In order to define artificial intelligence, it is important to define what intelligence actually is. Intelligence may be defined as the computational ability to accomplish a set goal or meet a set objective.
Intelligence varies in various degrees in people, animals, and even machines for that matter.
Artificial Intelligence can be broadly defined as the applied science of making intelligent machines, and highly intelligent computer programs to be specific. In other words, Artificial Intelligence is the process of design and development of computer programs that have the required skills and knowledge to undertake tasks that would otherwise use human intelligence to accomplish.
Intelligence requires mechanisms, and research in the field of artificial intelligence has been discovering how to carry out these mechanisms, and sometimes even simulate human intelligence.
At TekTorch, there are three major principles that guide the development of artificial intelligence:
Artificial intelligence systems are usually expected to exhibit the following characteristics:
The development of Artificial Intelligence can be broadly classified into two categories:
This is a term used to describe intelligent software that has the ability to simulate human intelligence and do a single task extremely well, mostly within a limited context. A birds-eye view may seem like the machine or the program is intelligent, but it operates under a lot of limitations and constraints as compared to the most basic of human intelligence. Some examples of narrow/weak AI include image recognition, personal assistants, checking the weather, analyzing raw data, and other rule-based programs.
Most AI that exists today is narrow/weak AI. Unlike other types of Artificial Intelligence, narrow AI is not sentient, conscious, or driven by emotion (that is, lacks human touch). While it is true that tools with narrow AI can interact with and process human language, the real reason we call it weak AI is that simply put, they can’t think for themselves.
It is also true that calling it narrow or weak AI is undermining how big of an innovation it actually is. Most narrow AI is powered by machine learning and deep learning. This means that when a machine is fed with a training data set, the computer uses statistical techniques to get progressively better at the task at hand, by means of both supervised and unsupervised learning.
Narrow AI has relieved us of a lot of mundane and monotonous tasks, and the uber-fast data processing has certainly improved overall productivity, efficiency, and quality of work.
Artificial General Intelligence, also known as Strong AI is often used to describe a machine that can undertake all tasks that a human can. In other words, artificial general intelligence is supposed to exhibit intelligence identical to that of a human and undertake any intellectual task that a human can.
AGI currently seems to be the holy grail for all AI researchers, and the quest to create a machine with a full set of cognitive abilities is dystopian science fiction at best with the current know-how available to us.
Currently, machines can in fact process data faster than us and perform all rule-based processes with consistent and better ease and accuracy. But the fact that humans can strategize, memorize, and think abstractly and creatively to make consciously aware decisions make us infinitely better than machines.
This intelligence is also very hard to define and programmed into machines because it is essentially driven by our ability to be sentient creatures.
AGI is expected to solve problems, reason, plan, learn, and integrate prior knowledge and inculcate a sense of sentience by being capable of experiencing consciousness, which is pretty far off from where we are today.
Artificial superintelligence, a relatively recent discussion has been around the development of machines that - wait for it- outperform humans! It is a hypothetical intelligence that will surpass the human intellect and cognitive performance in all aspects, be it general wisdom, creativity, problem-solving or emotional consciousness. Researchers predict that the leap from AGI to ASI will be a short one, but no one can really say when the first sentient computer will arrive.
This is the kind of AI that is generally perceived as a threat, and which has led the likes of Elon Musk to suggest an outcome as far out as the extinction of mankind
The AI hype:
The advent of AI WInter
Today, artificial intelligence is being used more than ever, both for the good and the not-so-good. It’s everywhere around us - from Healthcare to Defence, in music, and in books, in judging the resilience of markets and people, and influencing people’s consumption patterns, especially on social media. It is influencing your decisions consciously or subconsciously.
But wherever there’s hype involved, there’s misunderstanding. Today artificial intelligence has become such a big umbrella term that it is often misunderstood or misused. However, there are certain avenues that are making monumental breakthroughs owing to the enormous processing power and the ability to train mathematical models through accessible code. Some sub-domains of AI that are on the rise are Natural Language Processing, Computer Vision, and the biggest of them all - Machine Learning. A major reason for these domains suddenly turning up is simple - the abundance of data. This, when coupled with the advances in computational power/performance has given rise to the recent renaissance, and now Data is the new Oil!
Everything said and done, the fact remains that we are very far away from potent and ethical artificial general intelligence. Though the field has grown explosively in recent times, the only systems that currently exist are bundles of narrow AI - that is, they can only produce meaningful results inside a narrow domain. Here are a few things that have been achieved by means of narrow AI so far:
Yes, these achievements are impressive, but it is worth noting that the current principle applications of AI are relatively specific and straightforward with respect to human intelligence.
Currently, the modus operandi of even the most powerful AI is to solve specific tasks, but developing true artificial general intelligence (AGI) requires the development of an entire cognitive architecture that covers all aspects of cognition.
The immediate future will see the current trends with machine learning and deep learning continue, but soon enough questions will begin to surface about the efficiency of such brute processes, considering the current methods have a significant carbon footprint.
As we move towards AGI and the world realizes the true potential of ethical AI, the community will demand new methods to get things done. This would also finally be the period when the world learns that AI is not magic and begins to see the limitations of what is, at the core, just a ton of math. AI winters and hype cycles will begin to disappear, making way for steady and sustained AI innovation.
For artificial intelligence to truly reach the levels of human intelligence or greater, unpredictable radical changes may take place. The rise of Quantum computing will give way to a very probabilistic nature of AI given its nature and models can be trained in ways never possible before.
Here is a list of some traits for machines to develop, to realistically plan for a leap towards AGI:
With a boom in recent years, AI has come a long way to help organizations supercharge their business performance. The massive strides in AI, however, could not have been possible without improvements and enhancements in the computing power and underlying programming languages.
Here are some of the most popular and efficient languages and frameworks for the development of artificial intelligence:
Python was first developed in 1991 and has surpassed Java to be the second most popular language as per Stack Overflow. According to a poll, about 60% of AI developers and engineers prefer Python as their first-choice programming language for the development of AI solutions. Python is incredibly easy to learn and offers easy access to the world of AI development for programmers and statisticians alike.
Python is hands down the number 1 language when it comes to artificial intelligence development. Advantages of Python include an easy and simple syntax, faster development as compared to Java and Ruby, and a ton of libraries and tools to support the development of artificial intelligence
Some popular libraries in Python are:
For over 20 years now, Java is considered to be the best programming language of the world owing to its high-user friendliness, flexibility, and platform independence.
Java has been used for artificial intelligence development in many ways, and some libraries available for the development of AI are:
Created by Ross Ihaka and Robert Gentleman in 1995, R is the implementation of S programming language and is best used for data analysis and statistical purposes.
The biggest feature of R is its comparatively raw NumPy package which makes it the best choice for large scale number crunching. R also covers all paradigms such as functional programming, vectorial computation, and object-oriented programming.
Some advantages of working with R are - the availability of various libraries and packages for greater functionality, the ability to work with Fortran and C++, high-quality graphs, and an active support community.
Some packages for AI and machine learning programming available in R are:
LISP was created by the founding father of AI - John McCarthy in 1958. It is an abbreviation for list processing and is the second oldest programming language after Fortran. It is famously said that apart from Haskell, LISP is the only language where the programmer spends more time thinking than typing. For Machine Learning, LISP provides for the following features:
There are numerous challenges that the field of AI currently faces. Currently, despite huge strides made in AI R&D over the past decades, even the most powerful of AI techniques is to solve a specific task effectively. Even the best AI is not sentient, conscious, or driven by emotion. True AGI development calls for the development of a cognitive architecture that incorporates all aspects of cognition.
Reasoning essentially means drawing inferences appropriate to a given situation and can be broadly classified as inductive or deductive. And though there has been some amount of success in programming machines to draw inferences, true reasoning is more than just drawing inferences, it is all about drawing inferences relevant to a particular situation. This makes reasoning one of the hardest problems confronting AI development right now.
Problem-solving, in the context of artificial intelligence, can be defined as a systematic operation through a host of actions to come to a solution. Problem-solving can either be general or special purpose. Special purpose problem-solving is made for a specific problem and makes use of the very characteristics of the situation in which the problem is embedded. On the other hand, general-purpose techniques used in AI is a stepwise or incremental reduction of the difference between the current situation and the desired situation.
Knowledge can be defined as the judgement of the understanding of a person or a machine on a given subject. It is responsible for representing information about a problem in such a way that a computer can understand and utilize this information to solve complex problems. Depending on the type of functionality, knowledge representation in AI can be classified in the following ways:
Knowledge representation is more than just storing data into a database, it is about enabling an intelligent machine to learn from that knowledge and experiences to behave intelligently like a human.
Planning refers to the sub-domain of AI which focuses on finding the appropriate course of action in order to accomplish the given task by the machine. The idea is to reach the goals while optimizing overall performance measures.
Planning involves the choice of a sequence of actions that will be followed step-by-step by the machine in order to accomplish the set final goal.
A whole range of learning methods can be applied to artificial intelligence, the simplest of them being learning by trial and error.
The machine then stores the solution and recalls it the next time it encounters the same problem. This memorizing of procedures is also known as rote learning.
However, the challenge before AI is about applying past experiences to analogous new problems to come up with a solution, and this is called generalization.
A language may be defined as a system of signals having meaning by a set convention. A defining characteristic of full-fledged human languages is productivity.
It may be relatively easy to write programs that respond to human language to questions or statements albeit in severely restricted contexts.
Some steps in NLP are:
The big challenge for the development of such an AI is a genuine understanding of the language. Can the machine reach a point where its command of language is indistinguishable from a human? With the rate at which NLP is progressing, that day may not be as far away as one might think.
Perception may be defined as the scanning of the environment by means of sensors - both real or artificial, and the environment is then decomposed into separate objects in various spatial relationships. In other words, it is the ability to deduce things about the world from visual images, sounds, or other sensory inputs.
At present, artificial intelligence can sufficiently identify individuals, enable autonomous vehicles to drive on open roads amongst other things. But the challenge to overcome is that analysis can be complicated by many factors such as the angle form on which the object is viewed, the intensity of illumination, or the contrast with the surroundings.
The field of robotics is closely related to Artificial Intelligence and makes use of highly sensitive sensors on a robot to avoid and navigate around obstacles.
Though the present intelligence capabilities do allow a robot to undertake and perform tasks such as object manipulation and navigation, the real challenge for the development of AI lies in the secondary-problems of localization - that is, knowing where the robot is, mapping - learning what is around in the robot’s environment, and motion-planning - figuring out how to get there.
The boom in AI means that now is the right time to talk about the boundless landscape of artificial intelligence, which means that there is a frontier for ethics and risk-assessment as big as it is for emerging technology.
Some of the social challenges that AI will inevitably have to navigate around are:
Artificial General Intelligence, aka Superintelligence is a state of artificial intelligence where it exceeds human intelligence in virtually every field, including creativity, scientific temper, general wisdom, and social skills.
General Intelligence is a utopian concept as of now, as we neither have the resources nor the know-how to build artificial intelligence at par with human intelligence, let alone beyond it.
Cybernetics is the study of communication and control systems in humans and machines alike. Over the years we have had science fiction portray robots which are essentially cyborgs - flashback to the Terminator or I, Robot. And true to the saying that science fiction is the precursor to science fact, AI has not only caught up with the fiction but also laid out general practicalities. Research shows that a robot can successfully have a biological brain with which it can make independent decisions. Research is also consistently making strides, and along with the increasing number of cultured neurons, the range of sensor-based inputs is also expanding to include audio, IR, and Visual stimulus.
Human brain + machine interfaces are still possibly years away, considering studies today generally employ rat neurons. And if indeed we get there and bring about a robot with a human-neuron brain, there’s still a ton of social and ethical questions that need to be answered.
Today artificial intelligence is mostly about Deep Learning or Artificial Neural Networks, but there was a time not so long ago when Symbolic Artificial Intelligence was all the talk of the town. Also known as the classical AI, rule-based AI, or just the good old fashioned AI, Symbolic reasoning involves the embedding of human behavior and knowledge into machines and programs. Symbolic AI was also considered to be the road to AGI.
The early pioneers of AI firmly believed that every feature of intelligence can be precisely described in a manner that a machine can be made to simulate it. Soon enough, Symbolic AI took the center stage and a lot of tools and concepts in computer science were a result of these efforts. Symbolic AI programs were based on creating explicit rules for structures and behavior.
But Symbolic AI had its limitations, for example when a machine had to make sense of the contents of an image or a video. These processes aren’t explicitly rule-based, and symbolic AI starts to break in such a scenario.
AI futurists believe that symbolic AI will die, but this assumption couldn’t be farther from the truth. Rule-based AI systems are very important in today’s AI applications and will continue to be so.
The difference between a Symbolic and a Sub-symbolic AI lies in the way the models learn. The sub-symbolic approach to artificial intelligence is principled on the fact that human-based information format is not always the best fit for AI, and advocates for the raw data to be fed to the model for the machine to analyze and construct its own implicit knowledge about it.
Sub-symbolic AI is more revolutionary, futuristic, and easy on the developers since the machine learns by itself. But the issue with sub-symbolic AI is that they are intensely data-hungry. Examples of a sub-symbolic AI system are Computer Vision and Neural Networks.
Statistical Learning can broadly be defined as a set of tools for understanding data and can be classified as supervised and unsupervised learning. Supervised Learning is the practice of estimating an output based on one or more than one input. On the other hand, unsupervised learning aims to find a pattern in the given dataset without a supervised output. Essentially, statistical learning focuses on calculating probabilities for each hypothesis and gives results accordingly.
Statistical learning techniques, though powerful, are not applicable in many AI scenarios in the real world because just as machine learning is inherently exploratory, statistical learning is inherently confirmatory.
There is growing momentum in favor of hybridizing Symbolic and non-symbolic (connectionism) approaches to AI to create an intelligent machine that can make decisions.
Symbolic AI is the right strategic complement for the ever-popular connectionist techniques. As Artificial Intelligence proliferates deeper in our lives and our ask off AI becomes increasingly sophisticated, it would probably be the best step forward to develop a system that makes use of the strength of both systems while mitigating each other’s weaknesses.
A fundamental part of an efficient search is to understand the context of queries. Artificial Intelligence is disrupting all areas of knowledge activities today, and online searches are no different.
It is important for these searches to be precise, quick and respond in the right context. In this regard, AI information retrieval is an up and coming field, which focuses on returning context based search results, not just keywords. Using deep learning and neural networks, it is now possible to build a robust, scalable and semantic search engine.
Logic may be defined as the ability to analyze facts and arrive at a conclusion based on those facts, which in turn helps a human or a machine to arrive at the best solution to a problem or a question.
Logic is essential for intelligence of any kind, and is what makes AI intelligent. Normal programs are written with a predefined set of rules that are supposed to be followed, but the objective of AI is to make decisions by itself.
Probabilistic reasoning is a way of knowledge representation here the concept of probability is applied to represent the uncertainty in knowledge. Probabilistic reasoning is needed for the following reasons:
Probabilistic modeling provides for a framework to understand what learning actually is. It has therefore emerged as a top principle theoretical and practical approach to design machines that learn from data acquired through experience.
In Machine Learning, classification is a supervised learning approach in which the machine learns from a given dataset to come to conclusions, make observations or classifications. Classification can be performed on both structured and unstructured data, and the classes are often referred to as target, label, or categories.
Following are some classification algorithms:
A neural network, inspired by the neurons in the human brain, is a network made up of neurons arranged in layers that take an input vector and subsequently convert it to an output. Each neuron takes an input and applies a nonlinear function to it and then passes the output to the next layer, which takes it as an input again.
Artificial Neural Networks have a very high tolerance to noisy data, and they’re also able to classify untrained patterns and perform better with continuous-valued inputs and outputs.
A drawback of Neural Networks is that they have poor interpretation as compared to other models.
TekTorch is an AIaaS company based out of Sydney, and we help founders with highly specialized consulting in building scalable web and AI/ML solutions that bring tremendous business impact. We’ve worked with companies in various industries spanning oil and gas, telecommunications, tourism, finance, to name a few, and our team understands business challenges associated with these different sectors and possesses the technical acumen to solve these problems.
TekTorch works with businesses of all sizes and at different stages of their growth.
In a world of rapid technological change, staying ahead of the pack can deliver greater value and revenue to your business. Using state-of-the-art Artificial Intelligence and machine learning to perform tasks with greater speed, intelligence, and accuracy, we allow your business to operate more efficiently – and grow.
NLP or Natural Language Processing is the set of computational techniques that analyze and synthesize human natural language and speech. Simply put, we write effective algorithms that teach the machine to understand and manipulate human language and speech.
We take pride in our expertise with NLP, and here’s what makes our offering super effective :
CV or Computer Vision is the field of AI that aims to replicate the complexity of human vision in order to enable machines to identify and process objects in a way humans would do.
Computer Vision has been around for decades, but it takes an in-depth understanding of real-life use cases for the tech to move out from labs to a real-world setting. With our expertise working with Computer Vision over the years, here’s why you should consider our offering:
Robotic process automation duplicates human execution of repetitive processes with existing applications by using software robots that are configured to capture and record these applications.
Intelligent process automation is the journey towards automation leveraging artificial intelligence to the fullest, and as opposed to traditional automation solutions, robotic process automation can be deployed on top of existing legacy systems.
With all our experience deploying RPA systems across industries, here are a few reasons to consider our RPA offering:
Machine Learning is a subset of artificial intelligence that teaches a machine how to learn, and predictive analytics in ML models look for patterns in a given dataset to draw inferences just like a person would. The objective is to get the algorithm really good at coming up with the right conclusions with the given data, at which point it can apply the knowledge to a new dataset.
In our experience, when applying Prediction and Classification to a business problem, there are some fundamental questions that need to be answered that determine if the implementation would be a success. Following are a few evaluations we like to make, which usually translate to successful business outcomes:
Reinforcement Learning is a sub-domain within machine learning that is focussed around training an algorithm around the trial and error approach. Broadly, Reinforcement Learning encompasses a class of algorithms to optimize decision making through rewards and penalties (or, negative rewards) as they transition from one scenario to the next.
The key goal of Reinforcement Learning systems by TekTorch is to establish a best sequence of actions in order to maximize the long term reward when solving a problem.
Some important use-cases of Reinforcement Learning are:
RL is generally valuable when searching for optimal solutions in a constantly changing environment.
Deep Learning is both a sub-domain of machine learning and an extension of it, where the machine abstracts layers of algorithms to build an artificial neural network that learns and makes predictions.
Deep Learning is still relatively nascent, and organizations willing to explore Deep Learning should ideally be well informed about the challenges that they may encounter along the way:
TekTorch, with its seasoned team of developers and consultants are adept at developing production grade AI across business verticals, and deliver results that solve real-world business problems to fuel business performance.
It’s a fact that the supply of good AI engineers is far less than the demand, and bulk of the work in AI is thankless and boring stuff like ingestion, organization, transformation, evaluation and storage of data. Only a small part of the job involves designing networks, tuning parameters and choosing activation functions (which is actually pretty fun!)
The world is a long way from solving the problem of AI talent scarcity. Building an in-house AI team is an expensive proposition. Given the costs, consider asking these key questions to fasten your journey to AI adoption
By eliminating the need to build in-house capability, you get rid of all the ancillary expenses that come with the arrangement. Bringing in a team of experienced and expert individuals dedicated to your AI initiatives can maximize revenue and transform your business into a successful AI company.
At TekTorch, we’ve come up with a dedicated Virtual Chief Technology Officer (CTO) offering, and have a solid team experienced in building projects, understanding data sets and delivering AI products.
The overall objective of a CTO is to take hold of an organization’s IT strategy in order to maximize operational and financial efficiency to sync with overall objectives.
Small and medium businesses and startups have a perpetual struggle with capital, and it is imperative that they make the best possible use of available resources for efficiency. Onboarding a full-time in house CTO can be a costly proposition. Also, designing a compact and results oriented artificial intelligence team is extremely important for the business to sustain and succeed.
You probably need a Virtual CTO to assist you if you want to:
With the sheer pace of technological advancements, it is extremely important for businesses to adapt and grow with these advances in order to stay in the game and not be left behind. It is therefore prudent to eliminate the need of an in-house CTO, and instead onboard a virtual chief technology officer.
As your virtual chief technology officer, TekTorch has the ability to:
Virtual CTO is a game changer for organisations that are willing to experiment with AI capabilities but want to do it quickly without the risk of time and capital.
Chief Technology Officers are typically responsible for the entire organizations’ IT strategy in order to maximize operational and financial efficiency to sync with overall objectives. It is extremely important for CTOs to design a compact and results oriented team and strategy, even more so in the case of Artificial Intelligence for businesses to succeed and sustain.
And building sustainable AI development practices goes beyond just surface understanding of data science and machine learning. Almost always, it requires a union of strong IT knowledge with the domain knowledge of the problem you’re trying to address. In terms of the talent required, to build a winning team here is what you will need:
data for business decisions.
The waves of impact made by AI is starting to be felt across industries, and the impact in Healthcare is touted to be a game changing one for sure. Over the years, the normous innovation in Healthcare has seen the development of high-precision diagnosis and treatment procedures. But now, with the newfound ability of not just replicating human actions and decisions but also learning from them, Artificial Intelligence is bringing about a paradigm shift in the Healthcare industry.
The applications of artificial intelligence in Healthcare are immense, and as per a 2019 CB report, around 86% of healthcare provider organizations and life sciences companies will look to incorporate AI in healthcare by 2023. The fact that the pandemic has hastened this process is clearly visible, with AI healthcare startups playing an important part in the race against time to find a vaccine for Covid-19.
Apart from drug discovery and clinical trials, some areas where artificial intelligence is all set to make concrete inroads in the life-sciences and healthcare sector are -
As the world moves towards a predictive model of care than a reactive one post the pandemic, AI and ML enabled healthcare is the way to go, especially in the case of chronic health conditions, such as diabetes, cancer and heart ailments among other things.
In a world which is beginning to rapidly move towards self-driving and autonomous vehicles, artificial intelligence has begun to play a central role in the automotive industry. With the manufacturers implementing an entire range of human-computer-interactions (HCI) such as voice assistance, gesture-sensitive systems and personalized platforms, artificial intelligence in the automotive industry is witnessing implementation at an exponential rate.
On top of autonomous driving, there are numerous applications for AI in automotive sector such as:
With sustained advancements in computer vision, predictive analytics and machine/deep learning, artificial intelligence is set to set the pace of innovation for the automotive industry in the years to come in order to make automotive cheaper and autonomous.
The construction industry has traditionally been labour intensive, and continues to be so, albeit attempting to shift from an analog past to a digitalized future. The challenges in front of the construction industry today are unique - they are faced with cut-throat competition, stagnating growth in productivity, and a chronic shortage of labor. Though traditionally averse in adopting new technology, construction industries today are increasingly looking towards artificial intelligence, machine learning and computer vision as a means of transforming how they go about their business.
Some areas where AI, ML and computer vision can enormously benefit the construction industry are:
The adoption of AI and cognitive computing in construction is all set to keep increasing at a rapid pace, and even though it will remain to be human-driven for the foreseeable future, the use of AI, ML and CV among other things will massively augment the capabilities and improve business outcomes for the construction industry.
The financial industry have traditionally been early adopters of tech, and have always been on the lookout for the next big thing to improve their offerings and smoothen out theri systems.
Cognitive technologies hold the key to change the face of the BFSI industry, and artificial intelligence and blockchain powered FinTech is the latest booming area of interest that is bringing about a renewed phase of digital innovation in the financial industry.
FinTech isn’t exactly a new kid on the block, it has been around for quite some time now and lately has evolved very rapidly in a very short time. While FinTech was all about facilitating payments and transactions, FinTech 2.0 is all about making use of cognitive computing like ML or NLP or blockchain for that matter, and doing a whole range of tasks right from processing credit risks to running entire hedge funds and navigating the complicated realm of compliance/regulation.
Driven by artificial intelligence, blockchain and cryptography, here are some areas that will be impacted by the latest wave of FinTech innovations:
The rise of crypto-economic models is also set to disrupt the financial industry, and tokenization of economy and digital models of finance and trading will be crucial in the near future. The challenge here is to understand how crypto-economics will affect the established financial models and how can the digital economy be leveraged in tandem with the existing economy.
In an era where we’re generating a ton of data, data security is increasingly important. It is imperative for organizations to keep updating existing cybersecurity solutions and enforce every possible applicable security layer to ensure that data is breach-proof. The rise of artificial intelligence has the potential to equip an organization to mitigate an entire world of cyber threats.
AI powered cybersecurity has the potential to analyze user behavior, deduce patterns, and identify the whole range of abnormalities/irregularities in the network. AI powered cybersecurity has the ability to minimize the routine security responsibilities by identifying recurring incidents and remediating them.
As AI continually matures and eases into the cybersecurity space, and here are some ways AI can help organizations boosts cybersecurity:
Therefore, AI will be a game-changer that will transform our approach towards cybersecurity. With the ability to instantly spot malware in a network, guide incident response and detect possible intrusions before they’ve even taken place, artificial intelligence will empower organizations in their quest to keep their data secure.
The area of greatest optimism about Cognitive Computing, AI and ML lies in its potential to improve people’s lives, and there are a lot of practical applications with which AI could materialize a drastic improvement in the lives of its citizens.
The adoption of AI among government organizations so far has been uneven at best, and lags behind the private sector. But with that being said, the potential applications of AI in government are massive, and there is space for all aspects and departments of government to reap the benefits of artificial intelligence.
Here are a few ways governments around the world can use artificial intelligence and cognitive computing for effective administration:
Almost all governments around the world are understaffed, underskilled and face huge administrative backlogs. Resources are scarce and consequently delivery of services may be poor and slow in nature.
Artificial Intelligence can potentially alter the situation drastically, and take up all of the time and labor-intensive jobs to make space for increased efficiency at reduced costs resulting in the more responsive and effective policy implementation by the government in order to make the citizens’ lives better.
Effective implementation of artificial intelligence continues to find new ways to manage, execute and learn from laborious tasks across industries for increased speed and accuracy.
The field of law, though traditionally slower adopters of technology, shows a lot of potential for AI to work its magic and bring concrete business value to the table. A concrete and well laid out AI strategy can augment a lawyers’ ability by doing the front-line repetitive and mundane work, drive better results for their clients and become better lawyers in the process.
Here are some of things artificial intelligence can help law professionals with, so that they can make time for better and more productive stuff:
Artificial intelligence, mainly machine learning, has enormous value for the noble field of law, practitioners can automate and manage their data and processes, protect their confidential data and empower better decision making to drive better outcomes both for themselves and their clients.
Since very early days, game developers have aspired to programme software that both pretends like it is human and create virtual environments that are consistent with the experiences of a real person.
The advent of artificial intelligence in this regard has been a boost in the arm for the game developers and the larger video game community in general. Majority of the video games released in the last couple of years have highly sophisticated AI for all non-player controlled personas, characters or functions. Bots have increasingly become smarter in an effort to ensure quality engagement over prolonged periods of time and maximize player participation.
There are varied ways in which AI is being used to develop video games these days, and here a few of them:
As developers have increasingly started making AI based player profiles for better personalization and give a characteristic vibe to characters, video games have become more entertaining and the possibilities of advancement of these virtual environments by means of AI is endless.
Giant strides in the advancement of artificial intelligence, machine learning, deep learning, robotics and computer vision among other things have massively aided the development of new military capabilities to disrupt
military strategies almost all around the world. Artificial Intelligence has made its presence felt across the entire spectrum of military requirements - intelligence, surveillance, to even nuclear capabilities in some cases.
The ways AI can help militaries around the world are endless, but here are a few ways that may be used by the militaries in an ethical manner:
Apart from the above applications, AI could prove to be a master-stroke in maintaining world peace by keeping fast-paced or irregular advances in military approaches around the world in check and prevent a potential world-wide arms race or a cold-war like situation again.
With the ability to not just carry out, but also learn from the traditional human functions, artificial intelligence is playing an increasingly important role in the hospitality industry.
The main objective of hospitality leaders and providers, and service partners is to surpass customer expectations and offer intensely personalized assistance.
Artificial Intelligence can thus be a master stroke for the hospitality industry. With the ability to streamline processes, provide actionable insights, and personalize and optimize experiences, it has the potential to power in a new wave of responsive and guest-centric hospitality.
Amongst a sea of possible applications, here are a few which can radically affect business outcomes:
AI has the power to transform every facet of the hospitality business and institutions to deliver better customer experiences and in return, better business outcomes.
In recent years, perhaps no sector other than marketing has been so radically transformed with the advent of AI. It has become an indispensable part of the marketing industry, helping organizations position their products, collect consumer data and improve future products in the process. Every company out there is fighting a cut-throat battle to gain market share, and companies are spending around 20%(sometimes even as high as 50%) of their revenues on artificial intelligence enabled marketing efforts in order to understand their customers better and meet their end-user demands.
Here are a few ways AI can be incorporated in marketing:
With the rise of AI and data-driven marketing, an increasingly effective practice adopted by organizations across the board is Revenue Operations (RevOps). This practice aims to break down the silos between sales, marketing and customer success departments in an organization to give a holistic view of the revenue streams. Thus, artificial intelligence is transforming the way we look at not just marketing, but also sales and customer success in an organization.
Artificial intelligence is drastically changing the nature of creative processes across the board, and algorithms are increasingly playing an active role in creative endeavours such as music, architecture, fine arts and content creation. From being just a tool in order to conceive art, computers are beginning to step in as a creative entity in their own right.
Primarily conceived as a human domain, it turns out machines can do a lot more in the creative world than previously expected.
Here are just a few genres deep learning can produce art in, provided the system is fed with a training data-set:
The rise of artificial intelligence has not only transformed our ability to create art, but also put forward thought-provoking questions about our relationship with technology and the depth of human creativity in an effort to learn about ourselves.
Oil is the most important commodity globally, and drives most of our energy needs. But the global oil reserves have begun depleting, and the time is just right for Energy companies to turn to modern technologies and invest in artificial intelligence and other data technologies in order to maximize efficiency and secure future competitiveness in an ever-changing environment.
All problems are not created equal, and this represents a unique proposition for the oil and gas industry. As the industry becomes increasingly competitive and unpredictable, it’s high time they start looking at AI at a basic level at the least in order to streamline production, reduce costs, improve safety and empower decision making among other things.
With the use of data science, machine learning and artificial intelligence in general, here are just a few avenues Oil and Gas companies could use help in:
The combination of technologies such as AI, ML, NLP big data and IoT will significantly help the industry in the years ahead. And though AI has indeed been implemented in a broad array of some verticals, the need today is to take a step further and incorporate these technologies in even the specialized sectors of the industry to realize the overall potential impact for the oil and gas sector. .
Considering the utility of artificial intelligence in not only identifying the most important correlations and causations in data, but also learning from past behaviour and running predictive analytics in an attempt to judge future outcomes, the VC/Private Market industry is one which is primed for reaping the success from using cognitive computing and artificial intelligence.
With well-established metrics and droves of data points already being available to venture capitalists in order to establish correlations and patterns to assess start-up potential, AI and ML are the ultimate means to a powerful tool to filter through all the noise and assist VC’s in finding the best contenders for investment.
In order to make better, faster and more informed decisions, here are a few ways data, algorithms and artificial intelligence can help venture capitalists:
By harnessing the power of AI and ML, VCs will have access to better conclusions and insights from data in every stage of their investment journey, which is the juice for informed decision-making.
In the past few years, AI research and development has seen an enormous rise. Companies are posturing themselves in a manner that can give them an edge in the fourth industrial revolution, and countries are developing national strategies to explore how AI and cognitive computing can be used for the greater interest of society at large.
But ever since the inception/conception of artificial intelligence, there has been a raging debate around the design, construct, use and treatment of machine intelligence by humans.
Ever since the argument that AI could be detrimental to humans in ways such as physical, mental, social, emotional and financial to name a few, has been floated, there has been a need for systematic policies and mechanisms to understand how algorithms and data can be safeguarded from malicious behavior.
Science fiction is a precursor to science fact. 1950’s saw the works of Issac Asimov get published. His book, “I, Robot” laid the foundation for ethical artificial intelligence with his three laws of intelligent machine behavior, which were:
As artificial intelligence progressed past the AI winter and into the 21st century, exponential strides in the processing power and storage ability resulted in active efforts to commercialize the use of AI on a wide-spread scale. This not only called for better compliance with existing concepts of ethics, but an altogether better definition and framework for ethics in intelligent machines.
While Asimov’s laws spoke to the machines, Microsoft and Satya Nadella’s laws for ethical artificial intelligence talks to the people building it. In order for machines to assist humanity to address society’s scourges, here are the 10 laws of AI, for the ones building AI, in the words of Microsoft CEO Satya Nadella himself:
Google CEO Sundar Pichai has also been a strong advocate for the need of regulation in AI, citing the dangers of AI and prescriptive data security and privacy protection. This effort has led to 7 laws of development of artificial intelligence at Google:
Google also outlines the technologies it will NOT pursue:
Policy makers on the other hand seem to have a mixed reaction to the rise of AI all over the globe. Here’s a brief overview of how the global legal and regulatory trends look like:
While even after such extensive definitions there still might be ambiguities about the intricacies of the ethics of AI, there is one thing which is extremely clear - artificial intelligence is supposed to help us, not hurt us, lile Asimov said.
Artificial General Intelligence can be broadly defined as the ability to understand, learn or implement anything that a human can.
Although most artificial intelligence applications today can indeed perform tasks with more efficacy than an average human, the machines today are far from “intelligence” in a sense that they can only do a single function extremely well, and completely break down when doing something else.
Machines today need troves of data to learn from, unlike humans who may learn with significantly lesser learning experiences. Also, the concept of AGI dictates that a machine should be able to apply the knowledge gained from one domain to another, just like humans would do.
This would require the learning process of machines to be extremely similar to that of humans, so that they may learn in less time and gain competency in multiple areas. But the problem is that we ourselves don’t quite completely understand the functioning of a human brain, much less model and replicate it.
Hence, in theory we may indeed conceive the concept of artificial general intelligence, but in reality we are many leaps and bounds away from achieving artificial general intelligence.
“Success in creating effective AI could be the biggest event in the history of our civilisation. Or the worst. So we cannot know if we will be infinitely helped by AI or ignored by it and sidelined, or conceivably destroyed by it” - Stephen Hawking
While it is true that artificial intelligence is indeed transforming entire industries at end and has the potential to be an unparalleled force for good, but as the technology evolves, there’s bound to be negative aspects/consequences as well. And in order to be better equipped to mitigate and manage potential dangers of artificial intelligence, here are some key negative aspects of AI:
By definition, as is evident from the term itself, SuperIntelligence refers to the ability of artificial intelligence to exceed human intelligence in virtually every field, including creativity, scientific temper, general wisdom, and social skills.
AI today, albeit making great strides, requires an objective fixed by humans to pursue. The fact is, Superintelligence is just a pipedream at this stage, and it almost sounds like a utopian concept. Nevertheless, this is the kind of AI that is generally perceived as a threat, and which has led the likes of Elon Musk to suggest an outcome as far out as the extinction of mankind.