Neurotechnology is growing at a fast pace. But how much do we know about tech, and what impact will it have on our lives? Is there too much hype surrounding a technology that has yet to affect society significantly? We’re not the only ones voicing questions.

Furthermore — future technologies will bring out new challenges and opportunities, including privacy. Even people like dr. David Eagleman, a neuroscientist, fears that technology will soon allow us to read each other’s minds. According to him, all sensations can be interpreted because of the brain’s ability to reorganize itself or neuroplasticity.

On the bright side, he also says we can use this to create new ways of seeing the world. In the future, as we learn more about the brain and begin using new technology to enhance it, we may discover unique and fascinating abilities.

Revolutionizing the way of interacting

What if you could type out a message to your partner or send an email to your boss just by thinking?

That’s the promise of a form of brain-computer interface technology that is undergoing trials in Melbourne and New York. At Tom Oxley’s company, Synchron, the technology uses brain implants to link directly with computers and bypass the body’s regular communication routes — including speech, typing and even eye movements.

The technology promises to revolutionize the way people interact with computers, allowing them to type words and send emails simply by using their thoughts. But it also raises serious questions about privacy and how we’ll defend our thoughts from prying machines.

Link to remarkable achievements

If you could read my mind, what would you find? Would it be a stream of consciousness, random images, thoughts and memories that come and go like the weather? Or would it be as clear as a summer’s day?

I have no idea. But a team of neuroscientists may have just taken us one step closer to finding out.

According to Guardian, in July this year, it became the first company in the world, ahead of competitors like Elon Musk’s Neuralink, to gain approval from the US Food and Drug Administration (FDA) to conduct clinical trials of brain-computer interfaces (BCIs) in humans in the US.

The technology has been tested on more than 50 people so far. Its inventors claim to have improved their original prototype “remarkably” — with a week-long training period needed for patients to control a robotic arm using their brain signals alone.

Is less really more?

In this high-tech future, there’s no need to fire up your PC or fiddle with an iPhone to check your emails. Instead, you can send a mental command to the computer to bring up your inbox, pick out the messages you want, and send them off to their recipients. Much of this is already possible. In recent years, technical advances have made it possible for people with severe physical disabilities to control computers using only their minds.

Such breakthroughs are likely to become commonplace over the next decade as more people undergo tests and procedures that involve placing electrodes inside their brains. This will allow them to control computers and record and share their experiences of particular moments — everything, such as what they ate for breakfast yesterday morning (and whether they enjoyed it).

Benefits and challenges of ethical questions around neurotechnology

The consequences of sharing neuro data preoccupy many ethicists. From Oxford’s Uehiro Centre for Practical Ethics, Stephen Rainey explained that brains are central to everything we do, think, and say. On the other hand, when we have this rise of tech, there’s a rise in corporate power. This is the most crucial question of our generation — who will control the data in your head?

Tech companies are attracted by the potential to gather more information about people and make money from it. But they are also keen to stress that they do not want to follow in the footsteps of George Orwell, in which a totalitarian state uses technology to spy on its citizens and manipulate their thoughts. The outcome is dystopian, indeed…

Neuro-rights: Raising awareness among countries

So, in a world where companies can read our minds, what could go wrong? That’s the question being asked by lawmakers in the South American country of Chile.

Chile is not taking any chances on the potential risks of neurotechnology. In a world-first, in September 2021, Chilean lawmakers approved a constitutional amendment to enshrine mental integrity as a right of all citizens.

European legislators are also debating the issue of neuro-rights. The right to mental health was protected by French bioethics legislation in September this year. In the meantime, the Italian Data Protection Authority determines whether the country’s privacy laws protect mental privacy; Spain has begun work on its digital rights bill.

A non-binding recommendation on responsible innovation in neurotechnology was issued by the Organization for Economic Cooperation and Development (OECD) in April of this year. Australia has signed the recommendation.

Subtle technology — a potential to change everything

We’ve been conditioned to think there are things we can’t know and that these things are private. But what if we didn’t have to ask? What if our thoughts were being read — not only the individual ones we choose to express but also the ones we keep hidden even from ourselves?

It seems like a stunningly futuristic possibility, but it’s fast becoming a reality. It’s becoming increasingly clear that the brain holds an immense amount of control over our behaviour, and technology is on the cusp of understanding how to manipulate it. But what happens then? As this technology develops, we’ll soon be faced with a big philosophical question — where do we draw the line? For some, it will be easy to dismiss this as a needless exercise in ethics for the future.

Also, the best books on science and technology attempt to shift our perspective.

These books aim to force us out of our analytical, reductionist boxes and into a more holistic way of seeing.

Some of the very best achieve this by crossing disciplinary boundaries, while others are so packed with hard facts that they manage to make you think even if you disagree with what’s being said.

Thirty years ago, the famed artificial intelligence (AI) scientist Herbert A. Simon predicted that humans would have thinking machines in the twenty-first century. But we don’t. The sloppy but clever term “artificial intelligence” has fueled misdirection and misunderstanding. Simon deserves credit for exposing all this and predicting our failure to date. Now, in his last book, The Sciences of the Artificial, Third Edition, he asks why our thinking machines never arrived and considers their prospects.

On the other hand, we see the world and think about things that are too complex to be replicated by a thinking machine.

Rather than abandoning his faith in the power of AI and its possibilities, he believes that we have not kept up with our early expectations regarding its development.

The Age of Artificial Intelligence and Our Human Future

In an era when users have access to a plethora of personal assistants — from Apple’s Siri to Google Now (the older version), Alexa and Cortana, among others — all of which are dedicated to understanding and responding to natural language queries, there is growing concern that AI technologies will increasingly infiltrate every aspect of our lives.

Such AI concerns appear to fly in the face of reality because we already rely heavily on various AI technologies, such as Google search, Facebook’s news feed algorithms, Amazon’s product recommending engine, and Netflix movie recommendations, to perform complex analyses on massive amounts of data that inform our daily decisions.

Goldstein and Gigerenzer may have indirectly explained how artificial intelligence behaves and how it affects their applications.

Their research equips the reader to think critically about opportunities and risks of deploying technologies in contexts as diverse as governance, education, health care, business and journalism, religion and commerce.

According to the authors, “heuristics are simple, efficient, adaptive thinking strategies that complement — and sometimes supplant — more analytic and deliberative forms of inference.”

They refer to the recognition heuristic as one example. It is a type of recognition bias that takes advantage of our ability to recognize a common pattern. The authors point out that this heuristic is efficient mainly because it exploits an internal facility for storing and recognizing overlearned visual patterns.

The Age of AI: Our Future in a World of Artificial Intelligence

We can also learn more about this in the book titled — The Age of AI: Our Future in a World of Artificial Intelligence, by Henry A Kissinger, Eric Schmidt, and Daniel Huttenlocher, which offers itself as “a manifesto” for business leaders, policymakers and the public on “what to expect based on recent developments and where the opportunities lie.”

It is ambitious in scope both in terms of the subject: artificial intelligence is only one example of a technology that will upend society along with big data, energy and biotechnology. But also content-wise.

It offers background on why we should care about AI written engagingly, a high-level view of what AI can do and then specific chapters on why it will be important in healthcare & life sciences, transportation & car manufacturing, and many other fields.

The authors’ central thesis is that a global race is underway for AI leadership. They argue that the countries that develop novel AI-first will reap profound economic and military benefits with “potentially massive repercussions” for the countries left behind. There are as many as half a dozen “AI superpowers” determined to move to the head of this pack — China, France, Germany, Israel, Japan and the United States — but China is regarded as the most formidable competitor in pursuit of “the holy grail of AI.”

The authors have written a well-researched book that describes the current state of AI and the scientific discoveries that led to its recent explosion in performance. They lay out the technical challenges — both immediate and long term — to advanced AI forms. And they explore the policy issues raised by these breakthroughs, such as how an artificial intelligence system might be programmed to act in ways that would be illegal for humans or corporations.

The authors contend that there has been insufficient oversight and critical analysis of AI research by policymakers and academic leaders in computer science and other fields. Yet this book itself could spur a deeper examination of several important questions: In what ways might AI lead to better governance?

When Will Computers Pass the Turing Test?

Still, progress has been uneven, though. For all the hype, actual AI and machine learning applications lag those of mobile, cloud computing and big data — some say by decades. And the next wave of innovation with things such as quantum computing and neuromorphic chips may take even longer to gain traction.

But there are limitations here, too.

Many of today’s leading AI applications are narrowly focused, relatively easy to build into the core, and close to the experience layer where we all live, work, and play.

Their narrow reach is a product of their design — pattern recognition or machine learning — as much as the technology itself.

Experts are already predicting when computers will pass the Turing Test — when they can fool humans into thinking they are interacting with another human. In one breath, AI will make us unimaginably wealthy and free; in the next, AI will provide us with some or all of our emotional, social, and even physical needs. However, we’re unlikely to reach that world of AI fable any time soon.

The Future of Humanity: You, Me and AI

As AI continues to evolve — some would say it is unapologetically evolving — the fear of a singularity remains, with many companies and individuals positioning themselves to benefit from that change. With new perspectives emerging every day, we have seen one of the most eloquent and compelling reasons, yet that AI is not something to be feared but welcomed by those who desire a better future for all.

As we move ever closer to the emergence of brilliant machines, the real tragedy would be if humans remain separate and divided, unable to pull together to achieve more meaningful goals. By encouraging collaboration among humans and AI, we stand a greater chance of not just surviving but thriving as unified entities.

While it is clear that AI systems offer opportunities across various areas of life, many of these systems have a positive impact on the world in general.

When we fail to recognize that they pose significant risks, we all lose out on the fundamentals that are key to the whole concept of development itself.

Even though there is mainly a more philosophical rather than practical approach to this topic, it’s difficult to avoid the growing presence of AI in our lives. Particularly when we hear more and more about it and see it increasingly embedded in our social media, jobs, and homes.

There is no question that AI has the potential to improve our lives in countless ways.

However, ethical considerations cannot be ignored, and we cannot afford to get caught up in a false sense of excitement or awe. As with any powerful tool or technology, a risk/benefit analysis and thoughtful discussion about the ethics of AI need to occur.

Does AI Pose A Moral Dilemma?

Until this point, there’s the awareness that AI can automate many tasks, increase productivity and make our lives easier while also enabling us to solve problems that are simply too complicated for humans to solve alone. However, AI is not without its challenges. As we see AI becoming increasingly pervasive in our society and affecting every aspect of our lives, it also creates a host of new ethical and moral questions that have never been faced before.

Some of the most pressing issues we currently face regarding AI include:

• The current lack of consumer protection surrounding AI algorithms
• The potential for AI to intensify existing inequalities
• The potential for AI to create new systemic issues at a rapid pace

Currently, there aren’t many comprehensive regulations addressing the ethical use of AI. This means there’s no real way for consumers to protect themselves from having their data misused by companies or being discriminated against due to the biases programmed into machine learning systems. It also means that companies have little incentive to use their customers’ data responsibly.

Moreover, we lack any legal framework to protect us from malicious or reckless uses of AI, such as autonomous weapons systems or algorithmic trading bots gone rogue.

Ethics of AI and the Inability To Think Beyond Today

The past few years have witnessed a proliferation of initiatives on ethics and AI.

Whether formal or informal, led by companies, governments, international and non-profit organizations, these initiatives have developed many principles and guidance to support the responsible use of AI systems and algorithmic technologies.

Despite these efforts, few have managed to make any real impact in modulating the effects of AI.

What are the reasons for this failure?

There are at least three crucial factors. First, although there is an unprecedented level of hype around AI-driven technologies, they remain growing.

The hype has created unrealistic expectations among people and organizations about the capabilities and applications of these technologies.

Secondly, many of these initiatives focus on symbolic guidance such as principles or best practices instead of concrete actions that can be easily implemented. Finally, these initiatives are fragmented across numerous organizations that do not coordinate or collaborate.

The hype around AI has created unrealistic expectations among people and organizations about the capabilities and applications of these technologies.

The ethics of AI in a connected world

This personal trade-in data has raised new concerns over the ethics of the invisible digital infrastructure that underpins many of society’s most essential systems, influencing the way we think, communicate and act. Yet when the imperative should be to address these imbalances, many businesses have been accused of turning a blind eye to them and failing to take responsibility for their role in creating an ethical AI framework.

The effective control of AI systems is especially dangerous since it is difficult to define.

The reach of AI systems into every aspect of daily life shifted more social and economic activities into the digital world. Leading technology companies now control many public services and digital infrastructures through procurement or outsourcing schemes.

The public is blissfully unaware that they’re giving up control over their own data to private companies who can be compelled by state actors to exploit it. And private companies are becoming increasingly dependent on public institutions for their basic functioning. At the same time, they also use state-of-the-art surveillance tools to track and monitor users to extract value from them.

When AI Challenges Face Principles in Practice

Today we’re at a critical moment in the use of AI. In some cases, this technology has been shown to have extraordinary potential to advance society. Still, it can also be used to undermine the core values at the heart of society.

Suppose significant advancements in AI are not accompanied by a greater understanding of these issues. In that case, it is our duty as professionals and academics to ensure that this becomes a top priority for our colleagues and government institutions.

We need to work together on this issue. As scientists, engineers, and designers, we have an essential role in educating people about AI’s risks and opportunities. It would be negligent of us not to address this issue head-on.

As I mentioned earlier, I believe the key is change — specifically organizational change within both industry and academia — but how exactly do we make this happen? And what should this change look like?

There are three main issues at play:

The first challenge is that many attempts to model and govern AI systems fall short of fully appreciating the nuances and complexities of these systems. This is particularly an issue when considering AI systems’ “ethics” dimensions.

The second major issue is that to date, all the talk about ethics is simply that: talk. While there has been much discussion on AI’s risks, challenges, and benefits, we have yet to see any real, impactful action. This is despite researchers already producing frameworks, concepts, and recommendations for governing AI systems.

The third major issue is that discussions on AI and ethics are still primarily confined to the ivory tower. For real progress to be made, it is critical for governments, businesses, civil society organizations, and citizens to get involved in this debate.

AI and Ethics: The Conversation We Must Have Now

The time to begin this conversation is now. This is not a conversation about whether AI should be built but how it should be designed and developed. The good news is that AI has the potential to deliver equally great benefits as it brings its dark sides to light.

The list of issues that need addressing is lengthy, and some are more complex than others. However, they all share a common root: they relate to the ethics of AI — what it means to “do no harm” and maximize societal well-being while minimizing harm. These ethical conundrums are not new; we have been wrestling with them for millennia.

But unlike with other transformative technologies such as nuclear power, we do not have the luxury of time for these discussions. We must start now because it may be too late to alter the course once deployed.

Education. Innovation. Solutions-based science

While it might be easy to feel helpless in the face of daunting, global problems like climate change or nuclear warfare, many individuals and grassroots networks have begun to integrate a new way to respond: proactive solutions-based science.

AI is often one of the most academically challenging research areas with which any scientist can engage, but it also has huge potential to benefit society and drive innovation. This is why I have made it my mission and passion to build this conversation. I want to help ensure that we consider all the ethical and social considerations within our existing networks of expertise before moving ahead, so that AI can indeed be a force for good in society.

Constructivism in Artificial Intelligence

Constructivism is a knowledge and learning theory that can be applied to artificial intelligence. It argues that learning, knowledge, and understanding are constructive processes that build on prior knowledge.

For example, rather than forming a single conception of the world, pieces of information are layered on top of our existing knowledge.

When it comes to constructivism in AI, there is the belief that learning or knowledge is created by constructing internal models of the world that are constantly adjusted to fit with new experiences.

Constructivism in AI affirms that machine intelligence is best realized by programming machine intelligence systems to behave like infants, with instinctive reflexes, and then gradually learning how to interact with their surroundings.

A constructivist epistemology suggests that processing mechanisms actively change the world rather than simply mirroring it in computational terms.

The world is much more than a collection of stimuli. The mind interprets new input and transforms it into meaningful and valuable representations by drawing on knowledge and information about the world.

AI that Learns From Interaction

Constructivist AI is an artificial intelligence approach that emphasizes learning and problem-solving abilities through interaction with the environment.

These two terms are used within the broad sense of “learning” as any knowledge acquisition and “problem-solving” as any task that can be broken down into sub-goals to be completed in turn.

It differs from non-constructivist approaches to AI, which focus on programming or encoding solutions to specific problems. The ability to acquire new skills by modifying existing ones is the primary advantage of constructivist approaches over non-constructivist approaches.

Constructivist AI systems can learn new skills by interacting with their surroundings, but they are still far from solving all problems that humans can. The main limitation of constructivist AI systems is their inability to deal with abstract concepts, which are only meaningful in the context of other concepts.

To put it briefly, Constructivism in AI believes that intelligence is a fundamental property of mental or neural structures. It is essentially the idea that what intelligence is (and thus what intelligence looks like) is dictated by the mind itself; intelligence occurs only when intelligence is present, rather than things, events or stimuli in the environment. Furthermore, while constructivists agree that settings do impact learning, they place a greater emphasis on internal learning mechanisms. Internalization is another term for this concept.

Knowledge as a Process for Constructing Data

Constructivism is a philosophical position that holds that knowledge of the world has no absolute foundation but rather is dependent on the conceptual framework we use to generate and interpret it. Building an artificial intelligence system based on experience rather than pure knowledge can be supported by the theory of constructivism. When it comes to designing real-world intelligent systems, this method is a good fit.

This method can be used to create learning systems based on an extended form of constructivist epistemology. This strategy entails:

(1) defining a reasonable path of experience,

(2) developing learning algorithms that can benefit from this experience

Although these steps appear similar to those involved in traditional AI techniques such as case-based reasoning, there is a significant difference. In these techniques, human experts program the conceptual framework used by the system, whereas in constructivist AI, it is originated exclusively from experience.

Advancements in Constructivist AI: Challenges and Opportunities

Constructivist AI is not purely philosophical but is also concerned with the practicalities of AI.

What distinguishes constructivist AI from other forms of AI is this: One way to answer this question is to investigate the relationship between formal symbolic reasoning and human learning. Logismos (reasoning) and mathesis (feeling) have been debated since the time of the ancient Greeks, who differentiated between the two.

Plato stated that only logismós was capable of understanding abstract concepts like justice or beauty, whereas formal mathematical reasoning was limited to concrete objects like lines and circles. Aristotle disagreed, claiming that reason could get both tangible things and abstract ideas. The debate raged into modern times, with many philosophers taking an intermediate position in which some ideas can only be grasped through formal reasoning and others through empirical observation.

Understanding Constructivism in AI

In AI, the constructivist approach has emerged as a practical approach for developing advanced knowledge-based agents that learn from scratch rather than prior knowledge.

The constructivist approach is desirable when developing autonomous intelligent systems that can operate in unfamiliar environments without prior knowledge because it provides simple procedures for learning from interaction with the environment.

Some believe the main challenge of Constructivism in AI is how to represent the knowledge being learned. In other words, there aren’t any overarching principles for representing knowledge; instead, each field must be investigated in detail.

The purpose of this article is to discuss some possibilities of constructivist representations that are known in various domains (such as physics, biology, and robotics) and may apply to other areas where inductive learning is used (e.g., medicine). The main point is that researchers in all of these domains have demonstrated that certain basic constructs (sometimes referred to as “invariants”) repeatedly appear in different problems and domains.

Constructivist representations containing these fundamental constructs can be built up into more complex representations to solve problems in new domains autonomously.

The Key to Understanding Consciousness

Constructivist AI is an attempt to apply the most important constructivist ideas in psychology and cognitive science to the development of AI.

It is a methodology for creating a model or a system capable of understanding and acting in the world, and it is based on the creation and application of models of that world.

The model construction process, which was once the foundation of all human problem-solving behaviour, is now mostly done unconsciously. The goal of constructivist AI is to make this process explicit and use it as the foundation for machine learning and skill acquisition.

The central concept of constructivist AI represents the holistic concept that is greater than the sum of its parts.

The things we learn or believe we learn about the world are more than just a collection of facts, and they are two-dimensional models with structure and content. These models represent how we see the world, understand its fundamental structure, reason about what we can do in it, classify things into kinds and types, attribute properties to things, plan actions, and so on.

Instead of the traditional approaches to AI, which are based on imitation of human behaviour, constructivism assumes that ‘intelligence’ is the ability to build mental models.

The role of AI researchers and engineers in the future is actually to provide an intelligent system with data and tools for constructing its own models rather than providing the models themselves. And this requires intuition, creativity and a lifelong learning process so that we can identify risks, mitigate them and propose proper solutions for thriving.

“The secret of getting ahead is getting started”
Mark Twain.

In today’s dynamic and highly competitive business environments, getting started means accelerating the adoption of cognitive computing technologies quickly and in a way that delivers business value.

Today’s leading organisations realise that cognitive computing solutions are the key to future business success. This is evidenced in the millions of dollars being spent by technology investors on AI startups.

The most successful companies today, in various industries, are leveraging AI in their organisations, including:

• The adoption of AI in business processes
• The deployment of AI applications
• The implementation of cognitive solutions

Cognitive computing solutions not only automate processes but also enhance employee performance and streamline operations. Cognitive computing solutions also enable business organisations to analyse and visualise data, predict outcomes, and scale operations.

However, organisations face three significant challenges when integrating cognitive computing solutions into their business operations:

• The knowledge gap — Employees need to acquire new research and technical skills in AI
• The skills gap — Employees need to obtain new skills and capacities in AI
• The adoption gap — Employees need to adopt new ways of thinking and solving problems

Moreover, businesses need to scale their AI initiatives while maintaining high quality rapidly.

Who is shaping the future of artificial intelligence?

The goal of AI is to understand things.

This is different from the goals of machine learning and computer vision. The purpose of AI is to generalise, to learn what things, in general, look like, not just how to process examples.

When an image is distorted, AI algorithms either fail to understand what’s happening or ignore it. But the goal of AI is to understand, and understanding means understanding not just one distorted image but many distorted images. AI algorithms that understand, therefore, can reason about distortions. Rigid algorithms, on the other hand, repeat the same behaviour every time.

Part of the problem is that AI algorithms are fragile.

They are good at processing examples and using those examples to predict what will happen in the future. But they can’t reason about abstractions. If you tell them to draw a line, they will mark a straight line, but if you ask them to draw a line that varies a bit, they will draw a straight line, but they won’t know why.

One of the reasons is that AI algorithms are — just algorithms, and they don’t understand what they’re looking at. At Google Brain, AI pioneer Andrew Ng understood this, and they tried to solve this problem by training their algorithms to understand things.

The trouble is that training is very hard. When you train a neural network, you give it a set of examples and tell it how to calculate the probability its answer is correct. That almost works if the examples are chosen carefully. But in practice, the examples are chosen less carefully. So the AI algorithm ends up learning probabilities instead of understanding.

The second problem is the computational cost of training. With neural networks, the computational cost grows exponentially with the number of training examples, making it very expensive to train AI algorithms on large datasets.

Following this, it’s easy to understand that the business value of AI won’t come from AI itself — at least not at first.

The businesses that first reap the most significant rewards from AI are the ones that can harness it by making better use of existing data. AI will let us do things we couldn’t do before, but it won’t let us ignore our already existing data.

For example, after being a co-founder of Google Brain in 2011, Andrew Ng also co-founded deeplearning.ai and Coursera. The approach was to make AI easier to use. For instance, deeplearning.ai provides AI tools that are simple enough that anybody could use them.

Andrew Ng has also put AI to work, solving the problem of making it accessible to more people. The algorithms that deeplearning.ai uses in teaching AI are freely available, and anyone with an Internet connection can download them and use them for training their own AI programs. Ng has estimated that deeplearning.ai’s training algorithm on convolutional neural networks has been downloaded 50 million times.

Manufacturing AI gap

However, it seems like there’s more work that needs to be done. Google Brain’s former founder has launched a new company, Landing AI. On LinkedIn, it appears that the company has more than 70 employees. Investors in Andrew Ng’s startup Landing AI have given him $57 million, making him one of the most prominent figures in artificial intelligence.

In today’s competitive manufacturing environment, enterprises look to adopt new technologies quickly to stay relevant and profitable. Industrial companies also face the same challenges as more traditional companies when it comes to adopting new technologies. Training, change management, and data preparation are just a few of companies’ hurdles when implementing new technologies.

With the help of Landing AI’s enablement and transformation programs, customers can reap the benefits of AI. The company’s core product is LandingLens — an AI platform for industrial customers to build, deploy and scale AI-powered visual inspection solutions.

According to AI Magazine, billions of dollars have been invested in AI startups over the past decade. Artificial Intelligence (AI) startups are predicted to receive $90 billion in funding in 2021, up from $60 billion in 2020.

The only thing driving AI adoption is the need to deliver a better customer experience. AI can help deliver better digital experiences. Whether it’s AI as an enabler or an AI developer, AI enables many digital experiences. Whatsmore, AI-enabled technology can improve the customer experience by 80%. The customer is willing to pay for that.

One good example of consumer willingness to pay for AI is a virtual assistant called Alexa from Amazon. It’s being used less by consumers than one might think. And it’s not yet clear whether Alexa can become as ubiquitous as, say, Google Now. But it is believed that Alexa is “some of the leading stuff” that Amazon’s done.

Even among consumers, however, AI’s value proposition is not obvious.

According to App Annie, since its launch in 2016, Apple’s Siri has been downloaded more than 300 million times. But usage statistics are hard to come by, and it’s difficult to tell if those are people who use Siri rather than those who downloaded the app.

AI’s value proposition is best understood in the context of how humans behave. Therefore, we should go inside and trust our intuition when shaping AI and deciding if it’s a helpful tool. Is it all about AI optimising for efficiency and trying to mimic human behaviour? Let’s see what our intuition says about that.

The value of AI is being realised but in small amounts. But, as more AI becomes part of people’s daily lives, we expect to see sizable financial benefits. According to a McKinsey study, AI is expected to create $13 trillion of realised value to the world’s economy by 2030. For consumers, AI improved ad personalisation and recommendations, for example, and scene recognition in cars. For businesses, AI is improving inventory management, fraud detection, and customer service.

We are witnessing a historical evolution of technology, data, and talent.

By harnessing the business value of AI, we can power the transformational changes and deliver value to our customers and partners. This funding round allows us to accelerate our mission to bring AI capabilities to the masses and accelerate our leadership in the fast-growing AI solutions market. The emergence of AI is forcing businesses to reimagine their operating models — and, as a result, is fueling a wave of innovation and disruption across many industries.

How data-centric is manufacturing becoming?

The ability to leverage data, analytics, cloud, and AI technologies is critical to surviving and thriving in a digital business landscape. However, the methods in which these technologies are being deployed can vary widely, resulting in roadblocks to achieving corporate goals.

As a leader, how can you overcome this?

First, you have to understand that modern manufacturing and business are digital businesses. Your company depends on customers, suppliers, and partners for goods and services no longer relevant. How many digital connections your company has and how quickly it can respond to changing conditions matters.

Second, you have to realise that your company’s digital readiness is a journey. While you won’t reach 100% digital readiness overnight, you can undoubtedly achieve meaningful progress.

Finally, you have to realise that it’s not about technology, and it’s more about people, processes, and company culture. By focusing on the right areas, you can achieve transformation.

Got some questions about this topic? Feel free to contact me!

In the meantime, can also connect with me on LinkedIn.

‘Till next reading ,)

The world of quantum computing is about to enter the next phase of research and development, which should provide present holders with exciting capital gains. Although most quantum computing companies are not pure-plays, investing in these stocks can result in capital gains by purchasing and holding stock while watching the industry grow. In the 2020s, we may witness a rapid maturation phase of early startups.

Can Quantum Computing Disrupt the Future of Technology?

Since its origin in the 1960s, quantum computing has been a pipe dream, despite more than five decades of research and experimentation. However, quantum computing is making its way off the drawing board and into the real world — though there are still many obstacles to overcome. And, while we’ve seen significant advancements in quantum computations, power output, and precision, we haven’t yet seen any “killer use cases” that would drive a surge in consumer demand for quantum computers.

However, given recent advances in quantum computing research, collaboration, and investment, many experts believe it is only a matter of time before quantum computing is used in mainstream technology.

Stop the Quantum Hype and Put it in Perspective

Right, I understand that the Crypto hype train is intimidating, and it’s challenging to decide what to trust and invest in. Many new words and buzzwords are being coined these days, and I’ll try to clarify some of them for you. Another popular question is which company is most likely to succeed in the global quantum computing market. It’s an open question, but I believe some of the more common answers leave out important considerations.

After a decade of innovations such as artificial intelligence, Big Data, machine learning, the Internet of Things, advanced robotics, gene editing, 3D printing, nanotechnology, and so on, quantum computing is the next big thing.

Quantum computers operate on data using the fundamental principles of quantum theory. As we can see in recent years, quantum computing progress has been revolutionary and rapid, and it has gradually approached the realisation of a large-scale quantum computer. Many scientists and engineers believe that quantum computers will benefit humanity in the near future. Quantum computing is one of the most promising fields that have the potential to transform our world and society.

Qatalyst Quantum Computer Wins BMW Challenge

Recently, Quantum Computing, Inc. $QUBT announced that its Qatalyst ready-to-run quantum software was chosen as one of three finalists for the second and final round of the BMW Group and Amazon Web Services (AWS) Quantum Computing Challenge for the Vehicle Sensor Placement use case.

In the quantum computing space, competitors include Microsoft, Google, Amazon Web Services, and Alibaba. It is critical to recognise that these companies are not competing with one another but rather share a common goal: to make quantum computing accessible to everyone. So, who exactly are they?

These five stocks are breaking new ground in the technology sector, and I believe they will do well for investors. $QUBIT, $IONQ, $HON, $D-Wave, and $NVIDIA were all undervalued, and now it’s time to reap the benefits as they become digital staples.

How Quantum Computing Will Accelerate AI

Quantum computing is the next generation of computers that will revolutionise the way we think about technology. Quantum computers were science fiction just ten years ago. Today, however, Google, IBM, the Chinese government, the University of Bristol in the United Kingdom, and many others are developing quantum computers.

China has announced the creation of the world’s first commercial application of a quantum communication satellite less than six months after announcing that they had harnessed a quantum computer. It is also clear that China regards this technology as a significant achievement and competitive advantage over the United States.

When Quantum Computing Comes True: AI on Steroids

When quantum computers are finally more available to the public, AI could run significantly faster due to quantum speed-up. How long will that happen? I have no idea, but it’s more likely 2030s than 2020s. Anyway, the point of Quantum computing is not to make parts for quantum computers but rather to improve artificial intelligence.

Hardly a day goes by without a publication or news site reporting on some advancement in quantum computing technology. The advertising and marketing teams behind these reports typically overhype the development and its effects, which has led to a lot of confusion and unfounded hype with headlines like: ‘The Dawn of the Quantum Age is Already Here and ‘Quantum Computing: Breaking the Boundaries of Human Achievement.

On the other hand, driven by the plummeting cost of silicon and the development of artificial intelligence, quantum computing is now finally becoming a reality for individual investors. The entire venture capital climate seems to be pricing some companies at lofty valuations based on some recent successes. But the fact is, many of these companies are operating in markets that are still “in development” or are trying to develop platforms for new markets.

New Quantum Computing Architecture Achieves Record-Breaking Results

IonQ published a paper in Nature demonstrating how it successfully corrected errors in an existing 2-qubit prototype quantum processor. This is significant because, in the absence of error correction, any computational error that occurs during a quantum computation will be amplified and cause the entire computation to fail.

Even though their results demonstrated that error correction on a trapped-ion quantum computer was possible, I do not believe they were the cause of the spike. More than anything, it appears that it was just some clever publicity leading up to the release of their results in Nature Journal.

Where Evolutionary Systems and Evolutionary Algorithms meet Quantum Computing

When it comes to general-purpose computer performance, humanity is a little on the ropes. Moore’s law has run its course, and specialised hardware will not change these fundamental limits because we have now reached the fundamental limits of physics. When quantum computers become available, you can forget about apps like Google Maps; even the most basic text editors will no longer function.

Data-driven, search-driven, travel-driven, and pharmaceutical-research-driven strategies will only get us so far in making these systems work for us. I believe that quantum computing will lead to a true renaissance in the use of AI.

Quantum Computing: Challenges and Opportunities

Google is currently at the forefront of quantum computing research and has made significant progress. They are pushing the boundaries of quantum physics with their D-Wave quantum computer. Their early experiments produced impressive results, but it’s difficult to tell where they’re going. Swimming in shark-infested waters is akin to swimming in a shark-infested pool. Google is fearless in its pursuit of quantum computing, developing new algorithms for their computers to execute. Understanding their progress can be difficult if you don’t know what they’re attempting to achieve.

In terms of relevance, I believe it will take several decades for quantum computing to become a mainstream technology. This will be true across a wide range of industries, from defence to financial services and beyond. By collaborating with various retailers, D-Wave is already playing a role in personalisation and dynamic pricing.

On the one hand, they are improving their software development kit (SDK) to build commercial, government/military, and academic applications. In contrast, unlike their main competitor, IBM’s Quantum Computing Department has published several papers on the use of quantum computers for encryption.

The Quest for Quantum Computing

How many qubits are required to solve current problems? What is the required error rate? When can we expect quantum computers to breach current cryptographic systems’ security? How much computational power will quantum computers have in the near future, and what applications might result?

Are we Standing on the Cusp of a Quantum Computing Revolution?

IonQ, a quantum computing firm, has successfully created the first “fault-tolerant logical qubit” — a qubit that is resilient to a failure in any one component — laying the groundwork for quantum computers that are both reliable and large enough for practical applications such as risk modelling or shipping route optimisation.

Amazon opened the AWS Center for Quantum Computing in October 2021. The scientific community has known that specific computational tasks can be solved more efficiently when qubits (quantum bits) are used to perform the calculations. That quantum computer can solve some problems that are currently beyond the reach of classical computers. However, there are still many unknowns.

How Quantum Leap in Computing will Change Our Lives

Quantum computing holds great promise for solving intractable problems, but its application is still limited due to its difficulty communicating with qubits. While scientists work to develop more efficient methods of communicating quantum data via photons or superconducting fault-tolerant systems, companies such as IonQ are capitalising on the speed and accuracy that qubits promise by developing dedicated hardware capable of gathering data on multiple ions at once.

IonQ is ushering in a quantum revolution that will fundamentally alter how we live and work. IonQ can enable new approaches to significant societal problems by developing scalable, commercial quantum computers. Quantum computing is on track to improve intelligence processing for homeland security, accelerate research into new medicines and materials, and determine the origins of stars and atoms.

It can also discover better solar energy sources as an alternative to fossil fuels, transform how businesses optimise supply chains and processes, create super-secure communications technology, create new algorithms for machine learning and data analytics, improve agricultural yields globally, and do a variety of other things.

The World is Changing Faster than Ever, and You Need to Join the Futurists Movement

IonQ gives users the option of using Azure, Google Cloud, or Amazon Web Services. Developers can access testing environments via IonQ’s API, allowing them to test their app in multiple programming languages. So, quantum on the cloud, where does this leave us? Much more research is needed, but it may be the best place to start if businesses wait for technology to develop.

Quantum computing is in an exciting phase right now, with technology progressing from an academic concept to a product that developers can use. The question that has been bothering me is how this will affect the future of computing. That’s a difficult question to answer, which is why I decided to write this blog post outlining my thoughts on the subject.

Got some questions about this topic? Feel free to contact me!

In the meantime, can also connect with me on LinkedIn.

‘Till next reading ,)

If there’s one keyword or a key phrase that marked the month behind us, it’s definitely data and analyzing data. I have mentioned insight and data analysis so many times lately that I have lost count. I will also explain why it is so important and use trending tools to make the most of your data.

Data Storytelling

As I was talking at the Clubhouse panel, Data Storytelling, moderated by Heba Abusedou, If you have missed it, you can read the notes transformed into blog form on this link.

I constantly thought about what’s behind data storytelling and how people interpret all those insights and analytics they face, such as infographics, reports, etc.

Speaking of reports, you can find many creative ways to export automated reports from social media, for example. Still, there is an obvious need to add a human aspect to that automatized process. That aspect is emotion.

Emotional Connection Matters

A consumer’s bond with a brand impacts preference and loyalty. The type of human interaction that creates long-term value is precisely what technology is designed to provide and is destined to provide: listening more, remembering better, and learning more deeply.

The emotional connection toolkit of technology is growing by leaps and bounds.

People will soon be permanently and easily connected to the network via their clothing, wearables, biometric data, and presence and preferences that will be recognized everywhere. Human-to-human dialogue-based experiences like Alexa and Viv will take the place of hunching over computers. ANI (Artificial Narrow Intelligence) will anticipate needs that have not yet been imagined.

To remain competitive, businesses would need to adopt a new approach that emphasizes both emotional and practical techniques. They’ll need to understand basic human needs that technology is destined to meet and design consumer interactions around them: to help them belong and solve their internal issues caused by external problems.

A(d)ctivism

What’s more important is understanding the term data visualization and how a simple chart or graph can have a global impact. And great definitions is by Andy Kirk, author of Data Visualization, a handbook for data-driven design. Andy defines data vis as the representation and presentation of data to facilitate understanding.

The reason is simple — there has to be both motion and emotion. Call to action and feeling that some ad, chart, or graph wakes up within us. Whether choosing effective chart types, color palette, or fonts, you know what’s, in the end, the most important? Context.

With added context, we can tell a more impactful story.

That’s why you need to analyze your data, strip it down to the bones and see its core.

It drives us to the point why I chose data visualization in terms of Digital Marketing and advertising. Because this industry is transcending to another level, it’s getting more and more activism, and it’s becoming humanized. Most importantly, it’s powered by data.

So, why are we still debating the value of analyzing data?

Empowered by those insights and analytics, we want our target audience to see the value in the product that serves a purpose and is consistent with their values at the same time.

We want to create a data product with a call to action. When we share visuals, people share their experiences with others, allowing them to draw their own conclusions.

That’s why we shouldn’t separate emotional context and human connection to data from the actual graph that comes as an output of interpreting some data. Yes, it has to be visually appealing but to awaken emotion in people looking at it simultaneously.

Talk Data to Me — What’s the Hype Around Analyzing It? was originally published in Dataology on Medium, where people are continuing the conversation by highlighting and responding to this story.

Have you ever wondered — Do we make decisions logically or intuitively?

When it comes to reasoning and feelings, I’ve always gone with my gut instinct instead of analyzing things more objectively and critically. But there’s one issue. It’s known as biases.

People are mostly biased because they don’t know when their heuristic intuition conflicts with the task’s rational considerations, which probably come into play later in the reasoning phase. Then, it drives us to the point where we make impulsive or maybe even irrational decisions. And we don’t know why.

“Black box” or explainable AI?

Considering that there is this term bias in human psychology, it has its equal in AI. The controversial questions raised about biases in AI are mostly related to racism and gender inequality. The switch that was flipped in my head to dig into general biases, specifically in AI, was triggered after watching Coded Bias (2020) on Netflix. This is a provocative film about how various forms of biometric surveillance, artificial intelligence, and data science technology can be biased in both implementation and use.

The information that currently drives the world, the beliefs and attitudes on which cultures, societies are presently based — we should not store them in artificial intelligence systems.

We do not need a recreated world in which we are now. Even less do we need a mathematical model or algorithm that will annul the years, decades, and centuries of the fight against racism, gender discrimination, and many other vital issues that are at stake.

Hence, it’s so important to become aware of the fact that the bias doesn’t come from the machine learning model.

The bias source is the data used to train the model, training dataset that we provide.

In some cases, the system performs admirably on certain types of data while failing miserably on others.

We are constantly in virtual environments, filled with various stimuli. Not considering just advertising stimuli, all of them are oversaturated in space and time. Although it is no longer an environment, it’s rather an ecosystem, taking into account global accessibility. In such conditions, the will is invisibly broken, layer by layer, until a state of hypersensitivity occurs. That’s the perfect atmosphere for having bias affect both our and systems’ now impulsive decision-making process. The most astonishing thing — we wouldn’t even notice.

Combating privacy attacks and security

As human beings or AI, we should not blame ourselves as a higher intelligence stored in the information we currently possess. We need to find a way to build mutual trust. Let’s see some examples of how we can rebuild trust.

Providing just one pattern occurring in the digital marketing industry, we can replicate it from the microsystem to the bigger picture. We wake up in the morning, and instead of having a moment for mindfulness and being on our own, we’ll reach out for the phone and, most probably, social networks. After just a few scrolls, the ad pops up. And there is another one. And another one.

That data that was just for a couple of seconds in front of your eyes is now imprinted into your mind. More importantly, if you engaged anyhow and did what that brand or company wanted you to, you’re now a part of their funnel.

You didn’t want to, but you are.

AdAge on Fire

It just happened that recommendation algorithms optimize recommendation decisions for particular data-driven marketing strategy goals: to raise brand awareness, drive more conversions, or maximize their profits.

However, one crucial thing to remember is that these decisions may have significant consequences: recommending everyday items repeatedly to increase interactions and clicks can help the company achieve its goals, but it can also hurt individuals or the community.

AI-driven Audience Optimization

On the one hand, when AI and machine learning systems are trained using biased data, and the bias is not recognized and addressed, there can be severe consequences. Audience optimization is one example of a manifest of issues facing the implementation, where it comes to algorithmic bias.

A typical example is providing a customer base to find a lookalike audience on Facebook without manually cleaning data from customers with lousy purchase behavior. Otherwise, companies or brands could miss out on a potentially profitable consumer niche, causing their market share to stagnate.

Once we start feeding algorithms with preprocessed, “clean” datasets, those exact niches and customers with the highest product engagement may skyrocket your data-driven strategy. Therefore, it is critical to be able to assess the situation and take appropriate action.

Speaking of customers’ more human side, we see this situation occurring in online advertising. Therefore, if we cannot escape it, we can fight for its ethical side.

The fact is that when a machine learning model starts operating, its initial results will be influenced by the data with which it was trained.

That’s why it is important to train those models with improved data, not data that proclaim racism or gender equality, for example. However, once the model is in place, the system can continue collecting data and learning independently.

Information is beautiful

Besides data preprocessing, further refining data is critical; it can be very costly without additional insight. As a result, it is essential to estimate how the model values specific features and feature combinations. You can improve the model by using surprise, surprise — human knowledge. Interpretation is and will be in our blood.

It’s essential to revive the fact that — artificial intelligence is artificial. Its value is determined by the contribution of human observation and critique. Bias is the most serious of these issues if AI is not handled correctly. The key for any marketing team to minimize data bias’s negative impact is to keep humans involved as much as possible. Algorithms can’t imagine a future that isn’t the same as the present.

Human supervision is needed as long as these intelligent systems don’t meet ethical terms and conditions. Refining the model or the data collection shouldn’t be based on the return on investment only.

Amplifying human decision-making

Everything can be optimized, but as long as humans preserve their true nature and present information as beautiful as it is in the eye of the beholder.

Data doesn’t need to serve the algorithm that initiates digital marketing success or success in any other area.

Data is designed to break down internal biases, designed to tell a new story, one that goes against what we already thought.

What if we can have both — fighting bias and building trust in AI.

Then and only then, this shift in decision-making progress will finally make sense for the better times on the humanity timeline that we are witnessing.

The value of data mining has increased, as has the rate at which it is being used in creating marketing decisions. Including that increment, the information held in agencies’ and companies’ databases becomes crucial for the business to make enhanced strategic decisions.

Tldr, Data Mining takes your unstructured data (all the gathered information in those .xlsx and .csv files) and formats it into a structured form or output that you want. In this context, it is crucial to provide this data for marketing experts to be more exact and realistic about making decisions about the future steps.

Therefore, we should seek to create an upgraded platform empowered by the latest technologies’ ethical use.

It can empower us to develop more advanced and socially responsible strategies. From the clients’ side, businesses must become aware that there is more to marketing besides creative and traditional approaches such as their websites, quality content, products, employees, and customer relationships to succeed in digital marketing activities.

Data Mining in Marketing

Yes, we should continue with the current approach. The only thing suggested is the upgraded version that responds better to both clients’ and customers’ expectations by using other tools and technologies.

Implementation of text mining in digital marketing can be seen in various areas, including personalized campaigns, product design, cross, and additional sales, and marketing campaigns themselves [1].

Depending on the business, optimizing the advertising strategy may mean different things. Regardless, any analysis’s end goal is to provide actionable answers to relevant questions that can drive business performance [2].

• Search Engine Optimization (SEO) — optimization of search results from major search engines

Text mining will help you identify your keywords. For example, starting with the main keyword and using semantic relationships, you can get a complete list of related words or phrases.

These new keywords can then be integrated into blog entries, meta tags, and permalinks.

Using the RAKE algorithm on the previous two sentences, we’ll see how to extract keywords from our text data works. RAKE is an acronym that stands for Rapid Automatic Keyword Extraction.

!pip install nltk
import nltk

nltk.download('punkt')
!pip install rake-nltk
import pandas as pd

from rake_nltk import Metric, Rake
text_array = ["Text mining will help you identify your keywords.",
              "For example, starting with the main keyword and using semantic relationships, you can get a complete list of related words or phrases.",
              "These new keywords can then be integrated into blog entries, meta tags, and permalinks."
             ]
rake = Rake()
rake.extract_keywords_from_sentences(text_array)
rake.get_ranked_phrases()

rake.get_ranked_phrases_with_scores()

rake.get_word_frequency_distribution()

rake.get_word_degrees()

rake = Rake(ranking_metric=Metric.WORD_FREQUENCY)

rake.extract_keywords_from_sentences(text_array)

rake.get_ranked_phrases_with_scores()

rake = Rake(ranking_metric=Metric.WORD_DEGREE)

rake.extract_keywords_from_sentences(text_array)

rake.get_ranked_phrases_with_scores()

That’s how the RAKE algorithm works on simple text. Imagine what it can do with your latest blog!

• Social Media Marketing (SMM), the strategy of interacting with users on social networks through social ads

Facebook users provide a massive amount of information to Facebook, creating a detailed profile of users’ personal information, habits, and interests. All you need to do is click on the extract button in Ads Manager and start exploring all the possibilities that mining data from text offers.

Advertisers who use Facebook as a platform for their ads can then tailor their advertising strategy accordingly, perhaps focusing on specific groups of users with similar characteristics for a particular product. To visualize one of many opportunities — in the following example, you can use standard metrics such as Click Through Rate (CTR), Cost Per Click (CPC), Impressions, and conversion to see the correlation between click data variables.

Conclusion and Future work

There is no need to speak about its further impact on sales, customer satisfaction, point of sale data analysis, supply and store placement optimizations, and profitability. Market segmentation, competitiveness analysis, customer valuation, and cross-selling analysis are conducted with data mining [4].

If businesses use data mining methods more, they’ll start developing personalized product and promotion activities for specific customer segments.

We may see it in certain areas such as pricing, customer preferences, and product positioning.

That’s why data mining may allow companies to understand hidden patterns in current data, hence planning and implementing new marketing campaigns smarter.

Are the creative marketing agencies and companies ready to start applying available tools like data mining in daily work?

Resources:

[1] Tekin, M. et al. Data Mining in Digital Marketing, Proceedings of the International Symposium for Production Research, 2019.

[2] J. R. Saura, Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics, Journal of Innovation & Knowledge, Volume 6, Issue 2, 2021.

[3] C. Edwards, Exploratory Data Analysis of Facebook Ad Data with Python, C EDWARDS, 2018.

[4] P. Rajkumar, 14 useful applications of data mining, Big Data Made Simple, 2017.

Using Data Mining in Digital Marketing: Implementation, methods, and performance metrics was originally published in Dataology on Medium, where people are continuing the conversation by highlighting and responding to this story.

The nature of various markets that are part of our lives for quite some time is shaped and regulated. For example, the food and beverage industry continues to be one of the most regulated industries to ensure consumer health and safety.

Social Media Compliance

Our digital capturing devices, such as cameras built into smartphones, definitely lead to the exponential growth of digital content shared through social media platforms and all the other digital channels. That leads to a lack of regulation of knowing — what “ingredients” are there in digital content?

It has never been easier to capture a photo or video and share it with your friends, connections, or followers — in one word, community. Not to mention that many small businesses have joined this digital game, along with more prominent companies that are on the market for quite some time, due to forced digitization over all areas.

It’s definitely the better and wiser way to reach your customers or deliver your services but, once gathered at one place — what’s the actual quality of media constantly being spread all around these social channels?

Labeling by humans-in-the-loop

What’s certain is that data engineers and data scientists are constantly working on analyzing all the content on social media to obtain real-time information that can be applied to a wide range of issues.

The only thing that lacks during that process is sentiment. That’s where data gathering and its annotation jumps in to save the day.

AI and, more specifically, data annotation have a close relationship with privacy.

It is visible, for example, in the labeling of opinions (NLP) from social media or the annotation of people on video frames. In those cases, the data can be directly linked to a specific person.

Computer vision advancements and mentioned natural language processing (NLP) have significantly advanced and done wonders in AI because of deep learning algorithms’ improvements.

As a result, many businesses have quickly implemented AI and use it in various applications. Even though many examples of implementing AI do not necessarily involve personal data, the process gets complicated once they do.

The dominant data type segment

Speaking of data, it is good to mention that the image/video segment accounts for 35% of global sales.

The image/video segment accounts for 35% of global sales. Source: Data Collection And Labeling Market Size, Share & Trends Analysis Report By Data Type

Therefore, it is attributed to the growing use of computer vision in various industries, including social media.

Hence, data collection and annotation, combined with artificial intelligence (AI) technology, have started creating significant growth opportunities in several verticals, including gaming, social networking, and e-commerce. In terms of user interaction, image processing technology has benefited Twitter and Facebook (Facial recognition, tag suggestions), two of the most popular social networking sites.

As for Instagram’s use of AI, it is visible in the Explore feature. The posts you see in your personal Explore feature are tailored to your interests based on your likes and accounts you follow.

Instagram created an AI-powered ranking system that “extracts 65 billion features and makes 90 million model predictions every second.” Source: FB AI Blog

Data annotation and data labeling tools

Numerous data processing technologies, such as multilingual speech transcription, data classification, and data annotation in the data collection & labeling industry, have gained popularity due to the rise of mobile devices and the introduction of cloud media services.

After using some data annotation technology, professional data annotators and labelers review the annotations. Many of those platforms also offer comprehensive machine learning services, such as data loading, preprocessing, cleaning, data analysis/visualization, deployment, development, and re-engineering. They also make team planning and management more effortless.

Data cleaning, on the other hand, remains a significant problem in data labeling. We need to raise the awareness that, even though it is the most time-consuming part of every data science analysis, preparing the data is the most crucial starting point. You cannot get the best possible results without preprocessing them the best way possible.

Conclusion

To sum it up:

1. Annotation can be applied to any type of data, including images, videos, audio, and text. Computer Vision projects work with photo and video data, allowing machines to see the world as humans do. Natural Language Processing (NLP) describes human speech to computers using audio and text data.
2. Data labeling tools and annotation service providers are critical components of any modern AI project. Annotating data is time-consuming, labor-intensive, and expensive, posing several challenges for machine learning. Despite this, using labeled data is the simplest way to train your model.
3. Human experts play an essential role in the data annotation process because they supervise an ML model’s training phase. Digital methods may be used instead of human experts. Nonetheless, the benefits of annotators (quality, versatility, and low cost) outweigh the drawbacks (risk of mistakes, limited volume, and time consumption).

Hence, you should be aware of how much training data you require to get the most out of your investment, both time and energy. If you have too little or too much of it, it will degrade your AI project. Remember that determining the amount of training data can be a time-consuming trial-and-error process.

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Fusing Social Media Cues: Data annotation and data labeling was originally published in DataDrivenInvestor on Medium, where people are continuing the conversation by highlighting and responding to this story.