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Last Updated on July 6, 2023
Artificial general intelligence (AGI) is a hypothetical software that programmers had envisioned many years ago. The idea is to create a machine with the same or higher level of intelligence as humans. That is, the system would be capable of handling various tasks and solving problems even in situations where humans could not.
AGI is supposed to have complete computative knowledge. Its behavior and performance would be indistinguishable from that of humans, but its capabilities would be beyond human abilities. However, the AGI is still fiction and scientists are working to bring it to life.
Artificial General Intelligence Systems – The Marriage of Understanding and Perception?An AGI system’s primary goal is to emulate human intelligence, a complex amalgamation of understanding, perception, and reasoning. At its core, understanding allows the system to comprehend information and its context, while perception enables it to interpret and respond to inputs effectively.
The essence of an AGI system lies in its ability to not just process data, but to assimilate the background knowledge, grasp nuances, and formulate responses that reflect comprehension. For instance, a robot powered by AGI would not just understand the command “pick up the bottle,” but also perceive its surroundings to locate the bottle and identify the best way to pick it up, much like a human would.
Neural Networks – The Building Blocks of AGI?Neural networks are a fundamental component of AGI systems. They mimic the interconnectivity and function of human neurons, enabling machines to process information in a non-linear and context-aware manner. Neural networks learn from the information they process, thereby acquiring a form of “common sense.”
This ability allows AGI systems to not only understand complex topics but also to apply this understanding in diverse contexts, thereby moving closer to the overarching goal of AGI – to mimic human intelligence.
Alan Turing and IBM’s Watson – Their Impact on AGIAlan Turing, often hailed as the father of modern computing and artificial intelligence, provided the initial theories that have shaped the development of AGI. His pioneering work, including the famed Turing Test, has been instrumental in defining the field of artificial intelligence.
On the other hand, IBM’s Watson demonstrated the practical application of these theories.
Watson showcased the potential of AI in understanding, processing, and responding to natural language in the context of a complex game scenario. It marked a significant milestone in the development of AGI systems, showing that machines could understand and respond intelligently to complex, unstructured data.
Both Turing’s theoretical contributions and Watson’s practical demonstration have significantly influenced the development and understanding of AGI.
Cognitive Computing Capabilities: Is AGI Mimicking the Human Mind?Cognitive computing is a critical aspect of AGI. It refers to a machine’s ability to simulate the human mind’s complex functions, like understanding, learning, and reasoning. This entails mimicking human cognitive abilities and motor skills, enabling machines to interact with the environment as a human would.
For instance, NLP (Natural Language Processing), a subset of AI developed by computer scientists and psychologists, allows machines to understand and respond to human language, significantly enhancing their interaction with human users. Innovations like these, driven by institutions like Microsoft Research, bring us a step closer to achieving human-level intelligence in machines.
Consciousness and Artificial General Intelligence: Does Strong AI Need Self-Awareness?Consciousness – the state of being aware of one’s surroundings, thoughts, and feelings – is a distinctly human trait. Translating this into AGI, often referred to as ‘strong AI,’ is a contentious and complex issue.
Some researchers believe that without consciousness, AGI remains fundamentally limited, unable to fully understand and interact with the world as humans do. However, developing a machine that possesses self-awareness and consciousness brings forth significant scientific, ethical, and philosophical dilemmas that are currently unresolved.
Empathy in AGI: Can Machines Truly Understand Us?Teaching machines to comprehend and exhibit empathy remains a significant hurdle for AI researchers in the development of AGI. Machines, regardless of their level of artificial intelligence, are fundamentally different from humans.
They lack the lived experiences and emotional range that shape human understanding and empathy. While current AI technology can simulate responses to emotional cues, such responses are based on pre-programmed algorithms, not genuine emotional understanding.
For AGI to be truly integrated into our daily lives, it must bridge this empathy gap, posing a complex challenge for AI researchers and psychologists alike.
The ‘Theory of Mind’ in AGI: How Crucial Is It?The ‘Theory of Mind’ refers to the understanding that others have beliefs, desires, and intentions different from one’s own. This concept is pivotal in developing AGI that can genuinely understand and interact with humans.
An AGI system with a theory of mind would be capable of understanding humans on a deeper level, leading to more meaningful and effective interactions. Such an AI system could adapt its responses based on its understanding of the individual user’s mental state, thereby displaying an unprecedented level of adaptability.
The Role of Supercomputers in AGI: Are They Fast Enough?Supercomputers, with their unparalleled computational power, are often seen as key enablers in the development of AGI.
The fastest supercomputers can process vast amounts of data at incredible speeds, thereby facilitating the complex computations required for AGI systems. However, the quest for AGI is not merely about processing power. It also involves developing algorithms that can accurately mimic human intelligence, an area where even the fastest supercomputers face significant challenges.
The Elon Musk View on AGI: A Pocket-sized Revolution?These devices would be capable of understanding and even emulating human behavior, providing personalized assistance across a wide range of tasks. Such a reality could transform the way we interact with technology, allowing AGI to revolutionize the human race as profoundly as the internet did.
However, the distribution of such powerful technology also necessitates extensive ethical guidelines to ensure its responsible use.
How is Artificial General Intelligence Different from Artificial Intelligence?Many of us are already acquainted with the different AI systems such as Siri, Chatbots, Alexa, and others. But how do these intelligent models differ from AGI?
The artificial intelligence programs already in use are considered narrow AIs compared to the AGI. While the intelligence of the AGI is like the human brain, the existing AI software uses machine learning and natural language processing, which cannot imitate humans fully.
In addition, artificial intelligence technologies are designed to perform specific operations and problems. In contrast, artificial general intelligence will be able to serve various purposes without human intervention.
Emulating Human ConsciousnessAGI’s objective extends beyond simply replicating human intelligence. It aims to emulate human consciousness aspects, such as understanding emotions, demonstrating empathy, and possibly possessing self-awareness.
Although this goal remains mostly in the realm of theory, it differentiates AGI from traditional AI, pushing the boundaries of what we perceive as possible within machine intelligence.
Scope and CapabilitiesThe key difference between AI and AGI lies in their scope and capabilities. Traditional AI, or ‘narrow AI,’ is designed for specific tasks, whether it’s recognizing speech with Siri or recommending movies with Netflix’s algorithm.
However, AGI, synonymous with ‘full artificial intelligence,’ aspires to emulate the cognitive capabilities of the human mind. This means an AGI system could perform any intellectual task a human can do, from writing a symphony to solving complex mathematical equations.
Understanding and AdaptabilityAI applications operate within a predefined set of parameters – they excel at the tasks they are designed for but fail when presented with unfamiliar scenarios. For instance, a chess-playing AI, despite its sophisticated algorithms, cannot assist in drafting an email.
AGI, however, is theorized to possess the ability to learn and understand concepts outside its initial programming. This adaptability, mirroring human learning processes, allows it to adjust to new tasks and environments.
Examples of Artificial General IntelligenceAlthough an AGI machine is not yet obtainable, some artificial intelligence software possesses some of its anticipated features. The following are some of those systems;
Self-driving cars
Expert Systems
ROSS Intelligence
AlphaGo
How do AGI systems integrate understanding and perception?AGI systems combine understanding and perception using complex algorithms and neural networks. They process information and understand context much like a human brain, enabling them to perceive and respond to inputs in a human-like manner.
GPT-4 – Is It the Next Big Leap in AGI?Built upon a sophisticated neural network, GPT-4 is capable of deep learning, enabling it to acquire knowledge and improve over time. While it’s not a fully realized AGI, GPT-4 represents a significant milestone towards achieving a system with human-like understanding and perception.
How does GPT-4 contribute to AGI development?GPT-4, with its enhanced deep learning capabilities, offers a significant step towards AGI. It has improved comprehension, an expanded knowledge base, and the ability to understand complex topics, all of which contribute to the development of AGI.
The Leap to Artificial Superintelligence: A Future Prospect?Artificial Superintelligence (ASI) is often viewed as the next frontier in the field of AI, projected as intelligence that surpasses human cognitive abilities in every aspect. Renowned figures like Stephen Hawking and Ray Kurzweil have expressed both excitement and caution about the prospect of ASI.
What is the Future of AGI?A common question that is typically raised is whether the AGI will continue to be a hypothesis or will be achievable in the near future.
Yet, its development timeline cannot be ascertained at the moment. Some experts believe that the existing AI programs are an incomplete form of the AGI. Others argue that some required components of the system have not been invented.
FAQs How has Alan Turing and IBM’s Watson influenced AGI?Alan Turing’s pioneering work laid the groundwork for modern computing and AI, while IBM’s Watson demonstrated the potential of AI in understanding and processing natural language. Both have significantly influenced the development and understanding of AGI.
ConclusionThe AGI is a conceptual software or machine with the complete ability of the human brain. It is a versatile, autonomous system that is capable of performing at the level of human intelligence, unlike the existing AI programs that can only complete specific tasks.
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Artificial Intelligence: Governance And Ethics
In a report that asks critical questions for our future, The Rockefeller Foundation’s AI+1: Shaping Our Integrated Future explores the nature of artificial intelligence, stressing the need for a regulatory framework to shape and monitor AI. The august power of AI must not be left to market forces, the report recommends, but must be a force that helps all of humankind.
To discuss the report’s themes, this webinar we discussed the following themes:
1. The report states: “As we reimagine a way forward, The Rockefeller Foundation is betting that AI will help rebalance and reset the future in a way that addresses current inequities. To realize that outcome, we must develop a regulatory framework to ensure its responsible use.”
“We need to reimagine an entire new rule-making system that guides AI towards society’s goals instead of our current de facto rule-making system that guides AI towards the market’s goals.”
But given how well-financed the market players are, is this really possible? Can the forces of regulation truly overwhelm market forces?
3. What are some efforts to start to build this regulatory framework? What players might take the lead?
4. Are there actions that certain key professionals can take? Say, data scientists, managers or AI developers?
5. What is your forecast for the years ahead, as we grapple with the increasing power of AI and the need to regulate it? How do you foresee this struggle evolving?
Gillian Hadfield, Director, Schwartz Reisman Institute for Technology and Society
Top Quotes:
Kahn: Artificial intelligence has been an interest of the Foundation for actually a while, and we were actually the funders of the 1956 conference at Dartmouth that coined the term “artificial intelligence.” And the whole premise was around, at that time, they wanted to do research into how can we actually replicate the human brain. It was a little bit more of an academic, mathematical approach. And artificial intelligence has its ebbs and flows, but now we’ve seen an explosion in its use, and it’s really gone beyond an interesting technology to something that is just permuting all aspects, and we’re seeing this in the COVID response right now, how artificial intelligence is both being used to accelerate drug discovery and vaccine development, but also highlighting some of the privacy issues as we think about contact tracing and how we can use it in that context.
Kahn: So for us at the Rockefeller Foundation, our mission’s been for 100 years, how do we promote the well-being of humanity throughout the world. And right now as we’re thinking about this COVID and the pandemic situation, thinking about the near-term responses, but also, how do we set the course for a recovery so it’s a more equitable recovery. And we just feel guiding the development of AI now is really important to setting the stage for where we’re gonna go in the future.
Maguire: Let me briefly read this [from the Rockefeller AI report], because I think this sums up the question as I see it, really, it puts it in a true nutshell. It’s, “As we reimagine a way forward, The Rockefeller Foundation is betting that AI will help rebalance and reset the future in a way that addresses current inequities. To realize that outcome, we must develop a regulatory framework to ensure its responsible use. We need to reimagine an entire new rule-making system that guides AI towards society’s goals instead of our current de facto rule-making system that guides AI towards the market’s goals.” And I think that is really the issue, but I think it’s a very difficult issue because there’s very large companies that have enormous budgets, and they are pouring vast budgets into the development of artificial intelligence, applications, platforms, widgets, etcetera. The idea that some regulatory body, perhaps a governmental body, could actually really play referee against such powerful forces seems a little questionable, and I’m doubtful of that.
Hadfield: First of all, really important to recognize, there’s no such thing as an unregulated market. Markets are constituted by laws. We think about regulation just more generally. Markets are constituted by that. So the power that our large tech companies have today is in part constituted by the way the state protects contract rights, intellectual property rights, employment relationships, and so on. And the tools in our toolkit are some of those basic rules and those basic things that are constituting the power of markets.
But the other reason I’m optimistic about the capacity for now regulation that comes in, to say, okay, you could do this with AI, but you can’t do that with AI. You can use facial recognition on a phone, but you can’t use it to check up on your competitors, or you can’t have police departments using it in discriminatory ways. That kind of regulation, it’s definitely challenging to develop that regulation today, but we faced that challenge at the last major revolution in the economy, the early 20th century. That’s when we invented the regulatory state to harness and rein in the power of huge corporations at the time. Anti-trust law comes out at that point. I’m pretty optimistic that we can develop those new regulatory tools. I think they’re gonna look different than what we have now, but I certainly think we can do it.
Kahn:
I find the use of the words optimistic and pessimistic kind of interesting, because I feel like there’s been over time a negative connotation associated with regulation, particularly when it comes to innovation. That regulation is kind a bad, it slows things down. And to build on Gillian’s point, I sometimes use this expression of, the reason we have brakes on cars is not to go slow but so that we can go fast. And when we think of lots of markets, look at the health market. It’s fairly heavily regulated. But you can’t even imagine a system by which you could develop drugs or provide healthcare to people when you’re thinking about their safety without a lot of that regulation. That’s not necessarily a bad thing, it’s kind of striking the right balance.
Top Quotes:
Hadfield: Well, automation definitely changes the way who’s doing what jobs. And again, we’ve been through significant rounds of automation. I’m getting into my historian mode here, but in the 19th century, 70% to 90% of the population is working in agriculture and, of course, that changes over time. I’m not sure that we wanna hold up necessarily also the types of lives that people live in sort of mass manufacturing environments and factories as the ideal of people’s lives. So as an economist I’d say, look, first of all, yes, we should expect to see continuing automation and as we know, automation kinda ups that value chain. My colleagues in law certainly are gonna see some of their work displaced by artificial intelligence as well as a factory worker.
But I think because that creates more value, what we should be seeing is then a change in the mix of what kinds of work people do, how people spend their time. Wouldn’t it be nice to actually have a world where we were producing more or the same amount or more output, but people have more time to spend with their families, more time for leisure activities, more time for the types of creative work that we see unleashed by the kinds of access we now have to social platforms. We can write. We can post videos. We can do artwork. We’ll definitely see a different world. I think the question is, how do we share the surplus and the benefits of these technologies in a way that is equitable and supportive of the flourishing of human lives?
Kahn: I think that the downside if AI isn’t properly regulated, particularly in a context where we’re undergoing a big transformation, will be like what we saw when manufacturing and technology entered manufacturing. Or if we think of dislocations from using coal to using clean energy. These are big transitions that happen in society. And unless we think about what outcomes do we want, and we don’t sort of combine market and government to help guide those transitions so we maintain good social outcomes, then that’ll be a big risk. To indulge your negative, but in… And there’s good reason to be negative, there’s good reason to be concerned. One big concern of mine if we don’t regulate AI is that the current inequities that we have in society will get frozen in, because AI will just replicate all the biases that we have and make them kind of permanent versus just cultural and social. So that’s a big problem. We’re already seeing the growing inequality that’s happening right now and I think AI could just exacerbate that.
“And then to your point, we could see massive job losses and replacements that happen with AI. And if we don’t do that thoughtfully, then all of a sudden you’re gonna have entire groups and large groups of people who find themselves very limited with opportunities. So we’re seeing all these anecdotal issues when it comes to education, you’ve heard about the story about people who are assigning grades and that didn’t work out. And justice when they were trying to use AI to sort of determine whether people are guilty or not. That’s not really working out. Health is a big concern area. So there’s plenty of downsides, for sure, but I wouldn’t wanna throw the baby out with the bath water in ignoring the upsides. And we have a fundamental belief that AI can be a force for removing inequities and guiding a more equitable recovery as we come out of 2023.
Top Quotes:
Hadfield: Thinking about it in the AI setting, is something I call regulatory market. So this is a… Can we create a layer of competitive regulators, private regulators, companies that are investing in regulatory methods and technology, but regulate those regulators by having government set the outcomes that they have to achieve. So if we did this in the context of self-driving cars, it’s a very simplistic version of regulation, but you have a politically determined what’s the acceptable accident rate on the highway. Okay, so now I may be a private regulator that says, “Well, you know what, I’ve got a set of rules I think I could implement, and the companies that would have to buy my… They have to buy regulatory services, I would create a regulatory machine.” Zia might say, “You know what, I’ve got a way to do some technology for that. I’ve got a machine learning safe model that will regulate the vehicles.
“And Zia and I both have to demonstrate to government we achieve the target outcome, but now we’re competing to maybe provide you if you’re the manufacturer of the self-driving vehicles, you’re choosing between the methods we are proposing for achieving those outcomes. We both have to achieve the same outcome, but we are investing in figuring out better, more effective, more adaptive, rapid ways of achieving that. So that’s the… I think there’s a way for us to use those tools to get to this more adaptive, agile form of governance.
Kahn: Well, I actually am a big fan of Gillian’s model here, and I think there’s a slight analogy, it’s a little different, but when you think about insurance, which was the government sets the standard, and everyone needs to have insurance, and then you can shop around in an insurance market, there’s some kind of loose analogy to that. So ultimately, government will have to set the rules for what are the social outcomes that we want, that is a political process, and right now we have private sector companies that are in essence setting those rules, and whereas they were comfortable with that rule-setting before, they’re growing increasingly uncomfortable, and we see something like Microsoft, which is now not selling facial recognition technology to police departments because they wanna force the government to come in and set the rules and the norms because it’s out of the scope of what they want to or are able to or should be focusing on.
Kahn: We believe that there’ll be a raised consciousness in the same way that you have doctors have a raised consciousness about what actions they have have impact on people and society. Same with the legal profession. I think there’ll be a professionalization of data science that raises the social consciousness, and that will be a force that we can harness. We’re already seeing it in a lot of the large companies, large tech companies who are responding to their employee concerns as much as others. So in terms of what specifically someone can do, I think there’s a lot of resources out there to just understand what are the frameworks of how to think about ethical development and to engage with their management, to engage with their companies around, how do they think about the unintended consequences of their work, how do they think about choices that they can make and just create some of that internal pressure within companies. Companies themselves had business reasons to be interested in this, civil society has reasons, government has reasons. And that’s something that we’re excited about is we’re seeing lots of people who are aligning to this notion of we need some form of governance. We just don’t know exactly what it is.
Top Quotes:
Hadfield: Well, so I think two possible paths. One is that we don’t address this problem, and what we see are just the exacerbation of the inequalities and power and so on that we’re seeing now. And we see the blunt force instrument of “We’re just gonna ban this stuff because we don’t trust it and we don’t like it.” So I think that’s one not very happy future, and that’s what I think happens if we don’t solve this problem.
Again, I’m back to the optimist, and I’m also, if you’re selling ideas, so I’m gonna be optimistic. I’m gonna believe I can sell these ideas because I am absolutely confident there are paths forward where we get to smart regulation that harnesses the power of AI and allows it to become part of our regulatory environment so we can continue to shape our future as humans. I think there’s some reason to think that we are on that path. In the last year, we’ve started to see the shift from the call for fairly abstract guidelines and ethical principles, all of which is very good, to a recognition we need to start building the concrete regulatory mechanism.
That’s what Zia and Rockefeller and Schwartz Reisman and also with the Center for Advanced Study in the Behavioral Sciences at Stanford, we’re starting on an initiative to say look, okay, let’s start thinking about how we actually build those regulatory. Don’t just call for governments to write new laws. Don’t just call for engineers to be more ethical. It’s a solid regulatory challenge. There are ways forward on that. So I’m gonna be optimistic and say that’s the path we’re on.
Kahn: So I believe what you’ll start to see are some countries or states who actually figure out how are we gonna make sure that AI is a good infrastructure that helps us serve our society on health and education, and also creates interesting market opportunities, and just in the same way that the states figured out how electricity could help in that way, and how the internet and broadband helped in that way. And so I think there’ll be these positive examples out there that will start to become more and more common and that people will seek to replicate more and more.
Kahn: We’re seeing some examples of that. This isn’t exactly the same, but when it comes to digital ID, the countries and the states that are able to create a real digital ID system are seeing so many benefits from that, that more and more people then look to them. So I think five years from now we’ll be hopefully past the state of just those initial little examples, and there’ll be more and more a common and systematic approach of how do we, whether it’s some of Gillian’s ideas like unlock these markets for regulations, but it’ll be more and more common. I think it’ll be more and more of an expectation.
Challenging Five Artificial Intelligence Misconceptions
Artificial Intelligence, or AI, has been a buzzword for quite a while now, with most people have heard about it, but do we know what exactly Artificial Intelligence is? A lot many people are afraid of the word AI and don’t know how to tackle this buzzword, a fear which is usually caused by the unknown.
The Growth of AIIn recent years, artificial intelligence (AI) has evolved into one of the hottest topics for debates and discussion. However, though AI is getting an increasing amount of attention as its applications and capabilities grow, there are many misconceptions that mirror around the potential what AI can do changing lives. The misconceptions which surround AI arise from the experienced fear together with either a total lack of information or the misinformation on the subject. This article will go through some of the main misconceptions that make people question the capabilities of AI, and would try to bring it down additionally to explain what AI means and how it might affect your life.
#1. AI Mirrors the Human BrainThis is the top misconception that surrounds AI, which in its current state, consists of a host of software tools designed to solve multiple problems. Though AI may seem smart, it is not yet similar of equivalent to human intelligence. There are some forms of machine learning (ML) a subcomponent of AI that may have been inspired by the human brain, however, are not equivalent to mirror the human brain. Image recognition technology is more accurate than most humans but cannot be deployed when it comes to solving an analytical math problem. AI many solve one task exceedingly well in the current world but when the conditions of the task change, it would fail.
#2. AI is DangerousMany uses are still in awe of AI thinking of it as complex; through machine learning models are not inherently dangerous. They possess the same level of danger as other technologies which are already present in our lives. Most AI-systems follow human instructions, like solving a specific problem or analysing historical data with an aim to identify the optimal strategy to engage target audiences.
#3. AI is Difficult to ComprehendThis misconception comes with the dangerous word associated with AI that is discussed in #2. The truth is tech buzzwords do tend to be a bit confusing with an air of mystery to the layperson. Similar to the concept of the cloud, another dangerous word, the same mysteriousness is true for artificial intelligence as well. The basics of AI are straightforward and artificial intelligence is just a mathematical algorithm that is altered over time. The AI algorithm is always improving based on changing datasets. Similar to the human brain, AI learns on historical data to improve predictions of the future. Though the human brain draws decisions from a subjective point of view, a machine bases its decisions on objective facts learned from previous numbers and analysis.
#4. AI and Free of Bias?The truth is no technology is free from bias. AI is no exception. AI technology works on human input which is not bias-free. This puts AI with an intrinsic bias in one way or the other. Currently, there is no way to take the bias away from AI completely, however, efforts are high to reduce it to a minimum. In addition to the technological solutions, such as diverse datasets, it is important to ensure team diversity while working with the AI algorithms and have team members review each other’s work for good. This simple process can significantly reduce the bias that happens with selection bias.
#5. AI Can only Replace Repetitive Jobs Which Don’t Need Advanced DegreesConcured: Transforming Content Strategy Through Artificial Intelligence
Artificial intelligence (AI) is revolutionizing industries, driving innovations and enabling organizations to understand their customers better than ever before. Natural language processing (NLP) and machine learning algorithms enable marketers to make sense of large volume of data and deliver better customer experience. Delivering relevant content at the right time can help marketers dramatically shorten the sale cycles and achieve significant results. A company which is pioneering in the field of AI-driven content marketing is CONCURED.
Trusted by the world’s leading brands, CONCURED is the world’s first AI-Powered Content Strategy Platform that guides the ultimate content strategy to help maximize engagement and ROI. CONCURED uses Natural Language Processing, machine learning and deep learning to provide a SaaS platform that enables content marketers to audit, research, plan, distribute and track the performance of their content like never before.
CONCURED was founded to help content creators make data-led rather than opinion-led decisions. A scary amount of time and money is wasted in the name of ‘content marketing’ without any real understanding of what content is actually working. Decisions are too often made without the data needed to make the best, most profitable decisions. The company has developed its AI capabilities in one of the world’s leading AI hubs, Montreal. With commercial offices in London and New York, and multi-language capabilities, CONCURED is well placed to disrupt the multi-billion content industry globally.
A Passionate Leader and the Experienced TeamMoreover, the management board comprises of Kate Burns, Former Google, Buzzfeed and AOL CEO for EMEA. CONCURED recently hired a leading figure in the content industry, Peter Loibl. Peter previously served as the Vice President of Sales of Content Marketing Institute.
Artificial Intelligence and Machine Learning Drives GrowthCONCURED is using NLP, machine learning and data science to effectively read content like a human at an unprecedented scale. This allows marketers to make better, more informed decisions by understanding its audience and deliver compelling customer experiences. The company essentially offers the ability to tap into the research of a million researchers every minute.
Awards and Success StoriesCONCURED has won a number of awards and accolades. One of the most notable being named one of the world’s top 100 most disruptive companies. The award was voted for by the likes of Google, Microsoft, Oracle and UBER. Industry experts have been encouraged by CONCURED including Content Marketing Institute’s Chief Strategy Officer, Robert Rose having said “CONCURED is one of the most interesting & coolest technologies I’ve seen in the content space in years”.
Customers have also seen a significant increase in performance with Head of Digital at Nationwide mentioning, “CONCURED data allows us to see exactly what topics we should focus on to engage our audience. This helps us to justify our decisions and gain buy-in from others across the business”.
Challenges Along the WayOne of our biggest challenges to date has been to educate marketers on the capabilities of AI and just how much it can help. The media often creates an atmosphere of fear with quotes like “AI is taking over the world” or “robots will take your jobs”. “The reality is AI can help marketers more than they know, and we need to educate our audience and highlight things they didn’t realize are possible so that they can get ahead of their competition and significantly increase performance,” said Tom.
Future PerspectivesBig data and AI have been around for years. A significant increase in computing power and the ease of cloud computing has enabled organizations to make sense of large volumes of data in a way that hasn’t been possible before. Analysing big data enables organizations to uncover previously hidden insights on consumer behaviors, choices, and also segments.
“I think that AI &big data is amazing for a more transparent, better educated and more informed decision-making. AI will continue to augment decision-making with unprecedented levels of insight that result in better results. Evidence = better insight to make more profitable decisions”.
CONCURED is growing rapidly and the company is very excited about the future. Technologically CONCURED is well ahead of the most players in its space and is dedicated to innovating constantly in order to help its customers make better, quicker, more informed and more profitable decisions.
Top Trends In Artificial Intelligence In 2023
According to Gartner’s hype cycle of emerging technologies, 2023; Deep Learning and Machine Learning have reached the peak of inflated expectations. Artificial General Intelligence (AGI) and Deep Reinforcement Learning are in the phase of innovation trigger.
We are in 2023. The sentiment over Artificial Intelligence (AI) is euphoric. Every technology firm is jumping on the AI first bandwagon. Companies like Google, Microsoft, Amazon, and Alibaba are pushing the frontiers. There are a plethora of smaller players that are doing cutting-edge work in a niche area. AI is permeating into everyday lives.
As an active practitioner in this field, my views on the top AI trends to look out for in 2023 are as follows:
Firstly, let’s get the context of AI correct.
AI encompasses the following:
– Machine Learning (subset of AI)
– Deep Learning (subset of Machine Learning)
Trend #1: Machine Learning to Automated Machine Learning
A typical machine learning process involves the following stages:
A data scientist spends a lot of time in understanding the data. A data scientist tries to fit multiple models. They try out multiple algorithms to find the best model fitment that provides the optimal result.
Automated machine learning attempts to automate the process of performing exploratory analysis. It tries to automate the process of finding hidden patterns. It automates the training of multiple algorithms. In short, automated machine learning saves a lot of data scientist time. Data scientist spends lesser time in spending on model building and more time on evaluation. Automated machine learning is also a blessing for non-data scientists. It helps them to build decent machine learning models without deep-diving into the mathematics of data science.
In 2023, I see that this trend will become mainstream. Google recently launched AutoML in their cloud computing platform. There are niche companies like Data Robot who specialise in this area and are becoming mainstream.
“Automated Machine Learning will mature in 2023.”
Trend #2: Increase in Cloud Adoption for Machine Learning
Machine learning is a lot about data. It is the process of storing data. It is a process of analyzing data, training models and evaluating them. It is a data and compute-intensive process. It is iterative with hits and misses.
Cloud computing provides an ideal platform where machine learning thrives. Cloud computing is not a new concept. Traditional cloud offerings were limited to Infrastructure as a Service (IaaS). Over the past few years, public cloud providers have started offering Machine Learning as a Service. All the big cloud providers have a competitive offering in Machine Learning as a Service.
I see this trend continuing to increase in 2023. The cost of computing and storage in the cloud is lower and on-demand. The costs are controllable. The cloud providers provide out-of-the-box solutions. Data scientist now can spin up analytical sandboxes in the cloud, perform the analysis, experiment with a model and shut it down. They can automate the process as well.Machine learning in the cloud makes the life of a data scientist easier.
“Cloud computing would continue to enable Machine Learning acceleration in 2023.”
Trend #3: Deep Learning Becomes Mainstream
Deep learning is a subset of machine learning that utilizes neural network-based algorithms for machine learning tasks. Deep learning methods have proven to be very useful in the field of computer vision, natural language processing, and speech recognition.
Deep learning has been around for some time now. However, deep learning was in relative obscurity all these years. This obscurity was because of the following two reasons:
The sheer amount of data required to train deep neural networks.
The sheer computing power required to train deep neural networks.
These reasons cease to exist now. There is data now. There is abundant computing process available. The research in deep learning has never been so ebullient as compared to the past. Increasingly, deep learning is powering the fruition of complex use cases. Deep learning’s application ranges from workplace safety to smart cities to image recognition and online-offline shopping.
This trend will continue in 2023.
“Deep Learning will continue to be rapidly adopted by enterprises in 2023.”
Trend #4: AI Regulation Discussion Gains Traction
In 2023, data science community avidly followed the debate between Elon Musk and Mark Zuckerberg. The topic of the debate: Should we fear the rise of AI? Elon Musk had a pessimistic view on the topic. His views: the rise of AI has imminent dangers for humanity. On the other hand, Mark Zuckerberg, had a much more optimistic outlook on the topic. His views: AI would benefit humans.
This debate between these tech tycoons, has everyone thinking about AI and its regulation. In Jan 2023, Microsoft chimed in saying that AI needs to be regulated before it’s too late. There is no easy answer to this question. AI is still an evolving field. Excessive regulations have always stifled innovation. Maintaining a delicate balance is crucial. The regulation of AI is an uncharted territory with technical, legal and even ethical undertones. This is a healthy discussion point.
“Should AI be regulated? This will be a key discussion point in 2023.
About the Author
Pradeep Menon is a seasoned Data Science professional. He has more than 15 years of experience in the field of Data Analytics. He is a hands-on technical expert with a proven track record of helping organizations to transform.
Pradeep can balance business and technical aspects of engagement and cross-pollinate complex concepts across many industries and scenarios. He is a distinguished speaker and blogger and has given various talks on the topics on Big Data and AI.
Currently, he works as a Director of Big Data and AI Solutions with Alibaba Cloud. In this role, he consults his clients to be more data-driven and achieve success by the practical application of Big Data and AI technologies.
Artificial Intelligence: In The Perspective Of Psychology
Computer scientists in AI seek to create intelligent computers that can learn and do complex tasks normally requiring human intellect. Building blocks of AI are specialized hardware and software for developing and retraining machine learning algorithms. There are many go-to languages for AI development, although the common options are Python, R, and Java. AI (AI) systems typically function by taking in massive volumes of labeled training data, processing that data to identify patterns and correlations, and then utilizing those insights to predict future outcomes. A chatbot may learn to mimic human conversation by analyzing instances of textual interactions between humans. In contrast, image recognition software can learn to recognize and describe items in photos by analyzing millions of examples.
What is Artificial Intelligence?Artificial Intelligence (AI) refers to the ability of machines, mostly computers, to demonstrate characteristics often associated with human intellect. Expert systems, Linguistics, voice recognition, and machine vision are all concrete examples of how Intellect is used. The potential uses of AI are limitless. Many fields and organizations may benefit from this technology. Machines with AI include chess-playing computers and autonomous vehicles, which are now being tested in the healthcare sector. In banking and finance, AI spots red flags such as odd debit card use or significant deposits. AI development prioritizes these three cognitive abilities: learning, reasoning, and self-correction.
Educating oneselfAcquiring data and developing rules for transforming it into useful knowledge is the emphasis of this area of AI programming. Algorithms are rules that tell computers how to do something by carrying out each step in the sequence.
Skills in Deductive ReasoningThis area of AI development emphasizes making strategic algorithmic decisions.
Methods of Self-ImprovementThis area of AI development aims to tweak programs to yield precise outcomes constantly.
What is significant about AI? What Contributes to AI?Artificial intelligence is a science and technology that draws on fields such as computer science, biology, psychology, linguistics, mathematics, and engineering. Developing computer functions associated with human intelligence, such as thinking, learning, and problem-solving, is a key focus of AI. One or more of the following areas can contribute to developing an intelligent system.
Various Artificially Intelligent VersionsThere are two types of AI, and they are weak and powerful, respectively.
Weak AIA computer program that can only do one specific task. Games like chess and digital assistants like Alexa and Siri are examples of weak AI systems. When you put a query to the helper, it responds.
Powerful AI SystemsThese are those that can do actions often associated with humans. These systems are often more intricate and sophisticated, and their software prepares them to solve problems autonomously in various settings. Applications for such systems include things like self-driving automobiles and operating rooms.
Where do we now apply AI?Today, AI is used in various contexts, often with varying degrees of complexity. Popular applications of AI include recommendation algorithms that suggest what people may enjoy next and chatbots that appear on online websites or in the shape of smart devices (e.g., Alexa or Siri). Forecasting the weather and the economy, streamlining manufacturing, and reducing duplicate cognitive work are just a few of the many uses for AI today. AI may be divided into four sorts, from task-specific systems to sentient ones that do not exist yet. Categories
Reactive MachinesThese AI systems are task-specific and memoryless. Deep Blue defeated Garry Kasparov in the 1990s. Deep Blue can detect chess pieces and make forecasts, but it lacks memory and cannot learn from previous mistakes.
Memory 2These AI systems can utilize prior experiences to make future judgments. Self-driving automobiles use this method for some decision-making.
Mind typeAI with social intellect can comprehend emotions. This AI can discern human intents and forecast behavior, allowing it to join human teams.
Self-awarenessAI systems in this category have self-awareness and consciousness, and Self-aware machines know themselves, and this AI does not exist.
AI is growing swiftly because it analyses enormous volumes of data more quickly and generates more accurate predictions than humans.
AdvantagesDigital virtual agents are constantly accessible and good at detail-oriented work.
Expensive; technological skills required, few competent AI developers; only what is displayed; and Unable to generalize.
Machine LearningMachine learning is the process through which a computer extracts meaning from training data. If you want an algorithm to detect spam in e-mails, for example, you must train the algorithm by exposing it to many instances of e-mails that have been manually tagged as spam or not spam. The algorithm “learns” to recognize patterns, such as the recurrence of specific terms or word combinations, that indicate the likelihood of an e-mail being spam. Machine learning may be used to solve a wide range of issues and data sets. You may train an algorithm to recognize photographs of cats in photo collections, possible fraud instances in insurance claims, turn handwriting into structured text, and so on. All of these scenarios would need labeled training sets.
Depending on the approach employed, an algorithm can be improved by adding a feedback loop that tells it where it went wrong. The distinction with AI is that a machine learning algorithm will never “understand” what it was programmed to perform. It may be able to detect spam, but it will need to learn what spam is and why we want it to be detected. Furthermore, if a new type of spam emerges, it will most likely be unable to recognize it unless someone (human) re-trains the algorithm. Most AI systems are built on machine learning. However, while a machine learning system may appear “smart,” it is not according to our definition of AI.
ConclusionRecent efforts in AI have led to progress in many areas, including some previously unexplored. Moreover, AI has become more and more concrete, powering automobiles, detecting sickness, and solidifying its place in popular culture. As the first computer program, IBM’s Deep Blue beat world chess champion Garry Kasparov in 1997. Two prior Jeopardy! IBM’s Watson beat champions, a supercomputer developed in the 1990s, and the public was enthralled. Costs associated with AI hardware, software, and personnel mean that many suppliers are integrating AI features in their base products or granting access to AIaaS platforms. Businesses and individuals may use AI as a service to try out the technology for their own needs without fully committing to any AI platform.
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