Artificial Intelligence (AI) probably is the most important technology development of this decade. Many energy executives believe that AI needs to be embraced, but don’t know exactly how.

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This article was originally published on freeCodeCamp, you can read the full article here.

In this article, I’d like to share with you the articles I found most interesting and insightful & inspiring last year and this year (so far). My other goal was to create a comprehensive list of the most valuable pieces for my Python students.

I bookmarked so many great resources that it was not easy to select the best ones. So I divided the article into 10 categories — this, by the way, resonates well with the versatile and multipurpose nature of Python.

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1. Investing in algorithms has become a no brainer for large companies

Neural networks, machine learning or deep learning have now assumed their role as a center piece of corporate jargon. After IBM, Google, Facebook or Amazon, large industrial companies do their best to attract the best talent or acquire startups that have built strong algorithms

2. Garbage In, Garbage Out

Meaning that flawed, or nonsense input data produces nonsense output or "garbage". Algorithms are not magic black boxes from which compelling insights arise. Verified, structured data is the right way to start. In many cases, data has to be verified by human experts who are assisted by powerful analytic tools. From there, the best algorithms can be optimized.

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Wouldn’t you be interested to achieve cost reductions, better customer experiences, productivity and revenue growth and working-capital optimization for your company? Artificial Intelligence (AI) or Machine learning (ML) could do that for you. An investment in ML can pay off, if you adopt the right approach.

For the general public, AI is synonymous with robots that someday might even take their jobs and self-driving cars. But at the enterprise level, AI means something different. 

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This article was originally published on freeCodeCamp, you can read the full article here.

I have over two decades of professional experience as a developer, I know a wide variety of frameworks and programming languages, and one of my favorites is Python. I’ve been teaching it for quite some time now, and according to my experience, establishing Python environments is a challenging topic.

Thus, my main motivation for writing this article was to help current and potential Python users to have a better understanding of how to manage such environments.

If you’ve opened this article, chances are that you already know what Python is, why it is a great tool, and you even have a Python installed on your computer.

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Artificial Intelligence and Machine Learning will have deep implications for the organization of the future. To some degree, every company will have to become a math house. This will require more than hiring new kinds of expertise and grafting new skills onto the existing organization. Many companies will need to substantially change the way they are organized, managed, and led. Every organization will have to make use of algorithms in its decision-making. The use of algorithms will have to become as much a part of tomorrow’s management vocabulary as, say, profit margins and the supply chain are today. And every member of the executive team will need to understand his or her role in growing the business.

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Drift is a young company founded in 2014 that aims to be similar disruptive like Uber was to the taxi business. Drift employs Machine Learning and Artificial Intelligence as well as High Frequency Trading to compete with the traditional utilities. 

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According to the Forrester 2018 predictions Report there is a digital crisis going on. At least 60% of the companies are lagging behind digital ransformation and 20% of CEOs will fail to act on digital transformation at all and put their firms at risk.

Digital transformation is not elective surgery. It is the critical response needed to meet rising customer expectations, deliver individualized experiences at scale, and operate at the speed of the market.

The results are sobering: Over 60% of executives believe they are behind in their digital transformation. Lagging results have created a loss of confidence in the CIO, driving up the number of chief digital officers and business units creating their own digital strategies. 

But that misses the point.

Digital transformation is a CEO issue and an economic question. Digital transformation is expensive; CEOs can’t drive operational savings fast enough to fund it and are cautious about destroying margins.

In 2018, CEOs must show the political will and, with the CIO and CMO, orchestrate digital transformation across the enterprise.

Some CEOs will use their balance sheet to acquire digital assets and buy time. But 20% of CEOs will fail to act: As a result, those firms will be acquired or begin to perish

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Conducting compliance in today’s complex and fast changing energy markets is a daunting task. Relying on your intuition and experience together with random data will not be enough anymore. Your company certainly owns an overwhelming amount of data that you could use in your compliance process. Humans and models are usually not good at handling too much information. Machines that can learn from massive amounts of data can help you make faster, better, and cheaper decisions and detect abnormal and susceptive behavior in an early phase.

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BlackRock, the world’s biggest investment group, is setting up a new center dedicated to research in artificial intelligence, underscoring the heightened interest among asset managers in how Machine Learning can revolutionize many facets of the investment industry. 

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The renewables industry should engage with the wider debates surrounding the use of Artificial Intelligence. Solar and wind developers, operators, and investors need to consider how their industries can use it, what the impacts are on the industries in a larger sense, and what decisions those industries need to confront. 

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AI Energizer, a joint venture between Maycroft and River Commodity just published a white paper about how we supported a major European energy company to implement an Artificial Intelligence solution for their trading activities. In the white paper, you can read how that energy company increased their profitability by 20% using a Robot Trader and all about the process to get there. You can download the white paper by clicking here.

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Artificial Intelligence will be transforming regulatory compliance for energy companies, as Machine Learning increasingly will be used for regulatory compliance to make sense of big, complex data. The use of AI can help your company improve the speed and efficiency to achieve compliance, making it much more robust, at lower risk and much cheaper than if done manually.

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There are some interesting similarities between introducing Machine Learning in your company and building a new house. When you would decide to build a new house you would not only have to buy the materials, but you also would have to hire the skilled talent who can get the job done.

That exactly is the lesson many senior executives, including their CIOs, around the world are learning about their plans to implement Machine Learning technologies that can analyze and improve performance without direct human intervention. Despite investing in Machine Learning most companies do not havethe talent, data quality and budgets to fully leverage the technology. So the question is how to have a successful implementation of Machine Learning in your organization?

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Making decisions in today’s complex and fast changing energy markets is a daunting task. Relying on your intuition and experience will not be enough anymore. Your company certainly owns an overwhelming amount of data that you could use in your decision- making process. Humans and models are usually not good at handling too much information. Machines that can learn from massive amounts of data can help you make faster, better, and cheaper decisions. It requires an open mind—and a willingness to change your relationship with technology —to take advantage of this new technique.

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An increasing embrace of Artificial Intelligence by the electricity sector may lead to a world where power generation, distribution, and transmission operations are automatically optimized, where the grid is balanced independently of any human interventions, where trading and arbitrage decisions are made in nanoseconds at a scale that only machines could tackle, and where end-users never have to worry about searching for a better supplier or changing the temperature manually.

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