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| For a better future... |
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| Image Classification using Deep Neural Networks |
To make you feel at peace with these concepts, let us consider real world examples such as the automated personal virtual assistant applications like Siri. It applies Machine Learning and Natural Language Processing (NLP) techniques to respond to queries based on the user's input data. Applications like Google Photos use Machine Learning capabilities to detect faces in group photos, select the best moments based on users’ choices and add a fitting soundtrack to produce a custom movie for them. The Recaptcha that we encounter everyday while browsing the internet employs ML and works on the premise that by marrying up users who need to prove they are human to data that needs transcribing, benefitting both the sides. An existing deep learning architecture (ML) has been adapted to solve the detection problem in camera-based tracking for augmented reality (AR) - a technology that superimposes a computer-generated image on a user's view of the real world, thus providing a composite view. Sounds cool, right? That's why AI and ML have gained popularity in recent days.
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| Sophia (Robot) |
Behind the scenes, it gets dirty. The basis for effortless estimation is the troves of raw information, streaming in, and stored in enterprise data warehouses. Actually, analyzing big data on a granular level is near impossible. This is where Data Scientists come in. Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. It is ultimately about using data in creative ways to generate business value. Data Scientists all over the world are trying to discover efficient data compression algorithms and storing methods to handle the huge amounts of data (Big Data) pouring in due to the recent advancement in technologies.
Finding correlated data with big data is like trying to find a needle in a haystack. What’s more, certain patterns may not be obvious to research. We lose precious data in a sea of noise. This is where Quantum Computing comes to the rescue. Quantum Computing is the use of quantum-mechanical phenomena such as superposition (adding up properties of particles in an entangled state) and entanglement (a physical phenomenon that occurs when pairs or groups of particles are generated, interact, or share spatial proximity in ways such that the quantum state of each particle cannot be described independently of the state of the other(s), even when the particles are separated by a large distance) to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically.
Big data is one of the areas in which the marriage of artificial intelligence and quantum computers shines. Powerful quantum processors would be able to handle massive data sets, and artificial intelligence could analyze big data on a granular level. Researchers are already using artificial intelligence to analyze large data sets. A renowned medical startup has already started using AI to expedite cancer research. Artificial intelligence and quantum computers, once fused, will most likely improve each other — artificial intelligence tasked with improving quantum computing processes and vice versa. This means analysis of giant data sets will become more manageable, and perhaps, more efficient as time goes on and as these technologies progress. This could even aid humans in understanding a multiverse (a hypothetical group of multiple universes including the universe in which we live. Together, these universes comprise everything that exists: the entirety of space, time, matter, energy, and the physical laws and constants that describe them).
Cloud Computing (the practice of using a network of remote servers hosted on the Internet to store, manage, and process data, rather than a local server or a personal computer) or Blockchain Technology served to address the storage issues. So, what is Blockchain Technology? Well, a blockchain, in the simplest of terms, is a time-stamped series of immutable record of data that is managed by cluster of computers not owned by any single entity. Each of these blocks of data (i.e. block) are secured and bound to each other using cryptographic principles (i.e. chain). Picture a spreadsheet that is duplicated thousands of times across a network of computers. Then imagine that this network is designed to regularly update this spreadsheet and there you go, you have got a glimpse on the basics of a blockchain. The blockchain database isn’t stored in a single location, meaning the records it keeps are truly public and easily verifiable. No centralized version of this information exists for a hacker to corrupt. Hosted by millions of computers simultaneously, it's data is accessible to anyone on the internet. Originally devised for the cryptocurrency (a digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank) Bitcoin, Blockchain Technology is now being hailed as the New Internet by everyone around the world.
Although Artificial Intelligence dramatically improves our world in many ways, there are notable concerns regarding the forthcoming impact of it on employment and the workforce. Tasks which are monotonous, can be easily automated; this can gradually make certain roles obsolete. For instance, tasks and activities related to customer care/call center operation, document classification, discovery and retrieval, content moderation are more and more based on technology and automation and less on human work. The same is true for roles related to operation and support of production lines and factories: humans are being replaced by smart robots which can safely navigate the space, find and move objects (such as products, parts or tools) or perform complex assembling operations.
Under certain assumptions, and following the initial disruption due to technological unemployment, the AI revolution, will lead to a new era of prosperity, creativeness and well-being. The need for human employment to perform redundant tasks will reduce exponentially. The workforce and the underlying employment models will move from long-term, full-time employment agreements, to flexible, selective premium service offerings. There will be a stream of new business opportunities empowering a culture of entrepreneurship, creativeness and innovation. The above positive scenario requires a common, shared understanding of the technology, its opportunities and its risks. People need to adapt to the new technology landscape, become more flexible and also inherit an attitude of lifelong learning, collaboration, innovation and entrepreneurship. States need a new strategy with focus on education; they need to rethink how markets, companies and employment agreements should work in the new era of intelligent automation; they need to redesign the social mechanisms to cover a range of new scenarios and situations. Also, "basic minimum earnings," or a universal basic income (a cash hand-out, distributed irrespective of employment status), should be instituted so that it is enough to cover the cost of living and provides financial security to people.
Last but not least, artificial intelligence encompassing machine learning and deep learning will eventually become a “game changer” for climate change and environmental issues.
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