Deep learning will play a key role in the future of businesses


  • Deep learning neural networks mimic the decision-making processes of the human brain by performing a series of calculations to reach a conclusion.
  • Machines can process massive amounts of data that humans cannot process, but strong governance structures are needed to ensure positive results.
  • Deep learning can improve productivity, increase retention, and generate revenue if businesses use data to their advantage.

When you “solve” a problem or a question that requires a decision, it probably feels like you are following a linear checklist. But that’s not how the human brain works; it processes according to a non-linear model. And that’s basically how deep learning, a subset of artificial intelligence (AI), works.

Deep learning works like the human brain

Deep learning, in essence, learns from examples, much like the human brain. It is to mimic the way humans acquire certain types of knowledge. Since deep learning treats information the same way, it can be used to do things people can do, for example, learn to drive a car or identify a dog in a photo.

Deep learning is also used to automate predictive analytics, for example to identify customer buying trends and patterns so that a business can gain more customers and keep more. Know those sections on retail sites that show “frequently bought together” items when you buy a new screwdriver? These are based on predictive deep learning algorithms that have taken into account both your current research and your past buying habits to suggest additional products that you may also need.

Other applications include many daily dating and activities, such as virtual assistants, fraud detection, language translation, chatbots and service bots, colorization of black and white images, facial recognition and disease diagnoses.

A simple example of a neural network application is speech analysis. The network extracts sounds from the raw audio, which combine to form syllables, which combine to form words, which combine to form sentences that prompt actions. The machine learns that this particular sound means it has to go up a credit card balance and the more you ask the same, the more precise it becomes.

Deep learning has applications in all industries

Neural networks are not new; they have been around since the 1940s. In 1943, two computer scientists introduced models of neural networks, recreated neural-based threshold switches, and showed that even simple networks of this type are capable of computing almost any logical function. or arithmetic.

The first computer precursors were developed by a computer scientist tired of calculating ballistic trajectories by hand. Now, over 70 years later, deep learning has exploded in sophistication and use, primarily due to increased computing power (with dramatically reduced costs per unit of power), d ” better modeling and data availability. Deep learning requires massive amounts of data. Currently, it is estimated that the data we generate every day is 2.6 quintillion bytes. And it can analyze massive data sets much faster than a human. The machines do not suffer from monotony or fatigue.

Are there any risks in deep learning?

Let’s answer this question using the example of autonomous vehicles. Deep learning has given us these self-driving cars, but it seems unlikely that they will eliminate all road accidents, which would amount to an autonomous driving utopia. Indeed, a recent study of Insurance Institute for Road Safety (IIHS) says autonomous vehicles could prevent only about a third of all crashes. Yet it is more successful than people.

Yet concerns about widespread adoption may also include increased crash rates in the early days of deployment as technology learns, moral decisions left to manufacturers, and difficulties in attributing blame for accidents. And then there’s the hacking, because, after all, deep learning is just technology locked in a vehicle. In March 2019, two “white hat” hackers (the good guys) only needed a few minutes to navigate the infotainment system’s browser to break into a Tesla’s computer, run their own code and ask the car to respond to their commands.

We also need to look at the use of deep learning from the consumer’s point of view. If that doesn’t “work” (eg a phone won’t unlock) it can create an unhappy or frustrated customer, defeating the goal. To compound the problem, due to the complexity of neural networks in deep learning, it can be difficult to know where and why the system has gone bad. Often described as the black box of deep learning, data scientists strive to improve the visibility and transparency of how deep learning models work.

Models can also have unintentionally built-in biases – and these deep learning models are used for important decisions, including who gets loans, jobs, or parole. Deep learning should have clear safeguards with governance structures.

Deep learning is the future of business

Deep learning has given us image-based product searches – Pinterest, for example – and effective ways to sort fruits and vegetables to reduce labor costs. The former is more of a convenience for the consumer, while the latter is a real business case for productivity.

Significant resources are devoted to deep learning about financial services, where it is used to detect fraud, reduce risk, automate transactions and provide “robo-advice” to investors. According to a report by the Economist Intelligence Unit (EIU), 86% of financial services companies plan to increase their investments in AI by 2025.

The World Economic Forum’s Center for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient public procurement of artificial intelligence (AI) technology. Governments in Europe, Latin America and the Middle East are experimenting with these guidelines to improve their AI procurement processes.

Our guidelines not only serve as a practical reference tool for governments wishing to adopt AI technology, but also set basic standards for efficient and responsible public procurement and deployment of AI – standards that can eventually be adopted by industries.

Example of a procurement process based on the challenges mentioned in the guidelines

Example of a procurement process based on the challenges mentioned in the guidelines

We invite organizations interested in the future of AI and machine learning to get involved in this initiative. Learn more about our impact.

Integrating AI into your business has the power to improve differentiation and competitiveness, increase productivity, influence retention, and even change the course of disease – and it happens in every way. sectors and in all aspects of business.

It influences everything from recreating business and operational models, to hiring and retention strategies, to creating new corporate cultures that not only embrace but enable the use of deep learning. However, according to some estimates, Less than 1% data from most organizations is used, although there is huge amount of data available for transformative decision making. When will you start typing – and using – your own?

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