101 On Deep Learning + Blockchain [A Brief Introduction]
The tech and business industries are both very bullish about the future of machine learning. Experts believe that the technology can potentially add more than $2 trillion in value to the manufacturing and logistics industry by 2020, with another $2.5 trillion being added to the marketing and sales fields.
The International Data Corporation predicts that global spending on machine learning will exceed $77 billion by 2022. One of the most important drivers of this growth is deep learning, as major tech companies, pharmaceutical firms, and blockchain consulting services are all racing to take advantage of this powerful new technology.
A Brief Introduction to Machine Learning
Deep learning is a subfield of the larger machine learning branch of computer science (CS). Machine learning, which is essentially artificial-intelligence (AI) driven software, is already helping businesses increase profits and efficiency. Eventually, it will make a wide array of futuristic technology possible.
Machine learning is a complex new branch of computer science that combines traditional CS skills with mathematics, statistics, and artificial intelligence to create technology that can naturally expand its own capabilities.
The idea behind machine learning is that computers should be capable of more than simply running a program–they should be able to write their own. They should also be able to use past experiences to improve their skills and avoid making the same mistakes again.
These types of computers are also being used to help companies and individuals plan for the future. That’s because they can use any large data sets to find patterns and, most importantly, to make accurate predictions.
Slide by Andrew Ng, all rights reserved.
Deep Learning is the Future
Deep learning is itself a subset of both the machine learning and AI fields. It mimics the structure of the human mind to recreate artificial neural networks that are potentially more powerful than traditional machine learning systems.
While human-like computer systems have long been popular in science fiction works, recent advancements in computing power and processing speeds mean that modern units are capable of training large artificial neural networks.
For example, deep learning computers have been taught how to accurately identify the details of images through repeated exposure. Self-driving car startups like Wayve.ai expose their deep learning systems to millions of images of roadways. These computers have taken that huge amount of data and can accurately identify road conditions using the vehicle’s 360-degree cameras.
This type of active learning is incredibly important because it allows technology to improve over time and achieve higher and higher levels of accuracy. In fact, many deep learning systems are already capable of greater accuracy than human experts.
Observers can expect to see much more rapid developments in deep learning technology. That’s because the technology requires massive amounts of computer power–which are just now becoming available–and because it requires huge amounts of labeled data. Technologies like self-driving cars need millions or billions of images before they can achieve consistent results.
Current & Future Applications
Deep learning is already being used to advance technology, improve medical patient outcomes, and increase corporate profits. In the future, this branch of machine learning will make things like efficient pharmaceutical development natural language processing a reality. Blockchain development services may also use the technology to exponentially increase the power of deep learning networks.
One of the most exciting areas of deep learning research is in the healthcare and pharmaceutical industry. For example, deep learning may help resolve one of the most pressing problems in the healthcare industry–misdiagnoses.
Experts state that each year an estimated 5% of medical diagnoses are incorrect, impacting roughly 12 million patients every year. Just as importantly, this results in between 40,000 and 80,000 annual deaths.
Healthcare companies like IQuity are using deep learning networks to improve patient outcomes by detecting serious diseases earlier than human doctors. The company recently shared results from a study which demonstrated that their AI program could diagnosis multiple sclerosis “at least 8 months” before physicians would arrive at that conclusion.
Pharmaceutical research companies are using deep learning to increase efficiency and improve their drug finding strategy. The consulting company McKinsey estimates that machine learning will increase profits by more than $100 billion a year.
Deep learning offers such amazing benefits because it can use past data to predict whether a particular drug in development is likely to reduce symptoms, heal a disease, or cause serious side effects. It accomplishes this by organizing and analyzing past drug research data for clues that will help avoid costly, unnecessary research and focus on the drugs most likely to be successful.
Natural Language Processing
One of the biggest obstacles that the tech industry has yet to overcome is building a program that can truly understand natural human language. They’ve certainly made progress over the past decade. In the past, users had to search for data on the internet by using unwieldy phrases that a machine could easily understand. Today, they can type queries using natural language.
The industry has also improved their ability to understand spoken natural language. Phones and computers released a decade ago were frustratingly difficult for people to use without a keyboard.
However, things changed when Google launched its Voice Search in 2008 and Apple’s popular personal assistant Siri was released in 2011. Now, a college student can write an entire research paper using one of many high-quality voice-to-text apps, like Google Docs Voice Typing or Dragon Typing.
Much of this progress is a result of machine learning and AI. These programs have used deep learning to gain insight from billions of interactions with end-users, resulting in a much higher quality product that now works as advertised.
Research has found that nearly 40% of internet users have utilized a voice assistant in the past year. In the future, most people will likely interact with their electronics and the world around them using voice-activated technology that is made possible through deep learning.
Blockchain technology is one of the most popular areas of the computer science industry. It has risen from fringe movement to major technological market over the past decade. Experts predict that the total size of the blockchain market will reach an astonishing $57 billion by 2025.
Blockchain is the foundation of the cryptocurrency market, with Bitcoin and Ethereum as the most popular examples. It also makes things like smart contracts, distributed ledgers, and blockchain-based supply chain management systems a possibility.
The technology works by keeping information on every user’s computer. This prevents hackers from altering the data. In addition, cryptocurrencies like Bitcoin use a protocol that requires miners to combine their processing power in order to mine new coins.
Experts believe that this distributed ledger technology can eventually supply the power necessary for advanced deep learning. Scientists and blockchain developers may eventually use thousands or millions of computers to conduct these deep learning exercises that cannot be completed by a single computer or group of computers.
Machine learning is revolutionizing the entire world. This cutting-edge technology is making futuristics ideas like AI, realistic digital personal assistants, and cost-effective pharmaceutical research a reality. This promise is a major reason why Fortune 500 companies, angel investors, and blockchain development companies are investing billions into the technology.
Experts also believe that blockchain has the potential to unleash the full power of deep learning. That’s because companies can utilize the combined computing power of a large blockchain network to increase deep learning speeds and capabilities.
Look out for several new deep learning developments over the next decade. Pharmaceutical companies are expected to improve their research efforts and avoid drugs with dangerous side effects, while tech companies will use deep learning to further improve AI and natural language processing.