Technology has been improving human life since decades. Most aspects of human life have been enhanced by technology. Everything from airplanes, to telephones, to radios, to smartphones, have morphed our lives. We are able to do more in our lives than previous generations. We are also living longer as life expectancy is increasing throughout the world. However, people are unwell for longer periods in their life even though they live longer. While life spans have increased, healthy part of life has not increased in the same proportion. So people are spending significant portions of life under care. Most of the healthcare spending is in the area of caring and treating older people. What we need to focus on now is extending the healthy life, so people enjoy the extended life. Technology has helped us understand the aging process and explore ways to slow the aging process.
Automation has been happening in different areas over time and with technological progress, it seems like a threat now. It is estimated that over two-thirds of the workforce will have no work in the next decade. While the population keeps growing around the world, the lack of work brings challenges for the future that need to be addressed today. While we will not have the same kinds of jobs in the future, there will be different jobs and we must train the future generations differently to tackle challenges of the future. However, our society has been designed around growth. Every country is ranked by growth numbers in the short and medium term. This forces everyone to keep churning good growth numbers year after year and ignore the bigger challenges that await in the future.
In order to tackle the dramatic shift in how future productivity will function – where more of the work is automated, we must be willing to take a hit on the meaningless yearly growth numbers and start working towards completely different metrics and frameworks that define growth and productivity in the future.
Tesla is a Silicon Valley company making cars. Companies in Silicon Valley have always been disruptive – changing behavior of people using technological innovations. We usually see companies making computers, software, processors, phone, etc. Tesla is focusing on transportation. Tesla is making desirable electric cars for the masses. Most cars on roads today are powered by gas, which is processed from crude oil. The world is dependent on cars for transit and hence on oil to keep people moving and countries progressing. Tesla is going to eliminate dependence on oil with move to electricity as fuel.
Cars were a mechanical marvel. They were a combination of engineering breakthroughs integrated in a brilliant manner. The mechanical machines provided transportation within cities and long routes. They even got more economical over time and now most people on the planet own a car or have at least travelled in one. With the rise of technology, these mechanical machines evolved into technological instruments. Today, most cars are technology on wheels. There is not much mechanical left in them. Pressing the gas pedal does not move levers, but sends digital signals to the on-board processor. Everything from gas and break pedals, to the radio, to odometer and fuel readings is digital. There are various digital processors in the car and almost every action sends signals to different processors. These processors are similar to the ones on the computer or a smartphone. That means they are vulnerable to hackers. As we have moved to digital cars, the car makers have started hiring computer engineers and security researchers to prevent cars from being hacked. This has also given rise to car security companies that make devices to monitor cars with mobile apps and get alerts when theft occurs.
Mobile payments have grown into a big market. Peer to peer mobile payment services are used by almost everyone. When friends go out, they split the bill and pay each other using mobile wallets or mobile payment services. When college students split rent for housing or utility payments, they turn to mobile payments. Mobile payments are also used for purchasing goods and services from small sellers or shops. A few years ago, mobile payment apps were pretty new and used only by a smaller tech savvy group (though SMS based payments have been popular in Africa since several years). Services like Venmo (now owned by PayPal), Square Cash and Google Wallet have now emerged and most consumers are now using these services. In the last few years, the usage has grown from handful of tech savvy users to almost every consumer. This has gotten attention from the big banks as they wish to get a piece of the pie and enter the mobile payments market.
Machine learning has a plethora of applications. One area in which machine learning is being used is the field of medicine. Researchers perform experiments in labs to study effects of medicine on different animals. When a drug works on a monkey or mice, they start a clinical trial on humans (after endless amounts of paperwork). If the clinical trial is successful on a diverse sample of subjects, then the FDA approval starts. After FDA approval, the drug can be marketed for mass distribution across the country. At this point, the drug makers spend money on marketing, sales representatives, providing incentives to physicians that agree to prescribe the drug, and advertising on TV and the internet. The drug is known to treat a certain condition, and has a list of known side effects. Millions of people across the country take this drug and it works for most of them. This is a system that has been in use for decades.
We live in the information age. You don’t need to go to the library for information. You can pull out your smartphone from your pocket and ask Siri or Google Now or Cortana, and it will read out the latest information for anything you ask. We have resources like Wikipedia for knowledge, Wolfram Alpha for mathematical information and countless news sites and blogs indexed by Google. All of this information is at our fingertips. But we are about to get a lot more information about a lot of new things. We have reached an era of powerful micro-computers that are cheap and affordable. They have a plethora of on-board sensors that can produce data. This data will give us an insight into a lot of topics that we did not know about. It will open up our understanding of the environment we live in, the factors affecting it, and we will learn even more after studying co-relations between the data we will soon have.
Passwords have been around for centuries. When Julius Caesar was fighting a war in Europe, they used a password at the gate before soldiers could enter the camp. In those days, you did not have government issued Photo IDs. The enemy could kill a soldier, wear this uniform, steal his horse and penetrate the their camp. Having a password allowed camps to only grant entry to the soldiers who knew the password. With the advent of the internet, we have used passwords as an authentication mechanism for everything from email and online shopping sites to banking.
It is becoming easier for hackers to break passwords and hijack accounts. We have resorted to using more complex passwords than we cannot remember, and relying on password manager apps to store them. This is becoming very inconvenient. To solve this problem, many companies are working on the next generation of passwords. EyeVerify has developed eye-detection software that detects eye characteristics using a picture from the camera, and generates a password based on the characteristics. They call it the EyePrint. They also detect features like vein patterns in the eyes. So when you want to login to a service, you can simply snap a picture of your eyes and be authenticated. MasterCard is also working on a similar approach. They are using selfies for authentication. This involves analyzing more facial features like position of eyes, nose, mouth, etc. MasterCard believes that this is the future of passwords.
Computer Aided Design (CAD) has existed for a long time now. Designers learn how to use popular software like AutoCAD from Autodesk, and design products using the software. The traditional human designing process is now aided by computers. CAD enables designers to try out different designs on the computer and analyze them before they can settle on a design. Autodesk now has a new research project called Dreamcatcher. With Dreamcatcher, the fulcrum is slightly moved. Instead of computers providing help to the designer, the computer takes over and designs the product, while the human can propose tweaks and pick a design short-listed by the software. Dreamcatcher understands the product that needs to be designed, analyzes the different variables involved and does mathematical calculations to come up with the best design for the product. The human can then tweak or change the design and Dreamcatcher will finalize it.