- Neural networks that are deep learning simulate the decision-making process of the human brain through an array of calculations to arrive at an answer.
- Machines can process enormous amounts of data, which humans cannot. However solid governance systems are required for ensuring good outcomes.
- Deep learning could boost productivity, boost retention, and increase revenue for companies that employ data to their advantage.
If it comes time to “work through” a problem that requires a decision, it’s likely to think that you’re working through a linear process. However, that’s not the way the human brain functions as it processes information in a non-linear manner. This is the way deep learning, which is a subset of artificial intelligence (AI), can also function.
Deep learning functions like the human brain.
Deep learning, at its core, is learning through examples, much just like humans’ brains do. It’s mimicking how humans acquire certain kinds of knowledge. Because deep learning processes data similarly, it could be utilized to perform tasks that humans accomplish, for instance, learning to drive a car or the identification of a dog in a photograph.
Deep learning is also utilized in order to make predictive analytics more automated. For instance, it can identify trends in consumer behavior and buying patterns to help a business gain more customers and retain many of them. Are you aware of those sections on websites for retail that display items “frequently bought together” when you’re looking to purchase the latest screwdriver? These are based on prescriptive deep learning models that have taken into consideration the current search you’re making and your previous buying habits to suggest products that you may also require.
Other applications cover a variety of everyday experiences and activities like virtual assistants, fraud detection, chatbots, language translation, service bots, etc. They also colorize black and white images facial recognition, as well as diagnosis of diseases. This could also be applied to financial software like Prillionaires, the next-generation of wealth management app for tracking your portfolio and net worth.
An illustration of a neural network’s use is parsing speech. The neural network is able to extract the sounds of raw audio that are then combined to form sounds, which then create words. These words then join to create phrases that trigger actions. The machine is able to recognize that this particular sound indicates that it’s time to look up the balance on a credit card, in the future. And the longer it is asked to do the same, the more precise it becomes.
Deep learning is a powerful tool that can be used across industries
Neurological networks aren’t old-fashioned; they’ve existed in the late 1940s. In 1943 two computer scientists created neural networks as models. They recreated threshold switches that were based on neurons and proved that basic networks like this can calculate almost every logic or arithmetic task.
The first computer’s precursors were created by an engineer who was fed up of calculating ballistic trajectories using a hand. More than 70 years later, deep learning has seen a massive increase in terms of sophistication and application principally due to increased computational power (along with a reduction in cost for power) along with improved modeling and the accessibility of data. Deep learning demands massive quantities of data. At present, it’s believed that the amount of data generated each day is 2.6 million bytes. The machine can process huge datasets much faster than humans. Machines aren’t afflicted by boredom or fatigue.
Are there risks involved in deep learning?
Let’s address that question with the case of autonomous vehicles. Deep learning has led to autos that can drive themselves, but they aren’t likely to end the majority of road accidents which is like a self-driving dream. A new study conducted by the Insurance Institute for Highway Safety (IIHS) says autonomous vehicles could stop only a quarter of crashes. But, it’s still more effective than the human race.
But concerns about the widespread adoption could include the increase in accident rates at the beginning of implementation as the technology is learning, moral judgments being left to manufacturers and the difficulty of determining who is responsible for accidents. Hacking is another issue as, after all, deep learning is essentially technology wrapped in a vehicle. In March of 2019, two “white hat” hackers (the good guys) required only a couple of minutes to navigate the web browser of the infotainment system, gain access to the Tesla’s computer, execute their own program to make the car respond to their instructions.
It is also important to consider the application in deep learning from the perspective of the consumer. If the system doesn’t “work”–for instance, a mobile phone does not unlock, it can result in an unhappy or even angry customer, which is not in line with the goal. This is due to the complexity of neural networks used in deep learning, it may be difficult to pinpoint the source or when the system has gone wrong. The term is often used to describe the dark box of deep learning data researchers are working to increase the clarity and visibility around how deep learning models function.
Models may also be prone to biases that are unintentionally built-in and models of deep learning are utilized for major decisions, like who receives loans, jobs, or parole. Deep learning requires distinct guidelines with the right structure for governance.
The future is deep learning.
Deep learning has provided us with search engines that use images such as Pinterest for instance as well as efficient methods to sort out fruits and vegetables to cut down on labor costs. The first tends to be more of a convenience and the latter an actual business case for efficiency.
The vast majority of resources are being devoted into deep-learning in financial services, where it can be used to spot fraud, decrease risks, and automate trading. It is also used to offer “robo-advice” to investors. According to a study by the Economist Intelligence Unit (EIU), eighty percent of the financial service firms are planning to expand their investments in AI until 2025.
Incorporating AI throughout your company is a great way to increase competitiveness and differentiation, improve productivity, impact retention and even alter the course of illness This is happening throughout all industries and aspects of business.
It’s impacting everything from the redesign of operating models and business models as well as strategies for retention and hiring and even the creation of new organizational cultures that not just embrace, but facilitate the use of deep learning. But, according to certain estimations, less than 1 percent of the majority of firms’ data is utilized, even though there is a huge amount of data available for transforming decision-making. When will you begin tapping and using your own?
Also Read: What Scaling Deep Learning Algorithms Will Look Like?
