Eliminate Your Fears And Doubts About Cybersecurity

Cyber-attacks are growing in severity and in numbers each year despite the best effort of security experts worldwide. Organizations small and large, government or non government are losing millions to bigger and bigger cyber-attacks. Wannacry, Notpetya, Melissa virus, Conficker, Shamoon are just a few out of a large list of successful cyber-attacks the world has seen. Cyber-attacks could be of more than one type, such as- Malware, Ransomware, Phishing, DDoS, Malvertising and Password attack. In this era of growing cyber-threat, significance of cyber-security is undeniable. Organizations are now spending millions on cyber-security to keep attackers at bay.

Source of cyber-attacks

A cyber-attack can be caused by an individual or a group of individuals such as a criminal hacker group. Cyber-attack could be state sponsored or can be caused by non state actors. Reason of a cyber-attack can also vary. It could be for money as in the case of ransomware attacks or it can be for crippling state infrastructure and utilities.

Types of cyber-attack

Malware: It stands for malicious software which is crafted to cause problems to a computer or network. A malware can be a virus or a worm or spyware, adware or trojan. Malware can infiltrate a system through a download or a malicious link or operating system vulnerabilities. Superfish adware, zeroaccess botnet, Cryptolocker trojan, Stuxnet worm are some of the biggest malware attacks that happened.

Ransomware: It is a type of attack in which the hacker gains access to the system and encrypts the data, and asks for money to decrypt it. Users of the system are effectively locked out. This type of attack can be very devastating if target is a critical utility. SimpleLocker, TeslaCrypt, NotPetya and WannCry are some of the biggest ransomware attacks.

Phishing: Cyber-criminals posing as a trusted third party send emails asking for username, passwords, Bank account details, credit card details and then use this data to carry out a theft or gain access to sensitive information. It is one of the oldest and most widespread form of cyber-attack. Users should be careful while reading emails and clicking on links to protect themselves against this form of cyber-attack.

DDoS: It stands for distributed denial of service attack. Multiple compromised systems as in a botnet are used by criminal hackers to flood a targeted server with traffic which overwhelms the system by using up all the bandwidth and system resources, leaving the server inaccessible to normal users. A DDoS attack can run for hours to days. Reason for a DDoS attack can vary from business competition to political. GitHub, CloudFlare and Spamhaus has suffered some of the biggest DDoS attack.

What is cyber-security

Cyber-security is the practice of keeping systems safe from cyber-attacks. Cyber-security is achieved through network security, application security, Information or data security and user training and awareness. Optimized people, process and technology is a pre requisite for successful deployment of cyber-security. Users must adhere to safe web practices such as carefulness about opening links and setting strong passwords and changing passwords regularly. Organizations must have well defined policy regarding cyber-security. Technology such as firewall, antivirus software, anti-malware must be competent and up to date. 

Cyber-security in numbers

Cyber-security market will hit 300 billion dollar mark by 2024.

Cyber-attacks will cause 6 trillion dollar in damages by 2021.

A cyber-attack occurs every 39 second.

Nearly half of all cyber-attacks target small businesses.

Around 230,000 malwares are created by criminal hackers everyday.

US cyber-security budget was 14.98 billion dollar in 2019 and may increase to 17.44 billion dollar in 2020.

Ransomware attacks will cost about 20 billion dollar by 2021.

Importance of cyber-security 

In the era of Internet of Things more and more everyday objects are getting equipped with an IP address through which they can communicate with other objects. Apart from raising convenience level for users, this also creates growing opportunity for cyber-criminals to find a security vulnerability and launch an attack. In this increasingly connected world cyber-attacks are getting increasingly disastrous. Cyber-security is more relevant today than ever before. Strong cyber-security practices is a must for trouble free function in the world we are living today.

References:

1) https://www.cisco.com/c/en/us/products/security/what-is-cybersecurity.html

2) https://us.norton.com/internetsecurity-malware-what-is-cybersecurity-what-you-need-to-know.html
3) https://www.cpomagazine.com/tech/11-eye-opening-cyber-security-statistics-for-2019/

Things You Probably Didn't Know About Internet Of Things

Devices with embedded sensors and ability to gather and exchange data over an Internet connection are steadily rising in numbers. Everyday objects such as your toaster, refrigerator, car, electric lamp, alarm, wristband, watch etc. can now send and receive data through an Internet portal. By 2025, there could be around 75.44 billion connected devices around the world according to Statista.com. A common Internet of Things platform brings together the data devices send, analyses this data and extracts meaningful information which is then used to trigger meaningful action in a secure manner. This is how Internet of Things work.

What is Internet of Things platform
 
Diverse connected devices or things require a common language to securely communicate with each other. Internet of Things platform is a technology which makes that possible apart from data analytics and derivation of actionable intelligence from the device data. It makes use of cloud technology to securely receive and send data to various connected devices. IoT platform also provides tools for developing applications for IoT hardware. Number of IoT platforms is rising with rising number of IoT devices.

Stats about Internet of Things

Internet of Things or IoT device market could reach 1.1 trillion dollar by 2026.

There could be 3.5 billion cellular IoT connections by 2023.

IoT Market will attract 15 trillion dollar in investments by 2025.

IoT Management market could reach 16.86 billion dollar by 2025.

Global spending on IoT could reach 1.29 trillion dollar by 2020.

IoT Will add 10 - 15 trillion dollar to global GDP by 2030.

75% Of new cars will come with built in IoT connectivity by 2020.

Internet of Things and Cyber-security

IoT technology makes life more convenient but at the same time it is increasing opportunities for hackers and cyber-criminals. Every connected device is a potential target and can become a point of network vulnerability. Cyber-security should be the first thing in mind while purchasing an IoT device. Usually manufacturers don't make their devices secure enough. A not properly secured IoT device could be used by hackers to breach network and hold the entire system for ransom. The increasing number of cyber-attacks is a signal to manufacturers to make their products with advanced security features.

A Use case of Internet of things

You are awaken in the morning by your smart alarm. The smart alarm doesn't just wakes you up, it also signals the coffee maker to start brewing coffee and curtains to fold up. It signals the geyser to turn on, so by the time you enter shower, hot water is ready. The geyser can signal your smart toaster to turn on and electric vehicle to start charging so that by the time you are all set it is ready to take off. Your smart camera senses your absence and signals smart doors to lockup and thermostat to turn off. This is an example of how IoT makes life easy, but it can also be used by hackers to make life difficult, if the security is not competent enough.

References:

1) https://www.ibm.com/blogs/internet-of-things/what-is-the-iot/
2) https://www.iotforall.com/what-is-iot-simple-explanation/
3) https://www.visioncritical.com/blog/internet-of-things-stats
4) https://financesonline.com/iot-statistics/

How Industry 4.0 Is Transforming Industrial Production

A wave of new technologies such as cloud computing, 3d printing, robotics, artificial intelligence and internet of things has brought forth what is widely addressed as the 4th industrial revolution. Industries are using these new technologies to become more productive and efficient. Industry 4.0 is changing the world economy as it enables production of personalized goods closer to market.

IoT and IIoT

Network of interconnected devices is known as internet of things. When each device and machine in an industry is interconnected through a portal, we have what is known as industrial internet of things or IIoT for short. One simple way to achieve IIoT is to provide each device an IP address and then interconnect the devices using this IP address. IIoT makes it possible to get wide range of data for efficient automation and control of plant machinery thus raise productivity while cutting cost.

Cloud Computing and Industry 4.0

Cloud computing makes it possible to access data and computing power from any suitable device with Internet connection. With cloud computing, workers can access information and execute control operations remotely. Cloud solutions make way for faster innovation. Cloud is more secure and saves money.

Artificial Intelligence and Industry 4.0

Machines with artificial intelligence are saving cost and improving production. More and more industries are employing intelligent machines in production lines for higher productivity. Algorithms derive meaningful insight from plant data. This insight is utilized to optimize processes. Machines capable of learning from experience are accomplishing many tasks that previously required human hands. Machines are now capable of facial recognition, natural language processing, obstacle avoidance, autonomous driving, autonomous movement and much more.

3D Printing and Industry 4.0

The process of additively manufacturing a 3d product or component using a 3d printer and a 3d digital model is referred as 3d printing or additive manufacturing. Different type of 3d printers can be used to produce different type of product. For example, a plastic printer can be used to make plastic products and a metal printer can be used to make metallic products. 3D Printing makes it possible to produce highly customized end products. Here is a short list of different types of 3d printers-

1) Fused deposition modeling (FDM)

2) Stereolithography(SLA)

3) Digital Light Processing(DLP)

4) Selective Laser Sintering (SLS)

5) Selective laser melting (SLM)

6) Laminated object manufacturing (LOM)

7) Digital Beam Melting (DBM)

Robotics and Industry 4.0

Robots have become a key element of modern industry. There are collaborative robots that work with humans and there are stand alone robots. Robots have taken over much of the work in a number of fields ranging from automotive to agriculture. AI Powered robots are welding joints, assembling parts and weeding farms. Robots with advanced machine learning algorithms are learning from past mistakes thus becoming more precise and efficient at their tasks.

Engineers are using augmented reality to visualize their designs in 3d, collaborate and communicate better. Industry 4.0 is present and future. With these new technologies industry is aiming to become more autonomous, climate friendly and sustainable, cut cost and waste while raising productivity and efficiency. In future we will see more inclusion of new technologies in production.

References-

1) https://interestingengineering.com/the-industrial-revolution-40-and-its-possibilities-revealed

2) https://www.researchgate.net/profile/Rainer_Schmidt/publication/274894802_Industry_40_-Potentials_for_Creating_Smart_Products_Empirical_Research_Results/links/552beaa00cf21acb091ec04d.pdf

3) http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1807-76922018000400100

4) https://www.seebo.com/industrial-ai/

5) https://proto3000.com/industry-4-0/additive-manufacturing-perspectives-in-industry-4-0/

What Is Artificial Intelligence And How Its Changing Our World

Machines capable of learning, reasoning, self correction are known as intelligent machines having artificial intelligence. In short machine intelligence is Artificial Intelligence. Artificial Intelligence is achieved through hardware that resembles human brain as in neuromorphic computing. IBM has developed a brain inspired architecture using neurosynaptic cores. Most machines today are based on Von Neumann architecture. To achieve machine intelligence researchers are combining Von Neumann architecture with neuromorphic architecture. The Von Neumann architecture activity is measured in Flops while neuromorphic architecture activity is measured in sops or synaptic operations per second. Neuromorphic architecture simulates the working of neurons using neural networks. This helps researchers achieve amazingly high computing ability than traditional computers at lesser energy consumption as demonstrated by IBM true north board and Intel's Loihi board. Memristors also are used to achieve cognitive computing in machines or machine intelligence.

A concise history of AI

Concept of artificial intelligence has existed for 1000s of years. The term 'Artificial Intelligence' became official at 1956 Dartmouth Summer Research Project. John McCarthy and Marvin Lee Minsky are the Founding fathers of modern artificial intelligence. The Dartmouth conference also outlined 7 original aspects of artificial intelligence. They are-

1) Automatic computers

2)How computers can be programmed to use a language

3)Neural nets

4)Ability to measure the complexity of a problem

5) Abstraction

6) Self improvement

7) Randomness and creativity

Jeff Hinton is yet another prolific researcher in this field who kept working on developing AI through thick and thin. Research on artificial intelligence boomed from 1956 to 1974 with lots of funding and researchers taking interest. The period from 1974 to 1980 is known as first AI winter. Money stopped flowing and researcher interest waned due to lack of computing power and data. Period from 1980 to 1987 is known as second AI boom. During this period expert systems rejuvenated researcher interest. Hopfield net was created. Period from 1987 to 1993 is known as second AI winter. From 1993 onwards AI research and development exploded. Now a days we have deep neural nets, natural language processing and facial recognition working seamlessly.

Artificial Intelligence, Machine Learning and Deep Learning

A machine is called intelligent if it shows cognitive capabilities such as learning and problem solving. AI can be subdivided into narrow AI, general AI and strong AI. When the machine is better than humans in a specific task, it is called narrow AI. When the machine shows human like intellectual capabilities, its general AI. When the machine is better than humans in intellectual tasks, its strong AI.

Machine Learning is a subset of Artificial Intelligence. It is the ability of a computer to analyze a large set of data and learn from it . Using machine learning algorithms, computers can learn to accomplish a task without being programmed for it.

Deep Learning is subset of machine learning. It teaches computers to learn by example. Deep Learning is how driverless cars recognize a stop sign or other cars on road. Data is fed into neural networks in order to learn the characteristics of an object and thus identify it.

Supervised Learning, Unsupervised Learning and Reinforcement Learning

Supervised Learning, Unsupervised Learning and Reinforcement Learning are types of machine learning. When the machine learns under guidance using labeled data, it is called supervised learning. When the machine learns without any guidance using unlabeled data, it is called unsupervised learning. Reinforcement learning is a type of machine learning in which an agent interacts with its environment in order to maximize the chance of a reward. Regression and classification problems can be solved using supervised learning. Association and clustering problems can be solved using unsupervised learning. In case of reinforcement learning the input depends on action taken, there is no predefined data. Supervised learning can be used to forecast sales. Unsupervised learning can be used for anomaly detection. Reinforcement learning is used to make driverless cars and gaming agents.

What is Automated Machine Learning or Auto ML

If machine learning is applied to real world problems automatically, it is identified as automated machine learning. Using traditional machine learning methods to solve real world problems is time consuming and challenging. Auto ml makes machine learning simple to use through sophisticated algorithms capable of fetching most relevant information from data set. Not everything is yet automated though. Some human expert may still be required for a successful deployment and running.

What is AI bias?

AI systems are only as good as the data they are trained on. Bad data may result into a biased AI. Algorithms created by biased individuals may show bias when used to solve problems. Bias can enter into AI system at many stages such as understanding the problem, collecting data and preparing data. More than 180 human biases have been identified. Biases can cause trust deficit between man and machine. Good thing is it can be tackled using specific methods. Identifying and solving AI bias is essential.

Future of AI and applications

AI is predicted to write a high school essay by 2026. By 2028 it will be able to generate a creative video. By 2049 it will be able to write a new york times best seller book. By 2050 it will become able to compete in a standard math competition and by 2059 it will be smart enough to conduct math research. List of AI applications is huge. AI is finding applications in transport industry, manufacturing and agriculture. It is also used to create new tech experiences. Offices worldwide are employing AI solutions for higher productivity. It is estimated to add $16 trillion to global economy by 2030. Its predicted to create more jobs than number of jobs it will eradicate. Future seems bright for artificial intelligence. 

Reference:

1)https://www.britannica.com/technology/artificial-intelligence 
2)https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/?cn-reloaded=1
3)https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_overview.htm

How Brain Machine Interface Is Changing The Way We Interact With Machines

Research on brain machine interfaces is going on since 1970s. A brain computer interface makes it possible to control a machine such as a robotic arm by thinking about it. Electrodes placed directly on the brain through surgery or those placed on the head can be used to collect brain signals which is then used to trigger control commands through a computing machine. Brain signals are used to create an electroencephalogram or EEG which means electric brain picture.

Types of Electrodes

Electrodes measure potential difference between neurons. Charged particles move between neurons in response to external stimuli. This movement of charged particles creates  potential difference. This signal is amplified and applied to a computer in order to control a machine. Electrodes can be of direct type and non invasive type. Direct type electrodes provide better signals than those which are worn overhead. Non invasive electrodes are of dry type and gel based. Again gel based electrodes work better than dry electrodes. Dry electrodes are however more convenient to use. Researchers are coming up with high sensitivity dry electrodes for brain machine interfaces.

Developments in the field

Brain computer interface research gradually moved from animals to human subjects, over the years. Researchers at DARPA have done much work in direct neural interfaces which allowed them to help paralyzed person control a robotic arm and play a video game using thoughts in brain. DARPA also demonstrated that sensors can be used to help a paralyzed person feel the touch on a prosthetic or robotic arm. Such sensors can also be used to help a blind person experience vision without using eyes.

Companies and institutes active in the field

Research and development of brain computer interface is going on at several universities across the world such as University of Miami and University of California. Several companies are working on launching commercial BCI devices such as Neuralink and Emotive. Emotive is a headset which can be used to control toys or play a game on computer. Justin Sanchez who is currently with DARPA and Polina Anikeeva at MIT are two prolific researchers in this field.

Future of brain machine interface technology

In future paralyzed people will be able to control a humanoid bot or an exoskeleton in order to accomplish simple tasks. People will also be able to work with a computer, browse the Internet and communicate with others using their brains alone. Brain to brain communication is yet another possibility. In short people will become able to run most machines just by thinking. Possibilities for this technology seem endless.

References:

1) https://www.nature.com/subjects/brain-machine-interface
2)https://towardsdatascience.com/a-beginners-guide-to-brain-computer-interface-and-convolutional-neural-networks-9f35bd4af948 
3)https://www.techworld.com/tech-innovation/we-spoke-some-neuroscientists-about-computer-brain-interfaces-3691918/

Our Future With Robots Will Be Amazing And Here Is Why

Robots have started to cook and clean for us and are assisting us in our day to day lives. Industrial robots have been assembling parts and manufacturing materials for a long time now. In healthcare, robots have started performing surgeries. Bots and drones are delivering packages. Military has been extensively using robots for diffusing bombs, for surveillance and for vigilance. Autonomous tractors are tilling and ploughing farms. Robots are appearing in almost every field with abilities that seem to grow every day. With artificial intelligence they are beginning to show some cognition as well, like Honda's ASIMO which can remember names, faces, take simple orders and execute them.

We have been thinking about machines that can move about themselves and do things for as far back as ancient times. In around 400-350BC, Archytus of Tarentum built a mechanical bird that can fly. "If every tool when ordered or even of its own accord could do the work that befits it then there would be no need either of apprentices for the master workers or of slaves for the lords." Aristotle supposedly said that in 320BC. Two texts- Pneumatica and Automata which talks about building automatic machines, are attributed to ancient Greek engineer Hero of Alexandria. We find the mention of mechanical giants in ancient Greek texts. Leonardo Da Vinci built a Robot that can swing its arms and move its jaws, in around 1495. Tanaka Hisashige made Karakuri dolls in 1800s. These dolls were capable of drawing arrows from a quiver and shooting them. They could also be used for serving tea and were very popular among Japanese aristocracy.

Word 'Robot', taken from an old Slavic word 'Robota' meaning compulsory labor, was first used by Czeck writer Karel Capek in his play Rossum's Universal Robots. The play was about a society that build robots to serve them, but gets enslaved by the robots afterwards. Later in 1942, word 'Robotics' was used by Issac Asimov in his story 'Runaround', which also included his three laws of Robotics. Later he added Zeroth law saying- a robot may not injure Humanity or by inaction allow Humanity to come to harm. George Devol made a Robot in 1954 which was programmable. He Sold this robot to General Motors in 1960. General Motors became first company to use a robotic arm in its production line when it installed this robot called 'Unimate' in its Trenton, New Jersey plant. Researchers at Stanford, MIT and world-over, continued to develop newer, better Robots, bringing us to the current age of humanoid bots like ASIMO and ATLAS.

In old times people made robots using springs and pulleys and gears. Developments in the field of Electrical and Electronics in 19th and 20th century brought us Electric motors, sensors and microprocessors. These are the components we use in building robots now. Going about building your own bot, first you'll need to decide what you want your bot to do. It can be an obstacle avoiding robot, or a cleaning bot or a walking bot like DARWIN. Next you will need to decide what it will look like. Make a sketch or draw it on computer. After that you will need to select material for robot's body and select components which you'll put into it. Usually, the components which you will be needing are main processor board like an Arduino or Raspberry, controller board like Arbotix Pro Robocontroller. Motors could be geared DC motors, stepper motors and/or DC servomotors, normally referred as actuators. After that you may need wheels and casters, linear actuators, specialty robot motors, Bluetooth module or WIFI module or Ethernet for communication.

Stereo cameras or a webcam can be used for vision. Large number of sensors are available such as accelerometers, gyros, LIDARs, light sensors, temperature sensors such as LM35, voltage and current sensors, gas sensors, ground sensors, position sensors such as rotary encoders and potentiometers, touch sensors, Ultrasonic sensors for sensing distance from objects. Hydraulic pumps and pipes and solenoid valves may be required for some designs. LiPo or VRLA Batteries will be required for power supply. Other than this, you are going to need data cables and wires with connector, LEDs and displays, I/O boards, Motor driver boards, glue and bolts and some hardware tools such as pliers and keys and screwdrivers. You can always search for a particular part on Google and select from a number of vendors. Next you will require to program the processor for which you can use a programming interface and load the program from your computer using USB connector. After that you will need to put all the components together using your schematic. Microprocessor uses the feedback from a number of sensors to drive motors and pumps of the robot to accomplish tasks that it was programmed to accomplish. 

Accelerometer measures proper acceleration as a vector quantity in three dimension. Proper acceleration is acceleration relative to acceleration of the object in free fall. For the object under free fall, accelerometer will measure a value of 0m/s² and for an object sitting on ground it will measure a value of 9.81m/s² upwards. Accelerometer also senses orientation as it can sense the direction of weight. It can sense vibration as well as shock. Due to such properties, it is widely used in navigation system of aircrafts and spacecrafts and for balancing robots and to trigger safety mechanism in case of excessive shock or vibration. Accelerometer typically comprises of a spring mass system made of Piezoelectric, Piezoresistive or Capacitive sensor as spring and some mass with some damping medium such as a gas inside a housing. They are usually micro electromechanical systems or MEMS that can fit on a PCB. When the object accelerates,  mounted accelerometer also accelerates creating displacement of mass which stresses the Piezoelectric crystal or Capacitive sensor causing a voltage change which can be calibrated to give acceleration reading.

Gyros are used to sense rotation. They can measure angular velocity and the feedback along with position sensor feedback can be used for rotating arms or torso of the Robot or for maintaining balance in conjunction with accelerometer feedback. They measure angular velocity in degrees per second. Rotating gyro resists any change in its axis of rotation which is why it can be used for sensing orientation. Stereo camera is used for getting 3D images of surrounding, serves as 3D vision for robots. LIDAR is used for creating a 3D map of environment by shooting pulsed laser. It gives distance of objects around which is utilized by the processor in navigating the robot to tasks. It consists of laser, scanner and a receiver such as Silicon avalanche photodiodes. Ultrasonic sensors are also used to measure distance to nearby objects. It works by measuring time between transmission and reception of an ultrasonic signal. Current and voltage sensors are used with motors for precise speed control and protection. Temperature sensor gives temperature reading to the processor.

Honda started work on bipedal humanoids with prototype E0 in 1986. This was a slow walker taking 5s between steps. Researchers analyzed how humans and animals walk and run to get ideas and improved on their design. Next prototype E1 could walk at .25km/h. Model E2 was first to walk nearly like a human and could attain a speed of up to 1.2km/h. Model E3 could walk at 3km/h. Prototype E1, E2 and E3 were developed between 1987 and 1991. Next model E4 could perform quick walk at 4.7km/h. With more improvements prototype model E5 was made. E5 could walk autonomously. Engineers incorporated ground reaction control, target zero moment point control and foot planting location control to enable the robot to walk autonomously without falling. This was done to achieve more human like movement. Prototype E6 could balance itself on slopes or when faced with rough terrain. It could also walk on stairs without falling. After achieving reliable walking capability, engineers added torso, arms and head and first humanoid like prototype P1 was created. P1 could push carts. Prototype P2 (210kg) could maintain its balance when knocked. With improved sensors, software and body, P3 could walk faster, weighed less (130kg) and appeared more not like a science experiment at 1.57m. Development of P1, P2 and P3 was carried out between 1993 and 1997.

Engineers continued to research and improve on their work. On Nov 20, 2000 they presented ASIMO, standing for advanced step in innovative mobility. This humanoid robot was 1.2m tall and had abilities like intelligent real time flexible walk featuring predicted movement control. ASIMO was able to walk and turn more smoothly than earlier prototypes. By 2002, they had a more improved version able to recognize faces, postures and gestures, different sounds, objects under motion and surrounding environment. By 2005, ASIMO was able to carry beverages. Equipped with new posture control system, it was able to run at 6km/h in circular pattern. Height was slightly increased to 1.3m. Software was further improved to let ASIMO move more gracefully with the help of visual sensor, ground sensor and ultrasonic sensor. Ability to avoid obstacles was also improved. By 2007, ASIMO could recharge itself on sensing low battery using specially developed charging station. Two or more ASIMOs could work in collaboration now. They also added the ability to give way to people passing through.

ASIMO had a higher degree of autonomy by year 2011 as a result of continued improvements. The new ASIMO could determine its behavior based on information gathered with the help of sensors, from surrounding people and objects. Some of the components used by ASIMO are- lightweight but reliably strong Magnesium alloy body, gyroscope and accelerometer, 6 axis force sensor, joint angle sensor, 6 ultrasonic sensors in midsection, brushless DC servomotors and harmonic drive speed reducer, battery, IC communication card, floor surface sensors in feet. Some of the people involved in development of ASIMO are chief engineer Masato Hirose and project leader Satoshi Shigemi. Honda team has done an awesome job and they are not finished yet.

Apart from ASIMO, many other developers have created amazing bots.  UCLA Professor Dr. Dennis Hong is using DARWIN as a research platform to develop working humanoids. ATLAS is a humanoid developed by Boston based company Boston Dynamics, primarily for disaster relief ops such as search and rescue . As of February 2016, ATLAS can open and push a door to let itself out, maintain balance on rough snowy terrain and get up by itself when knocked down. It can maintain balance on a narrow beam of wood for considerable amount of time. It can also lift a heavy box and place it on shelf. ATLAS uses hydraulics for its movements, a stereo camera and a side camera for vision, a LIDAR for sensing its surrounding and gyros and accelerometer for orientation and balance. HRP 4 is a humanoid bot developed jointly by KAWADA Industries and AIST Japan. HRP 4 can walk, move its arms and legs in sync to make gestures and lift up to .5kg with its 5 fingers. NAO is a biped bot developed by Aldebaran Robotics of Paris, France. Bruno Maisonnier is the CEO. NAO can speak with coordinated gestures, tell jokes and stories and can even dance using pre programmed moves. It can get up on its own if pushed over. NAO uses 2 HD cameras for vision, four microphones, nine tactile sensors and eight pressure sensors among many other.

Under social robots we have Zenbo from ASUS, Tapia from MJI Robotics Japan, Aido from Aidorobotics and Jibo from Jibo Inc. For military purposes there is MQ-9 Reaper and MQ-1 Predator from General Atomics Aeronautical Systems, Packbot from IRobot, Massachusetts, TALON from Foster-Miller, Massachusetts and DRDO Daksh. Packbot and DAKSH are bomb diffusing bots used by armed forces and law enforcement. New Holland has developed an autonomous tractor for farm automation. Swiss company Sensefly makes eBee SQ drones which are used by farmers to watch over crop health. AgEagle is another drone that can watch over crops. Da Vinci series of surgical robots from intuitive surgical are remotely operated and have been performing surgeries for close to a decade. Smart Tissue Autonomous Robot or STAR did a better job sewing up a pig's small intestine than human surgeons. Amazon is testing delivery drones in order to deliver packages in under 30 minute. Food companies in UK and USA are testing autonomous delivery vehicles. YuMi from ABB, Baxter from Rethink Robotics and Nextage from Kawada Robotics, Japan are some of the industrial robots in use. We even have fully automated lights out factories like IBM's keyboard assembly factory in Texas.

       

After the disaster at TEPCO's Fukushima Daiichi nuclear power plant following the failure of emergency generators due to tsunami on March 11, 2011, many were hoping robots will come to rescue. None were up to task except for Packbot. Plant was dangerously radioactive for any human to enter. Packbot was sent in to take images of damage. At Osaka conference held in November of 2012, DARPA program manager Gill Pratt announced DARPA Robotics Challenge to encourage teams from around the world to build disaster rescue humanoid bots. DARPA was to provide funding of up to 4 million dollars to 8 teams coming up top in trials. To win the challenge, a humanoid had to complete 8 tasks in shortest time under disaster like scenario which included failure of wireless communication between bot and operator. The tasks included driving a vehicle, stepping out of the vehicle, walking on debris, opening door, going up stairs, turning on a drill and cutting through wall, opening a valve and a surprise task. 16 teams participated in trials dominated by team SCHAFT of Japan. Following the trials, SCHAFT inc. was acquired by Google and the team withdrew from finals. Their humanoid S-one was equipped with compact liquid cooled motors allowing for higher currents and therefore higher torque.

25 Teams competed  in finals held at Fairplex, LA county fair grounds on 5th and 6th June 2015 for the top prize of 2 million dollars. Of these were, team MIT, team Tartan Rescue from Carnegie Melon University, Team KAIST from South Korea, team Robosimian from NASA JPL and team IHMC robotics from IHMC, Pensacola Florida. Team MIT and team IHMC were using ATLAS. Prof. Russ Tedrake of team MIT had given his ATLAS as much autonomy as he can. Due to a small glitch the robot tried to step out of the vehicle while pressing throttle and fell down, breaking its right arm. Dr. Jerry Pratt led team IHMC. Jerry has been working on bipedal robots for more than 20 years, developing a sophisticated walking software. Team IHMC ATLAS also fell while walking over debris but without causing itself any serious damage. Repairs and error compensations were done overnight and the bot was able to finish all 8 tasks in second run. Team Tartan Rescue competed with their robot 'Chimp' which was the only bot to get up on its own after falling. Chimp with its motion planning ability was also able to complete all 8 tasks. Brett Kennedy of NASA JPL was leading team Robosimian. They competed with the quadruped bot Robosimian.

  

Team KAIST was led by Prof. Jun Ho Oh. After a disappointing performance in trials, Jun Ho Oh thought up some improvements to give their robot HUBO an edge. He put wheels at HUBO's knees and casters at its toes which allowed it to move forward while kneeling, increasing its chances of completing the tasks without falling. HUBO Could rotate its torso. Because of the air cooling system, the 33 motors in the robot could draw more current than rated, allowing it to be more powerful and faster. They put a supercapacitor system for emergency power to keep the bot balanced and communication up in case of main power failure. HUBO was able to complete all 8 tasks faster than other bots, claiming first prize. Team IHMC Robotics came second with a clock of 50:26 and was awarded 1 million dollars. Team Tartan Rescue came in third with a clock of 55:15 and received 500,000 dollars.

Graphene is a honeycomb like 2 dimensional hexagonal lattice of Carbon atoms. It is 100-300 times tougher than steel yet extremely lightweight at .77mg/m² and flexible. It is the best conductor of electricity and heat we have. It shows nonlinear diamagnetism, allowing it to levitate over suitable magnets. It is almost transparent and can be used to make semitransparent electronics. It can absorb light and has possibility to be used in solar cells. Continued research have shown that Graphene can be used in making classical and quantum transistors as well. Graphene can also be used to make best quality barriers. Due to such awesome properties, researchers are keenly pursuing ways of using it in building better bots. One possibility is using it as replacement for Silicone. It can also be used to make flexible, lightweight and extremely tough structure for robots. With Graphene actuators, future bots may become faster, more powerful and highly flexible. With Graphene as covering, they may also be able to harness sunlight for all their electrical power needs.

Quantum computing presents even higher possibilities for robots of future. Quantum processors equipped with sophisticated quantum algorithms can figure out global weather patterns, design stable new molecules or simulate human brain patterns. Robots equipped with quantum AI may be able to make decisions in real time factoring in huge amount of information from their surrounding, bringing them further close to human like behavior. Equipped with such abilities, robots of future may be able to carry out decision intensive tasks making their integration with human environment look even more natural. They are already taking over restaurant and fast food jobs. Zume Pizza of Mountain View California uses robotic chefs and Eatsa is a highly automated  fast food joint operating in San Francisco. Autonomous trucks and cars are taking over driving jobs. Google and Uber already have working autonomous vehicles and others are coming along fast. Robots are taking over the jobs of doctors, lawyers and accountants as well. They are increasingly being used for dangerous and dirty works in factories and laboratories. Robotic soldiers are poised to reduce the number of serving personnel. New technology brings new kind of jobs. There will be other type of jobs. People will have to just train accordingly. Future served by robots looks safer and more convenient. It's going to be awesome.

References:

1) http://world.honda.com/ASIMO/technology/index.html

2) http://www. ancient-origins.net/ancient-technology/steam-powered-pigeon-             archytas-flying-machine-antiquity-002179

3) https://learn.sparkfun.com/tutorials/gyroscope

4) https://learn.sparkfun.com/tutorials/accelerometer-basics

5) https://www.technologyreview.com/s/538136/a-transformer-wins-darpas-2-             million-robotics-challenge/

Image credits goes to respective sources.