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What is transhumanism?
Transhumanism refers to a cultural as well as intellectual movement which strives to improve human conditions that believes we can, and should, improve the human condition through the use of advanced technologies.
Life extension is one of the core concepts of transhumanism. Emerging technologies like genetic engineering, nanotech, cloning, etc. are the future of eternal life. In the same way, . Likewise, transhumanists implement new technologies to boost physical, intellectual, and psychological capabilities of humans beyond the natural capabilities.
TDCS, an acronym for transcranial direct current stimulation that quickens the reaction times. Transhumanism deals with the concepts of mind uploading to a computer, and what happens when we finally create a computer that has higher human intelligence.
How Big Data is Changing Our Lives
Using big data generated on social networking platforms is an emerging technique to predict collective consumer behavior. Techniques of big data support multifarious applications.
For instance, in the healthcare industry, you can predict flu outbreaks on the basis of number of tweets comprising of flu-related keywords, understanding patterns of human mobility by analyzing mobile phone records, or forecasting financial success of a commercial film by analyzing page view statistics of Wikipedia articles related to the film.
The common factor among these examples is the principle of quantifying and measuring individual’s activity collectively to understand the human society in a computing framework.
Human’s quest for knowledge has resulted in technological advancements from the recent centuries. Nowadays, we have access to a great deal of information; the micro elementary particles right up to distant galaxies, have explored each aspect of human life.
Still, many questions are unanswered, but the high level of knowledge creation and our increased level of understanding cannot be overlooked. Technology has resulted in inventions in the field of natural sciences, medicine, engineering, etc. that affects your daily life.
Huge improvements in natural sciences in 17th and 18th centuries, especially in physics, have laid the foundation for new convention of modern science, one that is based on experiments, measurements and quantitative modelling.
A modern scientist follows this principle to understand universal patterns and laws governing natural phenomena accurately by analyzing current states, thereby predicting the future behavior of the system.
On the contrary, social sciences performed real- experiments that quantify and measure specific parameters thus providing a mathematical model to clearly describe empirical observations that is challenging for enterprises. Natural systems require one to observe and monitor systems continuously and perform all necessary measurements.
On the other hand, studying social systems is not only a complete observation of all the complex actions and interactions, but it is challenging to define measurable parameters.
How is it possible to quantify the level of dissatisfaction of the members of a society? How can you measure the kindness of an individual? What is the strength of peer pressure and social interactions? Can we measure it quantitatively? Even if it is possible, how can we monitor the entire system and continuously track all parameters subject to different situations.
Social sciences are limited to qualitative observations without being able to predict future behavior of this system accurately.
The human system is continuously evolving; our lives are transforming into a digital world. Our social interactions are leaving behind a digital footprint. We daily interact over Facebook, Twitter, Instagram, and many other social networking channels and produce huge amount of Big Data. Digital data we produce daily ranges from online banking, ecommerce, social networks, online campaigns, petitions etc.
Most of the data is stored for varied reasons; cell phone providers record your data to issue monthly bills, Google records your browsing history to return better search results. For instance, Amazon records and analyses your purchases for making accurate product recommendations; Facebook monitors your “likes” and “pokes” to increase your social reach. Recording and analyzing such diverse data helps enterprises in understanding customer experience and user behavior.
For example, a politician can assess his/her popularity by taking account of number of Facebook likes or followers on Twitter. This method outclasses the traditional methods of conducting public surveys using questionnaires.
Large scale analysis can take place to reveal insights about gender dependent characteristics of human communication patterns.
Undoubtedly, humans are more complicated than atoms or stars and planets, but technology and artificial intelligence will help represent universal laws of human beings in a numerical framework.
Designing smart cities depends on real-time data analytics of daily human activities. Transportation systems will organize themselves automatically to optimizing flow of traffic; efficient health services will improve by prioritization of public demand; transparent financial services will be introduced, and democratic processes of policy formulation are some of the practical outcomes we can expect in future with the Big Data revolution.
How IoT changes our lives
We are already experiencing IoT because we are using our smartphones to control the air-conditioner, lights, security locks and other devices installed in our smart home and ffice. Smart cars have in-built sensors that control the car functions and generate useful data to avoid traffic jams.
In this section, we shall learn about how IoT has a positive effect in our daily lives.
Smart schools helps students connect with virtual libraries, research, assignments etc. Students will be able to learn quickly and it will improve the teacher’s efficiency. Even schools including visually impaired students can provide special cards that will enlarge font sizes automatically. A central hub for recording attendance will control absenteeism. Students will gain access to educational resources and tools using their mobile phone in order to complete their homework.
Enterprises also stand to gain competitive edge using the benefits of IoT. You can create new services and revenue streams along with the traditional products and services. Vending machines, for instance, can offer enterprises with inventory management to ensure smooth supply. You can track sales, frequency of items sold, high traffic days, etc.
Wearables are transforming our daily lives. The smart device tracks your heart rate when you exercise. You can control your electronic appliances like lights, cooker, etc. from your smartphone.
Need for human centric innovation
Human-centric innovation refers to design, development, and deployment of initiatives combining human and computer systems. It is concerned with practices and systems of technology use while human-computer interaction focuses on ergonomics and the usage computing artifacts and information science is focused on practices surrounding the collection, manipulation, and use of information.
In this type of computing, practitioners belong to various disciplines like computer science, human factors, cognitive science, sociology, communication studies, psychology, anthropology, graphic design and industrial design. Few researchers lay emphasis on understanding humans, both as individuals and in social groups; they focus on how humans adopt and organize their lives around computational technologies. While, others study about designing and developing new computational artifacts.
Governing algorithms: Algorithms have become popular in making human decisions. It ranges from online search or social media news areas to areas where decisions are made exclusively via human judgement, like health care or employment; algorithms are becoming important tools, or even sole decision-makers.
Digital transformation generates a wide variety of datasets known as ‘big data’. Till now, we know about how big data is produced and stored. Actually, algorithms help us draw some sense out of this vast data pool.
Algorithms are known as the brain of computing devices like computers, mobiles, and IoT devices. However, ethical guidelines must govern the proper use of these algorithms.
Safeguard crucial information: Algorithms basically decide what attracts attention, and what is ignored, what gets published, and what is censored. Algorithms are gatekeepers influencing how we react.
Subjective decisions: Few algorithms may not have a clear answer in ‘yes’ or ‘no’. These lead to subjective answers; however, predictions resulting from algorithms does not guarantee that it is the right answer.
Knowledge of working principles of algorithms: Today, algorithms are not easy to comprehend. Lack of transparency of the code adds to the problem. Complex calculations involve several steps including thousands, or even millions, of individual data points. Often, even the programmers cannot predict how an algorithm will decide on a certain case.
Transparency in data analytics: Enabling complex algorithms having complete transparency is challenging. It is not sufficient to publish source code of an algorithm, because machine-learning systems will finally make decisions that have not been programmed directly. Complete transparency would require that we should explain why any particular outcome was produced.
Few investigations come with reverse-engineered algorithms in order to create greater public awareness about them. That is similar to a watchdog function. Transparency is often not possible because public access might make the source code vulnerable to manipulation.
Communicating to users: Consumers must be given control over personal information that is stored in these algorithms. User notifications from app developers include rights to edit personal information and choice of being excluded from databases of data vendors.
Direct regulation: Public regulators are often prone to manipulation of algorithms. Algorithm regulations are a major concern for the finance industry. Automated trading comes with the potential of destabilizing financial markets; regulators must be able to modify such algorithms to increase data security.
Consider user safety while creating an algorithm because you are dealing with sensitive data of your customers.