Data science projects fail, frequently. Between the end of 2017 and 2019 several published reports from Gartner, NewVantage, and VentureBeat AI showed that'failure' rates on data science projects are north of 75%. But I don't think this is indicative of how powerful the growth of data, machine learning, and AI has been for business (and likely all sectors of the economy) over the same timeframe. Back-of-the-envelope machine learning is inconspicuously powering business today (2020). A premortem is a thought exercise to predict or foresee why an analysis or project might fail.

Deloitte has acquired SFL Scientific, a leading AI strategy and data science consulting firm. The move is especially interesting as SFL helped NLP-driven CLM company LinkSquares to get to grips with machine learning. In a fascinating blog post for Amazon Web Services (AWS), Michael Luk, chief technology officer at SFL Scientific, wrote: 'LinkSquares built its initial prototype using SQL running on AWS, but it wasn't using any machine learning technology. 'We learned about the team's vision to process terms and language automatically and understood the business pain points. We proposed building a custom machine learning solution to help the team scale and improve accuracy.'

The Iberostar Group has announced its ambitious plan to install artificial intelligence in its more than 100 hotels globally to become zero waste, reduce food waste and save more than 1,600 tons of food in the first year. This commitment by the company will become a reality thanks to an innovative system, based on technology from the Winnow company, which will contribute to ending food waste and bringing the company closer to its goal of being free of waste sent to landfill by 2025. The Iberostar Group committed itself to this zero-waste objective in 2020 with its own 2030 Agenda, which also includes being carbon neutral by 2030. According to Sabina Fluxá, Vice-Chairman & CEO of Iberostar Group, "Reducing food waste is key to meeting our 2030 Agenda goals. The value of food cannot be underestimated, and, at Iberostar, we want to ensure it is not wasted. In addition to training our employees to address food waste, we have put in place this innovative system to reduce the amount of waste we produce without affecting the guest experience. We are convinced the use of state-of-the-art technology, training, and innovation dedicated to removing food waste will help us reduce climate impacts, achieve our goals, and contribute to broader global goals that benefit the planet."

This week I'm targeting to finish at-least day 7 of Deep learning in my free time post work and over the weekend.

Matrimony platform Betterhalf.AI announced that it recorded a growth of $2 million in annualised revenue in FY22 with 50% quarterly growth, fuelled by its key matchmaking service. While the matchmaking service drives 70% of the company's revenue, non-matchmaking services like wedding decorations, photography, loans, verification, and astrology drive 30% of its revenue, as per Betterhalf.ai. "The continued momentum of our company speaks the market opportunity we are encashing. Our impressive growth concurs with the trust of our users who have invested in us and we look forward to continuing delivering rich CX and driving ongoing revenue momentum," Pawan Gupta, Co-founder of Betterhalf.ai. Apart from the revenue growth, the app also recorded a 250% growth in its team size by adding 75 full-time employees in six months, the firm said, adding that it is working aggressively to touch $3 million in annualised revenue by the end of FY23.

Finding an ideal, compatible life partner is a struggle. Pawan Gupta and Rahul Namdev, MIT Alums and co-founders of Betterhalf, learned it the hard way. Both were searching for their life partner in 2016 when they were corporate employees. That's when they thought of launching Betterhalf – A matrimony app that can work as a matchmaker for Indians without any interference from relatives and employees. Betterhalf uses the power of Artificial Intelligence to help people meet compatible matches with whom they can spend their life.

The agricultural sector is struggling as fewer and fewer people want to work in this sector, which means that agriculture and horticulture have to consider automation. An increasing number of robots in the agricultural sector are equipped with VBTI's technology. With close to twenty years of experience under his belt, founder Albert van Breemen also helps companies in the manufacturing sector. Albert tells us more about it in this instalment of Start-up of the day. "I have been working on artificial intelligence (AI) since the early 1990s and control engineering. Once I started my studies, I soon came across what is now called Deep Learning. During my time as a business developer at ASML, there was all this hype surrounding artificial intelligence. I heard from a lot of people that we had missed the boat. Everything to do with AI was already being done in America and China. We were supposedly lagging behind with that technology. However, in my mind, we had not missed the boat at all; there's just another one on the way. At some point I received a question from a customer. That customer wanted to scale up their production, but every product had to be checked by hand. That takes an enormous amount of time, and even finding the right people to do this kind of inspection work was almost impossible. That's where Deep Learning enters the picture, for instance, with the help of smart camera systems. And that's how I came up with the idea of turning it into a business. Eventually, in 2018, I decided to start my own company in the field of Deep Learning – which is used within the High Tech Industry."

According to the US Environmental Protection Agency, 11 percent of 2020 greenhouse gas emissions came from agriculture efforts from livestock such as cows, agricultural soils, and rice production. This means that we have a desperate need to change how we produce our food. Silicon Valley startup IronOx has been busy doing just that by using automation. It has moved crops indoors, used robots to manage them, and put them under the watchful eyes of smart cameras. To grow more and better efficiently and sustainably, according to an article by CNET published on Saturday.

Provided by ScienceAlert Storm clouds over a country road The vast number-crunching capabilities of artificial intelligence systems mean we can better predict the future of chaotic systems based on fewer and fewer patterns of the past – and a new algorithm is adding even more accuracy to the process. Developed through next-gen reservoir computing techniques, which take a more dynamic, speedier approach to machine learning, the new algorithm improves predictions of complex physical processes such as the global weather forecast. Calculations of these processes – known as spatiotemporal chaotic systems – can now be done in a fraction of the time, with greater accuracy, using fewer computational resources, and based on less training data. "This is very exciting, as we believe it's a substantial advance in terms of data processing efficiency and prediction accuracy in the field of machine learning," says physicist Wendson de sa Barbosa, from Ohio State University. Machine learning is exactly that: computer algorithms using a discovery process to make predictions (such as future weather patterns) based on large data archives (such as past weather patterns).

A few weeks ago I demonstrated how an artificial intelligence could now mimic skills and abilities that were once the exclusive purview of humans, like creativity. And while the detective story that an AI wrote alongside me was a little bit on the strange side, it did demonstrate what might one day be possible as the technology continues to evolve. But that also means that we need to be careful, because AIs could very easily evolve into areas that we may not like, or with morals that are counter to our belief systems. The White House has at least tentatively acknowledged this danger, calling for the establishment of an AI Bill of Rights through its Office of Science and Technology Policy. That effort is designed to democratize AI as much as possible, letting the public not only see the state of AI as it develops, but also giving people a voice in how and what AI technology develops.