With current technologies like artificial intelligence, machine learning and data analytics being democratized in everyday applications, the IT industry has almost successfully transitioned into the Digital Era. When talking about digitization, one idea most industry leaders agree upon is that automation is the need of the hour. Along with other new age technologies, Robotic Process Automation (RPA) too has made a mark in the IT industry and according to Gartner 85 percent of big organizations will have deployed some form of RPA by the year 2022. While RPA is meant to bring about efficiency and reduce errors made in mundane and tedious work done manually by humans, it does give rise to the question of will there be a loss of jobs in the IT industry due to democratization of RPA. Automation, a popular emerging technology has an assured place in the future of global businesses.

The most buzzed-about disruptive technologies that are changing business landscapes today are Machine Learning (ML) and Artificial Intelligence (AI). Almost all of us have heard or read about them but do we actually know what the fuss is all about? The enterprises are trying to harness the explosion of digital data and computational power with advanced algorithms to enable collaborative and natural interactions between people and machines. However, there's still a lot of confusion within the public and the media regarding what is ML and AI. People prefer to write AL and ML technologies -- and not ML and AI -- and the argument goes that the former syncs well with the human mind.

Acorn AI is built on the Medidata platform, and as a Medidata company, will draw on the company's business development, sales and marketing resources, among others, said the company's newly-minted president, Sastry Chilukuri, who first joined Medidata in January of this year as executive vice president of digital and artificial intelligence (AI) solutions. Chilukuri told us Acorn AI is addressing two big trends: "The first is we're increasingly getting into a world of precision medicine, with CAR-T therapy, tissue engineering, gene and cell therapy, etcetera." The other is the advancement of the advancement of AI and new sources of data. Now, increasingly, Chilukuri said researchers have a 360-degree view of the patient, including clinical, genomic, molecular, as well as socioeconomic, behavioral, and environmental data, which is creating "an enormous opportunity around personalization," he explained. In order to fully capture this opportunity, Chilukuri said life science companies need to make data "liquid" to answer a few important questions.

Domino's Pizza has collaborated with blockchain AI platform SingularityNET to integrate AI algorithms and solutions in its Singapore and Malaysia operations. The US pizza chain aims to improve its supply chain processes and logistics in the two southeast Asian countries, conducting feasibility studies, running AI-focused workshops by incorporating advanced technologies, and developing new algorithms that will benefit business processes. Domino's has more 260 stores and 4,000 employees in Singapore and Malaysia and is expanding business rapidly. The SingularityNET system is claimed to greatly help both divisions enhance their growth and efficiency by including unique algorithms in its AGI ecosystem into its operations. Henceforth, both of these divisions of Domino's will use AGI utility token, which was created to enable some basic functions of SingularityNET's decentralised marketplace, namely, frictionless and publicly-auditable transactions between AI services, and the incentivisation of development for new AI services.

These developments are a natural outcome of the growing costs, inefficiencies, and sub-optimal outcomes plaguing AML operations at most banks. Regulators and supervisory agencies have started to take note and share generally positive views and feedback. A recent joint statement from supervisory agencies in the US is being regarded to be a catalyst for further promoting innovation and expediting adoption of AI and ML solutions in AML. There is great appetite among industry participants to learn about live use cases of AI being applied in AML, as well as the challenges and considerations when operationalizing AI. In a recent Celent report we analyzed in detail live AI solutions from eleven vendors representing a good mix of incumbent providers and industry newcomers, as well as a mix of solutions that span different parts in the KYC-AML value chain.

No startup is easy to grow, and with more and more artificial intelligence (AI) companies being founded around the world, the reality is that they can't all lead the way. Some will struggle along for years, unable to find any real traction, while others will perish completely shortly after they're funded. As a founder or marketer, you need to be able to effectively communicate your value proposition. People respond to great products, so it's crucial to understand that product is the new marketing. Only the strongest, smartest, and most creative will survive and thrive.

You might think that anthropology and AI is an odd pairing. But in a 2017 article in WIRED, journalist James Temperton wrote, "For DeepMind to realise its ambition of cracking general intelligence, it needs an interdisciplinary approach to AI". As DeepMind co-founder Mustafa Suleyman explained in the same article, "We need to have in-house the very best anthropologists, sociologists…specialists on bias and discrimination in machine learning systems, working with both our researchers and applied software development teams so that they can give them feedback and guidance and introduce them to new modes of critical thinking". So in the spirit of cross-disciplinary collaboration, around 300 technologists working in AI (machine learning, data science, robotics, AI) and anthropologists and sociologists from around the world will come together at the Watershed on Bristol's historic harbourside to discuss human-centred AI. One of the keynotes, Dr Julien Cornebise, is from Element AI.

Artificial intelligence (AI) and analytics are providing clinicians and researchers with actionable insights, from early detection to end-of-life-care, and by changing the way research is done and diagnoses are made. However, unlocking the data treasure trove is not a simple exercise for any healthcare organisation. With Asia-Pacific (APAC) expected to become the global leader in IoT spending according to IDC1, healthcare is unsurprisingly becoming increasingly connected in the region. However, it is this connectivity that adds complexity to the data challenge. Healthcare data is now growing at a rate of 48 per cent every year.

Peter Isackson is an author, media producer and chief visionary officer of Fair Observer Training Academy. Elon Musk's effective style of branding relies on making audacious promises most people can't understand and engaging in perennial teasing campaigns. The Daily Devil's Dictionary can always count on Elon Musk to provide it with new material. As a hyperreal celebrity with amazingly deep pockets, Musk has the rare privilege of being in a position to play games with ideas, language and even the law, in an exceptionally creative way. At least to the extent that creativity implies a loose sense of accountability.

You may have experimented with blockchain projects to drive transparency in your supply chain or efficiencies in cross-border payments. You may have live AI-driven applications in customer services or backend automation. You may even be considering migrating your core infrastructure into the cloud to increase storage efficiencies and speed. But have you considered how these technologies should be integrated with each other to drive your core strategy now, and not just the periphery or on a ten year horizon? The driver for innovation projects in incumbent organisations is largely to break down silos.