Another simple, yet incredibly useful way deep learning is impacting healthcare is through the categorization of electronic health records (EHRs). Deep learning is already used in many natural language processing (NLP) programs, but EHRs present a uniquely complex problem, even for deep learning networks. Free text notes are often completed in a rush meaning they are messy, full of medical jargon, sometimes incomplete or filled out by multiple people, rendering them inconsistent. However, deep learning still represents the best method we currently have to analyze these records. A deep learning model developed by Google in 2018 was capable of predicting clinical outcomes, such as mortality and unexpected readmissions, better than traditional models after it had analyzed 216,000 patient EHRs across two hospitals.

The two most important sections of the GDPR for machine learning are Articles 13 and 22, which specifically spell out a data subject's rights in relation to automated processing. Article 22 states that'the data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.' GDPR essentially only allows profiling and automated decisions with the express consent of the subject, when expressly authorized by EU or member state law (including fraud and tax evasion detection), when required to ensure the security and reliability of a service provided by the controller, or when obligated to by a contract between controller and subject. Additionally, article 13 states that an individual has the right to a'meaningful explanation of the logic involved.' So even when permitted to carry out profiling, automated decision-making must ensure fair and transparent processing, use appropriate mathematical and statistical procedures, and measures must be established to ensure the accuracy of subject data employed in decisions.

These are undoubtedly positive developments. However, as in the Green Revolution, we could be so focused on producing the food required to feed people that we are forgetting to consider the societal impact it could have. Nearly 99% of farms in the US and across the world are family-owned, and the vast majority of these are small farms. Small farmers are incredibly vulnerable, operating with tight margins and in a complex environment in which they are at the mercy of numerous factors outside of their control, such as the weather. Technology as powerful as AI could help them, but it first needs to reach farmers in places where starvation is actually an issue at a price they can afford, so that the tools do not end up concentrated in the hands of a few. The impact on jobs also needs to be considered.

I don't think that the role of a data scientist will be under threat anytime soon. It is true that similar to everything else within tech, automation, and abstraction within ML will slowly climb to higher and higher levels. As a result, we'd need fewer data scientists and ML engineers in a few years for the same task compared to what we do. It's just that the total number of things that would need to be done using ML would continue rising up at a faster rate. As a result, I think that the total demand (and consequently the supply) for people with machine learning skills and background will skyrocket in the next decade.

There are very few areas that AI is not going to impact, and data visualization is certainly going to be among the most important. The problem is that Big data is outgrowing the basic dashboards and human data scientists that did data visualization in the past. There is simply too much data and too many insights held within it waiting to be revealed. Machine learning and advanced statistical algorithms are now necessary to keep churning them out them, and automated data visualizations to represent the findings. Natural Language Generation (NLG) is essentially just a variation on data visualization, which can either work alongside or independently of visuals to make it easier to interpret the information.

Robot process automation empowers knowledge workers, business advisors, and judgment based roll staff by allowing them to spend their precious time on customer centric business processes. According to the latest report, the global market of IT robotic automation was valued at US$ 183.1 million in the year 2013, and it is expected to see growth at a CAGR of about 60.5% between the year 2017 and 2020. Future leading enterprises will be those that know how to blend creatively these two effectively taking steps toward a virtual workforce. And, to that end automation delivers proven results.

Machine learning is increasingly being seen as the solution, dealing - or at least appearing to deal - with a number of the problems organizations are having implementing their cybersecurity initiatives. Former Department of Defense Chief Information Officer, Terry Halvorsen, believes that'within the next 18-months, AI will become a key factor in helping human analysts make decisions about what to do.' This point of view is being reinforced by significant investment in the field by the world's largest technology companies. MIT has been experimenting with it for some years, while IBM is training its AI-based Watson in security protocols and has now made it available to customers. Amazon also recently acquired AI-based cyber-security company Harvest.ai,

However, while few are going to quibble when it provides such benefits, many are also expressing concerns about its potential impact, whether it will leave any jobs for us or if we will all end up as pets to robots. And these are not restricted to crackpots, some of the world's brightest minds have warned against its dangers. Stephen Hawking has argued that, 'The development of full artificial intelligence could spell the end of the human race,' and he is not alone. Apple co-founder Steve Wozniak believes that, 'If we build these devices to take care of everything for us, eventually they'll think faster than us and they'll get rid of the slow humans to run companies more efficiently,' while Elon Musk has noted that'With artificial intelligence we are summoning the demon. In all those stories where there's the guy with the pentagram and the holy water, it's like, yeah, he's sure he can control the demon.

Advancements in artificial intelligence and natural language processing are allowing for more realistic and humanlike chatbot experiences. With a combination of greater computing power and enhanced deep learning algorithms, researchers have extended the layers of abstraction that can be processed by artificial neural networks. These networks are capable of allowing software to identify and understand and process patterns in data such as image, sound and text. For users of'griefbots', not only does this allow for greater understanding of their commands into the application, but also the output through the ability to imitate someone's personality through their digital legacy.

Companies need to understand the benefits and challenges that AI can bring and how to integrate it into their strategies, but does this really mean a CAIO is needed? Other technologies such as blockchain and IoT are likely to prove highly disruptive too in coming years, will they too need a senior leader who reports directly to the CEO? You don't want to hide AI in the bowels of other departments and it will need to be a centralized department, but at the same time you don't want your C-suite swollen to ridiculous levels, with everyone working at crossed purposes. If every new bit of tech has someone fighting its corner to the CEO, they will eventually get crowded out and nobody will have a say. A Chief Technology Officer's role is to take charge of new technology.