Google today announced the first cohort in its Google for Startups Accelerator: Voice AI, a 10-week program designed to pair startups with experts to help tackle product development, machine learning, and other technical challenges. The 12 companies selected will gain access to resources across Google's programs and products, Google says, as well as to its people and technology. The pandemic appears to have supercharged voice app usage, which was already on an upswing. According to a study by NPR and Edison Research, the percentage of voice-enabled device owners who use commands at least once a day rose between the beginning of 2020 and the start of April. Just over a third of smart speaker owners say they listen to more music, entertainment, and news from their devices than they did before, and owners report requesting an average of 10.8 tasks per week from their assistant this year compared with 9.4 different tasks in 2019.

Amidst all the fast-paced technological innovations, contact centres continue to be at the frontline of delivering customer experience. "Even though businesses have identified different mechanisms to reach out to users such as mobile applications, notifications etc, users still reach out to the call center. Case in point, even when you are able to book a cab in under two minutes through the app, you will want to reach out to customer care if there is a problem," shares Sourabh Gupta, Co-Founder & CEO, Vernacular.ai, an AI-first SaaS business enhancing customer experience through intelligent voice conversations. However, Sourabh points out that innovation for contact centres has been overlooked and that's why today they are unable to offer the same convenience that the business provides digitally through other mediums. This gap has come to the fore amidst the pandemic.

Masks and face coverings have been prevalent in many cultures and work environments for decades. But if you are reading this in the year 2021, we can read your mind -- you are thinking about the pandemic! Masks became a must-have accessory in our daily lives due to Covid-19. Analyzing people's faces has vast applications from retail stores to corporate campuses and experiential marketing. The question is how do we train robust AI models without having access to vast datasets of people wearing masks?

Seamless growth in Artificial Intelligence is redefining every component of enterprises operating around the globe. Things have become more comfortable for organizations as complex tasks are simplified and, retrieving and processing massive data volume has become effortless. As AI technology is evolving, AI-powered voice assistants are gaining a strong hold in the workplace. According to a report by Juniper Research, the number of devices that leverage voice assistants will be 8.4 billion by 2024, which will be more than the global population. Due to its ability in time management and enhancing productivity, businesses are eager to incorporate voice assistants in their workplace.

For the last 5-10 years, data science has attracted newcomers near and far trying to get a taste of that forbidden fruit. But what does the state of data science hiring look like today? TLDR: There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let's place more emphasis on engineering skills. As part of my work developing an educational platform for data professionals, I think a lot about how the market for data-driven (machine learning and data science) roles is evolving.

Like any good piece of high-tech hardware, NASA's Perseverance rover features an Easter egg hidden in plain sight. Since landing on Mars on February 18th, NASA has been sharing thousands of photos captured by the rover. And if you look close enough, as Space did, you'll catch a decal bolted to the top of its body. That decal depicts Perseverance and every single other NASA rover to successfully make it to the surface of Mars. This plaque I carry pays tribute to those who've gone before me, and to new possibilities ahead.

RL algorithms can be used to solve tasks where automation is required. However actual implementation is easier said than done. You can ease your pain by using TF-Agents, a flexible library for TensorFlow to build reinforcement learning models. TF-Agents makes it easy to use reinforced learning for TensorFlow. TF-Agents enables newbies to learn RL using Colabs, documentation, and examples as well as researchers who want to build new RL algorithms.

Automated decision-making tools are becoming increasingly ubiquitous in our world. As ML models become more widely adopted, special care and expertise are needed to ensure that artificial intelligence (AI) improves the bottom line fairly. ML models should target and eliminate biases rather than exacerbate discrimination. But in order to build fair AI models, we must first build better methods to identify the root causes of bias in AI. We must understand how a biased AI model learns a biased relationship between its inputs and outputs.

Artificial intelligence (AI) has revolutionized many technology areas. As a few examples, it has already been instrumental in improving and enabling voice recognition algorithms, digital assistants, advertisement recommendation engines and financial trading applications.[1] Significant investment is being made for further development of this promising new technology, with R&D spending on AI predicted to reach $57.6 billion by the end of 2021.[2] Along with these R&D efforts, companies are also trying to protect and monetize their AI inventions, in some cases opting to seek patent protection. From 2002 to 2018, the number of AI patent applications filed with the United States Patent and Trademark Office (USPTO) more than doubled, from 30,000 to 60,000.[3] These R&D efforts are no longer limited to software companies.

DALL-E from Open AI can generate images from text captions. It is very similar to GPT-3 and Image GPT and produces amazing results.