AI & Machine Learning

With the attention for AI growing, also the call for ethical AI is growing. This is not surprising seeing the many problems we have encountered already. The problems can arise when we rely too much on unaccountable AI. These problems exist due to biased algorithms that are trained using biased data and developed by biased developers. High-quality, unbiased data combined with the right processes to ensure ethical behaviour within a digital environment could significantly contribute to AI that can behave ethically. Since this is difficult to achieve, the European Union published a set of guidelines on how to develop ethical.

Steady advances in artificial intelligence and natural language processing have made digital assistants increasingly capable of performing complicated voice commands under different circumstances. But does it mean that our digital assistants are ready to escape the confines of smartphones, smart speakers and computers and a bunch of weird gadgets? The only way to make smart assistants really smart is to give it eyes and let it explore the world. While the idea of putting a face on the voices of digital assistants sounds appealing, the truth is that with today’s AI technology, such an idea is doomed to fail.

The world of big data, machine learning (ML) and AI have developed rapidly over the last 5 years with new technologies, processes and applications changing the way organisations are managing their data. There is a good barometer of what the state-of-the-art is in big data manipulation as well as the concerns of developers and users.  AI and machine learning combined with ever-increasing amounts of data are changing our commercial and social landscapes. A number of themes and issues are emerging within these sectors that CIOs need to be aware of.

Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive.  It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful. It’s important to start with understanding what your Board of Directors does and doesn’t do. While Board members may be very senior in their experience of your and other industries, they may not be as well informed as you and senior management on new technologies and how to exploit them.

When AI infiltrates our corporate and government networks, IT Service Management (ITSM) organizations will be responsible for keeping these systems up and running. With enormous workloads, limited budgets, and an influx of IoT, ITSM departments are struggling to keep up with demands. To help service management organizations keep up, we will see ITSM solution providers incorporate AI into their solutions as a way to improve efficiency while reducing costs. We should expect AI technology to disrupt current ITSM technology in three key areas.

Are you interested in Machine Learning?  Are you asking yourself which are the key skills within this profession? If you are interested in machine learning, you are not alone. In fact, machine learning is one of the hottest fields right now and more and more people get interested in it everyday. This blogpost will tell you about the most valuable skills within the field and what you really need to have in your arsenal to call yourself a machine learning engineer.

If bots could learn, they would require less up-front effort in RPA deployment. Thanks to advances in applied artificial intelligence (AI) and machine learning algorithms that have the ability to detect patterns and make predictions and recommendations, bots do not have to receive precise programming instructions to adapt to changes in business processes. Bots will be able to be used to automate a far wider range of business processes than is currently possible, which could drive demand for the technology.

In a data-driven business world it’s clear that machines are beginning to play, and will play, an ever-larger role in C-suite decision making. The best leaders of today and tomorrow will no longer rely on the instincts of a few decision-makers and will instead use insight driven by machine and deep learning solutions. With new competitors changing the market at rapid pace, companies seeking to achieve ‘superstar’ status and dominate the top of the profit and value ladder will need AI to guide the way forward.

Machine learning not only saves time on building a fraud detection routine but also can remove bias against certain taxpayers if done properly. If supervised learning is fishing where people have fished before, then another type of machine learning—unsupervised learning—is fishing where no one has fished before. Both supervised and unsupervised approaches provide tremendous value for government tax authorities, especially when used upon complex data sets like tax returns, financial transactions, taxpayer contacts, accounts receivables, network traffic, and even employee activities.

Efforts are underway among the ERP vendors to consider more innovative ways to handle the massive data demands of ERP and in fact surface more obvious value from ERP through machine learning and ERP algorithms. AI-enabled ERP can ultimately mean more intuitively surfacing access to all ERP data services through the methods that users would feel most comfortable using. Moving ERP systems from reporting on historical activity and getting them to be helpful with predicting and forecasting behaviour and outcomes become much more of a likelihood when AI and machine learning is applied to ERP systems.

There is great potential in leveraging AI and deep learning to help with the tax process. Tax fraud has existed in many forms for year, but it has become particularly widespread with the huge increase in identity theft. Taxpayers and tax agencies want transparency. Compared to other types of data-driven analysis, the amount and quality of the data is more important with deep learning models. While the picture of deep learning for tax preparation sounds bleak and ominous, there are places in tax administration where deep learning is appropriate and beneficial.