In this special guest feature, Chris Heineken, CEO and Co-founder of Atrium, suggests that as Machine Learning (ML) is growing in the IT and cloud space, understanding how to best utilize its capabilities will change the approach to implementing new IT investments. As CEO of Atrium, Chris leads a world-class team in empowering companies to embrace the next generation of tech through the power of AI. Prior to founding Atrium, Chris was the COO at Appirio where he was responsible for leading the Company's global consulting, sales, and operations teams. Chris started his career with Accenture and later founded Bay Street Solutions, a CRM/Siebel consulting firm, acquired by Perficient. He earned his undergraduate degree from UC Davis and MBA from UC Berkeley.

Practical adoption of artificial intelligence (AI) faces a variety of roadblocks--splashy, high-profile deployments of AI have not been received well, with Microsoft's "Tay" bot on Twitter parroting anti-Semetic vitriol just 16 hours after launch. Similarly, Amazon's AI-powered hiring process displayed bias against women and the company marketed unreliable facial recognition technology to municipal law enforcement. AI often reflects the biases--including, and especially, unconscious biases--of the designers, which would make Facebook attempting to build an AI with an "ethical compass" a concerning prospect, given the multitude of other problems the social network has experienced. This is a problem that necessarily requires diversity of thought, according to Northeastern University's Ethics Institute and professional services firm Accenture, which published a guide to building data and AI ethics committees. Such committees are, by definition, not achievable by pooling together people of similar backgrounds to debate the merits of AI design.

It wasn't that long ago that Sophia Withers was travelling around Australia and Asia after her undergraduate degree. After a stint working on farms and in brunch cafes, she moved to Melbourne and joined KPMG Australia. While working as a coordinator for their audit division, she began studying the University of Birmingham's part-time Online MSc International Business, in 2018. It was during the program that she deep dived into her passion for blockchain and emerging technology. Her degree research project--How will Blockchain 3.0 facilitate social and economic impact?

Arka Dhar is the CEO at Zinier, an intelligent field service automation platform. Driving north on the 101 from Silicon Valley to San Francisco, there are dozens of billboards promoting new artificial intelligence (AI) ventures. According to PricewaterhouseCooper and CB Insights (via Bloomberg), venture capitalists poured a record $9.3 billion into AI startups last year, and this is just the beginning. For many business leaders, AI is still a buzzword. This largely stems from an obsessive marketing regime that hypes up technologies like AI, machine learning (ML) and the internet of things (IoT). But behind the billboards and eye-watering funding rounds are industry-disrupting technologies, and understanding their potential impact as well as how to get executive buy-in are pivotal to the future of work.

Driving north on the 101 from Silicon Valley to San Francisco, there are dozens of billboards promoting new artificial intelligence (AI) ventures. According to PricewaterhouseCooper and CB Insights (via Bloomberg), venture capitalists poured a record $9.3 billion into AI startups last year, and this is just the beginning. For many business leaders, AI is still a buzzword. This largely stems from an obsessive marketing regime that hypes up technologies like AI, machine learning (ML) and the internet of things (IoT). But behind the billboards and eye-watering funding rounds are industry-disrupting technologies, and understanding their potential impact as well as how to get executive buy-in are pivotal to the future of work. During initial meetings I've had with decision makers, it's been tremendously helpful to demystify AI from the start and translate it into language that aligns with business goals.

The amount of data available to organizations every day continues to proliferate at a staggering volume. But technologies such as analytics and artificial intelligence (AI) have the potential to help businesses make better use of these massive volumes of data. In an age of collaboration between humans and machines--what we call the "Age of With"1--organizations can gain advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making. But not every organization is optimizing the opportunities available in the Age of With. Some do little or nothing with data to aid their decision-making. Others carry out analytics projects in pockets of the business.

Under enormous pressure to generate growth, today's C-suite is adopting technology that spawns new capabilities and applications. But many still struggle to scale innovation company-wide. It's creating what we call the innovation achievement gap--the difference between technology innovation investment and realized value. Value is difficult to capture in part because the conventional IT "stack"--spanning software applications, hardware, telecommunications, facilities and data centers--wasn't built for today's world of analytics, sensors, mobile computing, artificial intelligence applications, the Internet of Things (IoT), and billions of devices. Nor was it designed to adapt to the world of tomorrow, whatever that might be. But it's not the case that digital native companies are closing their value achievement gaps, while legacy companies aren't.

Artificial intelligence is here, but businesses have barely scratched the surface of the impact it can have. Instead of supercharging their growth, some companies are finding themselves stuck with proof-of-concept experiments that end up going nowhere. Leaders need to create the right company culture to inspire, build and empower a workforce fit for the AI age. While the'how' remains a challenge, the emerging problems are actually the'why' and the'who'. Leaders see attracting the right talent and competing investment priorities as the two top barriers to AI adoption.

The Deloitte Cognitive Services Platform (DCSP), which is in the "closed beta" stage, will run in the Google Cloud and use a range of technologies and functions involving AI and machine learning offered by the internet company. Deloitte said that, in its platform, the consultancy will use its expertise in business transformation and professional services to make AI more accessible to organizations and make sure it can be deployed for various business purposes. Peter Fach, a Deloitte Consulting partner and co-founder of the platform, said the use of the Google Cloud will allow the consultants to develop AI-based services that are scalable and can be implemented at big and small companies across all business segments. "Our platform and services offer a new form and a totally new way of offering professional services," Fach said in a press release. DCSP will offer a proprietary, cloud-based service offering called "Deep Process," which lets companies automate the analysis and understanding of documents.

Driven by growing interest in artificial intelligence (AI), the global artificial neural network market is projected to grow from $117 million in 2019 to $296 million by 2024, for a compound annual growth rate (CAGR) of 20.5%, according to a study Artificial Neural Network Market by MarketsandMarkets. The study said the major factors fueling the market growth include the increasing demand to train large volume of data sets with low supervision to drive the market, and growing need for enhanced processing power, learning ability and speed of neural networks to drive the growth of the market. According to the study, the artificial neural network market is segmented into two categories, managed services and professional services. The professional services category includes the consulting services segment. This sector is expected to grow at a rapid pace during the forecast period as consulting services help organizations to utilize ANN tools capabilities that uses graph structures for semantic queries with nodes, edges, and properties to represent and store connected data.