Advancing AI Technologies, Deep Learning Show Meaningful Enterprise Applications

neural network

Technology learning has been moving forward exponentially, and it’s mind-boggling. To the excitement of industry leaders, the world of artificial intelligence has been advancing particularly fast in recent months. Researchers are developing robots that can teach themselves.

For the swiftly-approaching future, people are worried about their jobs. A Pew Research study found that 65% of Americans expect that within 50 years, robots and computers will take over much of the work currently done by humans.

They’re right. With high potential for increased profitability, efficiency and accuracy, CEOs and managers have every reason to seek out AI associates.

Any job that could be classified as “repeatable complex decision-making” can easily become automated. Tractica consulting firm found 200 real-world applications for machine learning in enterprise environments. They predict these streamlining AI applications will increase revenue from $358 million in 2016 to $31.2 billion by 2025.

With automation and deep learning, the nature of many white-collar worker jobs will soon shift. Employees in legal, accounting, human resources, insurances, call centers, financial services and other sectors should prepare for a changing future.

Here are the top 10 enterprise industries sectors for AI adoption:

  1. Business Services
  2. Advertising
  3. Media and Entertainment
  4. Investment
  5. Finance
  6. Healthcare
  7. Manufacturing
  8. Agriculture
  9. Legal
  10. Education

In addition to the afore-mentioned adoptions, AI technologies are driving a proliferation of uses in nearly every industry. With AI software, computers are able to accomplish several tasks better than humans, especially when it comes to processing very large amounts of data. Machines can process with greater speed, efficiency and accuracy.

The nature of work is changing fast, but that won’t leave us jobless. It will simply make the type of work needed different. We’ll need to hone in on the skills and abilities that make us uniquely human. We’ll also need to figure out how we can better utilize AI capabilities and collaborate to solve bigger problems.

AI Applications in Business

We’ve slowly been adapting to and enjoying AI-enhancement in our personal lives. Now it’s beginning to integrate more and more into our work lives. Some of the most immediate applications for AI will be in business, especially within huge corporations that process great loads of data.

Investments into AI research labs have been pouring in, especially with the world’s leading tech companies on board. Companies like Google, Amazon and Microsoft are pouring into their AI labs and quietly developing their own deep learning techniques. They’re working to create user-friendly commercial products

These tech leaders have been placing their bets on AI deep learning and scouting top researchers for the development. Now their efforts are beginning to bear fruit. Last year Google’s deep learning system “AlphaGo” made headlines for its ability to defeat the world champion of Go, the most complex board game ever created.

For fans of 1997 chess-champion Deep Blue, this may seem like a small achievement for two decades of research. However, given the difference in total possible game outcomes (that’s 10120 for chess versus 10761 for Go), the progress is astounding. To put these numbers into perspective, our universe is composed of some 1080 atoms.

With such recent deep learning advancements, we’re hitting a peak with AI and beginning to see the real impact in our economical realm.

These established corporations and startups will be releasing commercial solutions with user-friendly deep learning applications. These futuristic devices will soon be able to help people who are computer scientists to do more than recognize their voice and face. In the future, deep learning will be able to help identify diseases and other such medical wonders.

As artificial intelligence integrates more into our workplace, you’ll see more of these developing abilities:

  • Automation
  • Human Assistance
  • Conversation
  • Cloud Processing
  • Deep Learning

Out of these prominent trends, deep learning is becoming the most impactful with increasingly understood applications.

Deep Learning Vs. Traditional Algorithms

Deep learning has reached public notoriety for its extensive future potential. At the moment researchers are working more for the robots than with them to train in deep learning. But that will change soon.

Deep learning AI is moving aggressively from the lab into the industry. IBM has been the most prominent technology supplier, especially with their Watson format.

Last year IBM Watson Explorer AI replaced 34 jobs at the Fukoku Mutual Life insurance firm in Japan. The AI machines are able to calculate payouts to policyholders, analyze and interpret data, unstructured images, audio, text and now videos. Firm owners expect to see a 30% boost in productivity and a return on machine investment in less than two years.

Well-known applications of deep learning include: image and object recognition, speech recognition, time series forecasting and traditional classification tasks. Think about Siri or Alexa for voice recognition and Facebook’s photo matching for image recognition.

The premise of deep learning is to replicate the human brain, specifically the neocortex which is our higher-level thinking realm. Like humans, deep learning bots learn from experience. It emulates human learning abilities by creating a virtual neural network.

Neurons are cells that transmit electrical or chemical information. When they connect with other neurons, they create a network. They’re like bits of code running statistical regressions. If you keep stringing these together, you can create virtual neural networks.

The most advanced deep learning networks are created with millions of simulated neurons and billions of interconnections. Much like a child learning about the patterns and categories of the world around them, these artificial neural networks need to be trained. Now they’re training through a combination of reinforcement and unsupervised learning. Think about those Google chat bots that have been so entertaining.

Once the machine identifies smaller parts of a complex pattern, it will create layers that feed into another layer. This helps the machine to recognize even greater levels of abstraction, which leads to the name “deep learning.”

Through training the program to understand simple objects like an ear or a nose, it can then piece together more complex patterns like a human face. With these types advances in the AI realm, it’s possible for the software algorithms to see, hear, reason and learn.

When most people talk about AI, they’re referring to advanced decision trees and statistical analysis of large amounts of data. Algorithm-based AI is the more traditional use. However, even the most complex algorithms lack certain human abilities. It’s taken decades for machines to achieve toddler-level abilities in judgement-making.

Things we consider to be common sense, or our most basic judgement abilities, have been difficult for computers. Things like recognizing faces and emotions or answer novel questions were a struggle. They have required step-by-step handholding through deterministic algorithms.

However, with this human brain-inspired branch of AI, enormous strides have been made and expansive learning potential has been unlocked.

They’re developing robot intuition and responsiveness. Learning to work through complex decision-making and categorizing leads the deep learning AI agenda. With these abilities, deep learning can make some of the most complex computer functions, such as self-driving vehicles, possible and more effective.

Interacting with Robots, Technology

With immense promise in advancing technology, who knows how many innovative ideas are sifting through entrepreneurial minds just waiting to be developed. There’s a lot to work with here, and the need for technology interaction is only going up from here.

Our future success will depend largely on how well we are able to coordinate with these machines. That’s why it’s important to understand the driving technology trends.

It’s time to start focusing on the future and considering how we will coordinate with our rising robot counterparts. The best approach is to develop adaptability and keep learning.

Predictions and worries about the future of robots vary widely. People worry that we’ll either face a mass extinction or life extension, with thoughts of immortality. These ideas seem far-out and futuristic for most people.

The biggest fear is that AI robots will begin to take over our jobs and cause mass levels of unemployment. For employees who aren’t learning anything new and have no desire to, this is bad news.

There’s a whole layer of workers in many industries who do simple tasks. Automation will definitely impact these unskilled workers. This is where a specialist could come in with the ability to automate and outsource these tasks, making those positions unnecessary.

The specialists will, at minimum, be able to break down large tasks into smaller goals and set up outsourcing systems. These methods are highly-appealing to companies because of the increased efficiency it provides. However, for that layer of unskilled workers, this is admittedly a scary move forward.

The global workforce will definitely be forced to expand in practical abilities, to develop improved learning skills and to continue growing.

More and more applications will be found for deep learning that further improve the world. Most experts believe that AI will take over almost every job that we’re doing today. Instead of worrying about job security, that should compel us to start dreaming of new goals for AI to achieve.

With our combined human and artificial intelligence, we’ll have the ability to start solving climate change and curing cancer.