How did farming affect your day today? If you live in a city, you might feel disconnected from the farms and fields that produce your food. Agriculture is a core piece of our lives, but we often take it for granted. The world's population is expected to grow to nearly 10 billion by 2050, increasing the global food demand by 50%. As this demand for food grows, land, water, and other resources will come under even more pressure. The variability inherent in farming, like changing weather conditions, and threats like weeds and pests also have consequential effects on a farmer's ability to produce food.

Around 1900, a German farmer made an extraordinary claim: he had taught a horse basic arithmetic, and even to read and spell! Indeed, in public demonstrations, the horse, called Clever Hans, was able to answer all of its owner's questions correctly, by a sequence of taps of its hooves. Oskar Pfungst, a psychologist of the time at the University of Berlin, did not quite believe the animal really possessed such human-like capabilities and designed a set of experiments to prove the farmer's claim wrong. In 1907 he published his famous report on the matter, making a surprising observation: Clever Hans would only give the correct answer if the answer was known by the experimenter asking the question! The horse had not actually learned to solve the question by calculating or reading, but rather it recognized subtle cues in the experimenter's body language to figure out what answer was expected of it.

Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use. While machine learning has a variety of use cases and the capability of deep analysis it is not without limitations. With large data requirements coupled with challenges in transparency and explainability, getting the most out of machine learning can be difficult for organizations to achieve. Understanding these realities of machine learning and establishing realistic expectations is the only way to put your organization in a stronger position. Data is the core of machine learning. The very nature of machine learning is to train an algorithm on clean and prepared sample data.

The concept of "edge computing" has been around since the late 90s, and typically refers to systems that process data where it is collected instead of having to both store and push it to a centralized location for off-line processing. The aim is to move computation away from the data center in order to faciliate real-time analytics and reduce network and response latency. But some applications, particularly those that leverage deep learning, have been historically very difficult to deploy at the edge where power and compute are typically extremely limited. The problem has become particularly accute over the past few years as recent breakthroughs in deep learning have featured networks with a lot more depth and complexity, and thus require greater compute from the platforms they run on. But recent developments in the embedded hardware space have bridged that gap to a certain extent and enable AI to run fully on the edge, ushering a whole new wave of applications.

In recent years, many research teams worldwide have been developing computational techniques inspired by the human brain, such as deep learning algorithms. While some of these techniques are considered highly promising for a wide range of applications, conventional hardware does not always support their computational load and thus can limit their performance. A possible solution for overcoming the limitations of existing hardware and ensuring that brain-inspired computational techniques achieve optimal results entails the creation of new electronic components that better reflect the structure of the human brain. A class of neuro-inspired computing chips is specifically designed for artificial intelligence (AI) applications that mimic the neural structures in the brain of humans and other animals. Researchers at Tsinghua University in China have reviewed recent advancements in the design of neuro-inspired computing chips to gain insight on the progress made so far and identify challenges that still need to be overcome.

Introduced in 2017, the Transformer is a deep learning model designed for NLP. Like recurrent neural networks (RNNs), Transformers handle sequential data. However, unlike RNNs, due to the attention mechanism, Transformers do not require that the data be processed in a sequential manner. This allows for much more parallelization in Transformers (in comparison to RNNs). In turn, parallelization during training allows for training on larger datasets.

The above are excerpts from a long reply to a few questioning letters written by nine eminent philosophers from Massachusetts Institute of Technology, Harvard, Cambridge University and others. These letters asked questions like: Can artificial intelligence (AI) be truly conscious--and will machines ever be able to "understand"? How does technology interact with the social world, in all its messy, unjust complexity? How might AI and machine learning transform the distribution of power in society, our political discourse, our personal relationships, and our aesthetic experiences? The questions were addressed to the most recent arrival in the world of AI, called GPT-3.

Younghoo Lee is a Senior Data Scientist at Sophos. Together with Joshua Saxe, Sophos Chief Scientist, he recently presented these findings at DEFCON 28 AI Village. Business Email Compromise (BEC), is a form of targeted phishing where attackers disguise themselves as senior executives to dupe employees into doing something they absolutely shouldn't, like wire money. It started out as an evolution of the fraudulent international money transfer scams, and the messages were often riddled with poor punctuation and grammar, misspelt names and more that made them relatively easy to identify. Yet they still made money.

"Machine learning is to businesses today what petrol engines were to horses back then." For this week's ML practitioner's series, Analytics India Magazine got in touch with Eugene Khvedchenya from Ukraine. Eugene is a Kaggle master and is currently ranked 104 on the global leaderboard. He has more than 10 years of experience in developing computer vision applications, and in this interview, he shared few valuable insights from his decade long journey. Eugene started programming from a young age ever since he saw his father assembling Orion 128 PC, a popular DIY PC in the early 90s.

KPMG and the CCFA identified high-growth retailtech enterprises that strive to advance digitalization, intelligence, and integration for the retail industry. Malong Technologies, a leading computer vision technology provider, was selected for delivering significant value to retailers by reducing shrinkage (inventory loss) and improving the customer experience at in-store self-service systems. Using the KPMG proprietary Startup Insight Platform (SIP) and a team of experts, 50 leading enterprises specializing in retailtech with major deployments in the Chinese retail market were selected through a quantitative analysis on six different aspects including the team, technology, product, market, business model and funding. The winners were announced at the annual CCFA International Retail Innovation Summit held in Shanghai. As a global leader in AI for product recognition, Malong Technologies has been committed to providing smart retail solutions to retailers worldwide.