goal of AI is simple: To accelerate “data to insights.” We have seen tremendous progress in the basic AI ingredients — the exponential growth of “data, compute and algorithms.”
Data, as measured by the total number of bytes, is in zettabytes. Compute, as measured by hardware execution capacity of operations per second, is in petaflops to exaflops, and algorithms, as measured by the number of parameters in a neural network, have exceeded a trillion.