Interview with Alvin Kaule

The future of intelligence is artificial

2020 December 8th

In recent years, interest in AI and machine learning has exploded, technology is growing exponentially ,and there is now talk of a fourth industrial revolution with artificial intelligence, big data, and robots as significant drivers of change.

In fact, AI-based technology is already wide-spread, and we all experience it every day when we use search engines, social media, streaming services, and e-shops. Companies like Google, Amazon, Facebook, Netflix, and Spotify analyze users’ interests and behaviors to optimize products, services and marketing. AI affects us all every day.

The technologies can seem unmanageable and they are moving fast. It is difficult for the business community and organizations to prioritize and keep up—and what is it really that you’re chasing?

We asked Delegates Solution Provider Alvin Kaule for the down-low on artificial intelligence and what the future holds for AI.

1. What actually is artifical intelligence?

AI is everywhere, which is why you as a company, public authority, or organization simply must relate to. Similarly, we as individuals should think about how we relate to IoT and artificial intelligence in our own homes, devices, and in communication with others. It is now the big steps are being taken. Either you are with or you fall behind.

According to John McCarthy’s definition of artificial intelligence, the term covers “machines that can perform tasks that are characteristic of human intelligence.” In other words, the ability to reason, plan, solve problems, think abstractly, understand complex ideas, etc. However, no AI-

based solution has all these features at once, and it will probably be questionable whether the technology is ripe for it within the near future.

Machine learning can be considered as a method of achieving AI, where the idea is to give machines access to data so that they can learn themselves. Precisely the combination of deep learning and reinforcement learning are the technologies that come closest to AI today.

2. Where’s cutting edge technology at right now?

A lot is happening in the field of AI technologies right now. Among other things, we’re seeing rapid development in the evolution of driverless cars—led by Uber, Alphabet, Waymo, and Tesla, which most people already know as big players in the market. Additionally, many things about in the field of health, where for instance Stanford researchers have shown good results in detecting skin cancer. Other examples are speech recognition in smartphones and virtual assistants such as Siri and Alexa or the groundbreaking text generator GPT-3 from OpenAI, where it is difficult to distinguish whether it is a human or a robot who is the author.

Another advancement has taken place in AI technology combined with deep learning, where reinforcement learning has shown great potential. Here, DeepMind managed with AlphaGo to beat the world’s best Go players and later AlphaZero was developed, which without training data even learned to play chess and in less than four hours could beat the best chess engine so far Stockfish.

Another important development in AI is the development of tools and services that make it easier for companies to incorporate elements of AI into their own solutions. For example, access to services such as Cognitive Service from Microsoft and similar services from, among others, Google and Amazon.

The trend is a democratization of access to Machine Learning with AutoML Vision, which enables companies to create their own models for image recognition with a minimum of knowledge about machine learning. I believe this trend will continue, especially in the more common areas of application of Machine Learning.

All machine learning algorithms are based on data and methods, eg deep learning, which require large amounts of data. Therefore, access to data will be incredibly important for those companies that want to be successful with AI. In particular, access to good quality data will be critical for the successful use of AI. The majority of the AI projects that fail are due to poor data quality. The principle of “garbage in, garbage out” also covers machine learning.

3. Which businesses are benefitting from ai?

Companies today can already benefit greatly from using AI and machine learning to solve many challenges based on available data. Examples of the use of machine learning in eg sales and marketing are churn prediction, segmentation of customer groups and recommendations of products. In finance, fraud detection and trading are other widespread applications of machine learning.

The McKinsey Global Institute has conducted a study looking at which sectors are at the forefront of the field in the use of AI and which are lagging behind. Here it is clear that financial services and tech companies are leaders, and that a sector such as construction is far behind when it comes to investing in AI. Especially when it comes to future investments in the technologies.

Decision makers in the backward field will need to make informed decisions and understand exactly where AI can accelerate insight, innovation and business development. Shortly said; where can artificial intelligence lift earnings, create efficient processes and – at least as importantly – where it has no value!

In addition, it is also important to understand the difference and relationship between technical barriers and organizational barriers such as culture and maturity. To be in the forefront, one must understand some of the critical technological challenges that are helping to slow down the development of artificial intelligence and exploit the potential of AI, right where it provides the most value for your business.

4. What does the future look like?

I very much expect that AI will change the way we live today. In the not-so-distant future, we will, for example, experience self-propelled means of transport based on technology. Systems will become more intelligent and artificial intelligence will help to automate most trivial tasks and provide decision support for more complex issues, such as deciding on work injury cases, which today are based on human judgment. With the growing amount of data and the proliferation of sensor data, even more applications of machine learning will become more widespread. Data sources will continue to cross over each, and we must ensure that we can explain why algorithms arrive at whichever results they do.

In other words, we’ll be forced to solve the “blackbox problem.”

Furthermore, data security is also a topic that is increasingly relevant, and we should also address the ethical question of what we want to achieve with the use of machine learning. For example, the question many ask themselves today: Should all employees in a company be replaced by self-learning robots?

Interested in a talk about your possibilities?

At Delegate, we help clients get started with the technologies of the future. We can also help you assess how artificial intelligence can help optimize your business and make sure you get off to a good start, with projects that really make a difference.

Contact Alvin Kaule, if you want to hear more about your options. or +45 22929896

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