A couple of years ago I wrote about Google's AI camera, Google Clips ($250). This is a device that you plonk on the kitchen table (or hang around your neck), and which automatically takes photos whenever your favourite landscape, child or pet steps into frame. I compared it with a Nikon DSLR ($3,300) and proclaimed that AI devices would consume the lower end of the market while creative photographers would cling on to their interchangeable lenses. On reflection I'm not surprised by the news this week that Google Clips has been withdrawn. As we all know, AI relies heavily on machine learning, which requires huge volumes of data and experiments to accurately predict everything from eye disease to your next favourite artist on Spotify. Google tried to teach its camera about composition, subject focus and other skills using photo libraries but even then the results were disappointing.

When a dishevelled zealot holds up a placard on a packed street, shouting "the end is nigh", they are at best politely ignored. Yet when the media does it, people buy newspapers. So it should be no surprise that the narrative of an imminent jobs apocalypse at the sleek, chrome hand of automation should have been so successful in recent years. Some businesses profit from threats, which perhaps explains the origin of the robots-taking-our-jobs hysteria: consultancies publishing a succession of breathless reports into the coming age of AI, which conveniently enough can become a profitable opportunity if you pay for their expensive digital transformation services. The reality is that, as with any other attempt at long-term prognostication, we don't really know to what extent automation will disrupt or indeed destroy the job market as we know it. The future is inherently unpredictable, although it would be odd to dismiss the clear trend of the last ten, 40 or even 200 years (depending on which industrial revolution you want to start with): we are automating more.

The global machine learning (ML) industry is increasing at a compound annual growth rate of 42% and will be worth $9 billion toward the end of 2022.

Recognizing that organizations are slow to adopt AI, due in part to rising data complexities, IBM (NYSE: IBM) today announced new innovations that further advance its Watson Anywhere approach to scaling AI across any cloud, and a host of clients who are leveraging the strategy to bring AI to their data, wherever it resides. "We collaborate with clients every day and around the world on their data and AI challenges, and this year we tackled one of the big drawbacks to scaling AI throughout the enterprise – vendor lock-in," said Rob Thomas, General Manager, IBM Data and AI. "When we introduced the ability to run Watson on any cloud, we opened up AI for clients in ways never imagined. Today, we pushed that even further adding even more capabilities to our Watson products running on Cloud Pak for Data." Increasing data complexity, as well as data preparation, skills shortages, and a lack of data culture are combining to slow AI adoption at a time when interest in AI continues to climb.

European Government Organizations Are Enthusiastic About Artificial Intelligence but Face Challenges Adopting It, According to Accenture Study DUBLIN; Oct. 23, 2019 – Public-service executives in Europe are optimistic and enthusiastic about the impact of artificial intelligence (AI) on government operations and services but face challenges implementing the technology, according to a study issued today by Accenture (NYSE: ACN). The study -- based on a survey of 300 government leaders and senior information technology (IT) decision-makers in Finland, France Germany, Norway and the U.K.-- found that the vast majority (90%) of respondents believe that AI will have a high impact on their organizations over the coming years. In addition, nearly the same number (86%) said that their organization plans to increase its spending on AI next year. Customer service and fraud & risk management are the two operational areas favored most for public service AI deployments, cited by 25% and 23% of respondents, respectively. In addition, respondents most often cited increased efficiencies, cost or time savings, and enhanced productivity as the greatest anticipated benefits from their AI investments.

Research shows that consumers are starting to prefer self-service to talk to a customer service agent. Inspired by this, smart brands bring their customers on their bot journey to deliver a more effective and efficient digital customer experience. The Wall Street Journal uses Facebook Messenger to launch its AI-powered bot. Now, this leading portal enables its users to stay on top of big news and stock quote easily. Through giving simple commands, it also shares key financial metrics, company information, and live stock quotes.

Artificial intelligence is the most exciting upcoming technology in the world today.

This isn't a movie script or a futuristic scenario – this is all happening right now thanks to the power of Natural Language Processing (NLP)! I honestly feel the number of breakthroughs happening in this field is unparalleled. The past two years have been a blur – the Transformer architecture, introduced in 2017, has truly transformed the NLP space. From the super-efficient ULMFiT framework to Google's BERT, NLP is truly in the midst of a golden era. Are you ready to be part of this revolution?

Music genre is arguably one of the most important and discriminative information for music and audio content. Visual representation based approaches have been explored on spectrograms for music genre classification. However, lack of quality data and augmentation techniques makes it difficult to employ deep learning techniques successfully. We discuss the application of graph neural networks on such task due to their strong inductive bias, and show that combination of CNN and GNN is able to achieve state-of-the-art results on GTZAN, and AudioSet (Imbalanced Music) datasets. We also discuss the role of Siamese Neural Networks as an analogous to GNN for learning edge similarity weights. Furthermore, we also perform visual analysis to understand the field-of-view of our model into the spectrogram based on genre labels.