Source:

• Sage

• Sage

Source:

• Reuters, Published on February 10th, 2025.

• Reuters

Source:

• Sheerluxe, Published on January 29th, 2025.

What’s the most important thing business leaders or entrepreneurs need to be aware of?

“Leaders need to accept and understand what AI technology can do. I have lived through the internet boom and the initial AI comeback a decade ago in the form of machine learning. Both of these were waves of change in the IT industry that affected every aspect of our society and our lives. But I’ve never seen such a high speed of adoption as with generative AI. Even though the technology is young and not perfect, it is obvious that it fills a real need for most of us, individuals as well as businesses. Therefore, leaders must educate themselves in AI to learn the truth about its capabilities and risks. Use AI to solve a problem; do not invent a clever solution to a problem no one has. Be aware of the new risks that generative AI introduces, like hallucinations and toxicity, and allow use of AI accordingly for your own customers.” – Zorina Alliata, professor of responsible artificial intelligence, digital business & innovation at OPIT

Which industries do you predict will be most disrupted by AI in the next couple of years?

“The financial industry is always one of the first to adopt new technologies. Financial companies are already using generative AI for document processing, risk assessment, fraud prevention and algorithmic trading. Because of increased computing power, we also see AI growth in healthcare and life sciences for drug discovery and enhanced diagnostic procedures. Retail, education, logistics are also adopting AI at a high pace. Which industries will remain unaffected? None, really. Even in high-touch human professions like nursing, therapy, parenting, AI is a tool that can help. While not replacing the job entirely, the industry will change because the AI tools are changing the way the job is done.” – Zorina 

Are there any new business models emerging due to AI advancements?

“I think we will see more AI-as-a-service (AIaaS) offerings, where AI tools are built on top of large language models and offer specific capabilities. This is an area where there is a lot of innovation, and I’m excited to see this develop further. I already use AIaaS on a daily basis for better writing, research, creating videos and presentations, and code debugging.” – Zorina 

What are the biggest challenges for small businesses and start-ups in adopting AI technologies?

“A big risk is too much enthusiasm and optimism. Generative AI has been adopted at a great speed. When you first try it, it is amazing. It can write a whole paper in seconds. It can explain complex diagrams and concepts. It feels like the trusted assistant you always needed, but it’s important to remember that AI comes with risks. It’s one thing to write an AI service that recommends what movie you should watch next, and another thing to write an AI service that reads your X-ray and diagnoses if you have a tumour. These two applications of AI have very different risk thresholds. You need to plan your AI service or product to be appropriate for use and to minimise the risk for your customer. I’ve also seen start-ups that tried out an idea and are now planning to build a product out of it, without any understanding of what it takes to run AI services at scale. Having best practices implemented, a good operational foundation, governance and a clear operational model are all requisites for running any production systems, especially something as risky and fraught with unknowns as AI products are.” – Zorina 

Which ethical considerations should entrepreneurs keep in mind when integrating AI into their businesses?

“Some considerations when creating your risk strategy for AI include data privacy and security (ensuring responsible collection and use of customer data); transparency (being clear about how AI is used in products or services); fairness and bias (addressing potential biases in AI algorithms); job displacement (considering the impact on employees and planning for transitions); accountability (establishing clear responsibility for AI-driven decisions); and environmental impact (considering the energy consumption of AI systems).” – Zorina

How is AI changing customer expectations?

“Customer expectations have gone up significantly since generative AI enabled better interactions. Customers expect omni-channel communications, immediate responses, and predictive service. For those companies that still have fragmented data in several platforms and lack a cohesive customer journey, the learning curve will be steeper. The good news is, there are a lot of innovations in this area.” – Zorina 

What skills do you think entrepreneurs will need to succeed in an AI-dominated business world?

“Some skills that would be useful include:

• AI literacy: understanding the basics of AI, machine learning and data science.
• Data analysis & interpretation: ability to work with and derive insights from large datasets.
• Strategic thinking: identifying where AI can add value to business processes and products.
• Ethical decision-making: navigating the ethical implications of AI implementation.
• Adaptability & continuous learning: keeping up with rapidly evolving AI technologies.
• Human-AI collaboration: effectively working alongside AI systems.
• Soft skills: creativity, critical thinking, emotional intelligence and leadership will become even more valuable as AI handles more routine tasks.

As a leader, you are not required to write code or figure out the best way to deploy your model, but a high-level understanding of what AI can do will help you have meaningful conversations with your technical team and create AI products that are truly useful.” – Zorina

Finally, how will AI impact the workforce this year?

“There are several studies on this, such as the one the World Economic Forum (WEF) released this month about the status of work and the future of jobs. Some of the highlights are that AI and other technologies will continue to broaden digital access, with a first effect on increased demand for AI and data skills. The number of technology-related roles is the fastest growing, but frontline roles like farmworkers, delivery drivers and construction workers are predicted to see the largest growth. AI has evolved quickly to create images and videos, threatening the jobs of designers and movie producers. It was not what we would have predicted a few years ago. AI has a way of growing in unexpected ways, as we discover new paths of research and innovate ways to use it. I personally think it is hard to predict exactly where AI will go, and what will be the result of automating all routine tasks and behaving closer to humans. One thing we can be sure of is that people who understand AI and know how to use it will benefit from whatever new challenges are coming our way.” – Zorina

Read the full article below:

• Sheerluxe

Source:

• The Yuan, Published on October 25th, 2024.

By Zorina Alliata

ALEXANDRIA, VIRGINIA – In recent years, artificial intelligence (AI) has grown and developed into something much bigger than most people could have ever expected. Jokes about robots living among humans no longer seem so harmless, and the average person began to develop a new awareness of AI and all its uses. Unfortunately, however – as is often a human tendency – people became hyper-fixated on the negative aspects of AI, often forgetting about all the good it can do. One should therefore take a step back and remember that humanity is still only in the very early stages of developing real intelligence outside of the human brain, and so at this point AI is almost like a small child that humans are raising.

AI is still developing, growing, and adapting, and like any new tech it has its drawbacks. At one point, people had fears and doubts about electricity, calculators, and mobile phones – but now these have become ubiquitous aspects of everyday life, and it is not difficult to imagine a future in which this is the case for AI as well.

The development of AI certainly comes with relevant and real concerns that must be addressed – such as its controversial role in education, the potential job losses it might lead to, and its bias and inaccuracies. For every fear, however, there is also a ray of hope, and that is largely thanks to people and their ingenuity.

Looking at education, many educators around the world are worried about recent developments in AI. The frequently discussed ChatGPT – which is now on its fourth version – is a major red flag for many, causing concerns around plagiarism and creating fears that it will lead to the end of writing as people know it. This is one of the main factors that has increased the pessimistic reporting about AI that one so often sees in the media.

However, when one actually considers ChatGPT in its current state, it is safe to say that these fears are probably overblown. Can ChatGPT really replace the human mind, which is capable of so much that AI cannot replicate? As for educators, instead of assuming that all their students will want to cheat, they should instead consider the options for taking advantage of new tech to enhance the learning experience. Most people now know the tell-tale signs for identifying something that ChatGPT has written. Excessive use of numbered lists, repetitive language and poor comparison skills are just three ways to tell if a piece of writing is legitimate or if a bot is behind it. This author personally encourages the use of AI in the classes I teach. This is because it is better for students to understand what AI can do and how to use it as a tool in their learning instead of avoiding and fearing it, or being discouraged from using it no matter the circumstances.

Educators should therefore reframe the idea of ChatGPT in their minds, have open discussions with students about its uses, and help them understand that it is actually just another tool to help them learn more efficiently – and not a replacement for their own thoughts and words. Such frank discussions help students develop their critical thinking skills and start understanding their own influence on ChatGPT and other AI-powered tools.

By developing one’s understanding of AI’s actual capabilities, one can begin to understand its uses in everyday life. Some would have people believe that this means countless jobs will inevitably become obsolete, but that is not entirely true. Even if AI does replace some jobs, it will still need industry experts to guide it, meaning that entirely new jobs are being created at the same time as some older jobs are disappearing.

Adapting to AI is a new challenge for most industries, and it is certainly daunting at times. The reality, however, is that AI is not here to steal people’s jobs. If anything, it will change the nature of some jobs and may even improve them by making human workers more efficient and productive. If AI is to be a truly useful tool, it will still need humans. One should remember that humans working alongside AI and using it as a tool is key, because in most cases AI cannot do the job of a person by itself.

Is AI biased?

Why should one view AI as a tool and not a replacement? The main reason is because AI itself is still learning, and AI-powered tools such as ChatGPT do not understand bias. As a result, whenever ChatGPT is asked a question it will pull information from anywhere, and so it can easily repeat old biases. AI is learning from previous data, much of which is biased or out of date. Data about home ownership and mortgages, e.g., are often biased because non-white people in the United States could not get a mortgage until after the 1960s. The effect on data due to this lending discrimination is only now being fully understood.

AI is certainly biased at times, but that stems from human bias. Again, this just reinforces the need for humans to be in control of AI. AI is like a young child in that it is still absorbing what is happening around it. People must therefore not fear it, but instead guide it in the right direction.

For AI to be used as a tool, it must be treated as such. If one wanted to build a house, one would not expect one’s tools to be able to do the job alone – and AI must be viewed through a similar lens. By acknowledging this aspect of AI and taking control of humans’ role in its development, the world would be better placed to reap the benefits and quash the fears associated with AI. One should therefore not assume that all the doom and gloom one reads about AI is exactly as it seems. Instead, people should try experimenting with it and learning from it, and maybe soon they will realize that it was the best thing that could have happened to humanity.

Read the full article below:

• The Yuan

Source:

• Il Sole 24 Ore, Published on October 28th, 2024.

By Redazione Scuola

The data emerges from a survey carried out on LinkedIn by OPIT – Open Institute of Technology on the occasion of the start of the new academic year of the institution led by Francesco Profumo

Artificial Intelligence must become a subject of study starting from High School, given that this expertise is increasingly requested in job advertisements. This is the opinion of the LinkedIn community, consulted by OPIT – Open Institute of Technology, an academic institution accredited in the EU, led by Professor Francesco Profumo, former Minister of Education and Rector, and by Riccardo Ocleppo, founder and director. 66% of those who frequent the famous social network believe it is essential to introduce the teaching of Artificial Intelligence already in High School. Additionally, 72% noted an increase in mentions of AI in job ads, while 48% said the use of AI was considered an essential requirement by the companies where they applied for jobs. 38% use Artificial Intelligence mainly for writing texts, a further 38% for specific analysis and research, while 23% use it for translations.

OPIT

Open Institute of Technology carried out the survey on a sample of its followers (to date there are around 8,000 followers worldwide). The survey was launched on the occasion of the start of the new academic year of OPIT (October 2024) and involved professionals, students and technology enthusiasts, offering a significant insight into the perceptions and current trends regarding the use and teaching of Artificial Intelligence. The evidence from the survey highlights an increasingly widespread propensity towards a future in which Artificial Intelligence plays a crucial role. It is no longer a distant concept, but a reality that is already manifesting itself in daily work dynamics. Companies and professionals are rapidly adapting their strategies and skills to remain competitive in an ever-changing market, where the use of AI has become a fundamental element.

“Opportunities to innovate and improve professional development”

“The growing awareness of the importance of Artificial Intelligence in the workplace suggests that professionals are actively integrating these skills into their daily practices. This change offers opportunities to innovate and improve professional development” – explained Riccardo Ocleppo. “The technological transition we are experiencing is rapidly transforming the world of work, and AI will be increasingly central to this evolution. Rather than fear it, it is essential to study it, know it and understand its potential. Only through conscious preparation and a proactive approach will we be able to make the most of the opportunities that this technology offers. One of the distinctive elements of OPIT is precisely the integration of the teaching of Artificial Intelligence, in different modalities and with different perspectives, in all programs. This approach provides students with the appropriate tools to successfully face a constantly changing professional context, characterized by the growing demand for updated skills in the digital field”.

Reference academic reality

With two degrees already started in September 2023 – a three-year degree in Modern Computer Science and a Master’s degree in Applied Data Science & AI – and four new degree courses starting in September 2024 (a three-year degree in Digital Business, and the Master’s degrees in Enterprise Cybersecurity, Digital Business & Innovation and Responsible Artificial Intelligence, which brings the overall offer to 6 degrees), today OPIT is an academic institution of reference for those who intend to take up the challenges of a job market increasingly projected towards Artificial Intelligence, technology, digitalisation and information security. And the interest in three-year degrees in Computer Science and Digital Business is such that OPIT has reopened registrations for these courses, offering the possibility of entry from January. Today OPIT has more than 300 students from 78 countries around the world. The highest percentages come from Italy (31%) and Europe (36%) followed, to a lesser extent, by other areas of the world: North America, Asia, Africa, Latin America and the Middle East.

Read the full article below (in Italian):

• Il Sole 24 Ore

Source:

• Authority Magazine Medium, Published on September 15th, 2024.

Gaining hands-on experience through projects, internships, and collaborations is vital for understanding how to apply AI in various industries and domains. Use Kaggle or get a free cloud account and start experimenting. You will have projects to discuss at your next interviews.

By David Leichner, CMO at Cybellum

14 min read

Artificial Intelligence is now the leading edge of technology, driving unprecedented advancements across sectors. From healthcare to finance, education to environment, the AI industry is witnessing a skyrocketing demand for professionals. However, the path to creating a successful career in AI is multifaceted and constantly evolving. What does it take and what does one need in order to create a highly successful career in AI?

In this interview series, we are talking to successful AI professionals, AI founders, AI CEOs, educators in the field, AI researchers, HR managers in tech companies, and anyone who holds authority in the realm of Artificial Intelligence to inspire and guide those who are eager to embark on this exciting career path.

As part of this series, we had the pleasure of interviewing Zorina Alliata.

Zorina Alliata is an expert in AI, with over 20 years of experience in tech, and over 10 years in AI itself. As an educator, Zorina Alliata is passionate about learning, access to education and about creating the career you want. She implores us to learn more about ethics in AI, and not to fear AI, but to embrace it.

Thank you so much for joining us in this interview series! Before we dive in, our readers would like to learn a bit about your origin story. Can you share with us a bit about your childhood and how you grew up?

I was born in Romania, and grew up during communism, a very dark period in our history. I was a curious child and my parents, both teachers, encouraged me to learn new things all the time. Unfortunately, in communism, there was not a lot to do for a kid who wanted to learn: there was no TV, very few books and only ones that were approved by the state, and generally very few activities outside of school. Being an “intellectual” was a bad thing in the eyes of the government. They preferred people who did not read or think too much. I found great relief in writing, I have been writing stories and poetry since I was about ten years old. I was published with my first poem at 16 years old, in a national literature magazine.

Read the full article below:

• Authority Magazine Medium

AI, and its integration with society, had an incredible acceleration in recent months. By now, it seems certain that AI will be the fourth GPT (General Purpose Technology) of human history: one of those few technologies or inventions that radically and indelibly change society. The last of these technologies was ICT (internet, semiconductor industry, telecommunications); before this, electricity and the steam engine were the first 2 GPTs.

All three GPTs had a huge impact on the overall productivity and advancement of our society with, of course, a profound impact on the world of work. Such an impact, though, was very different across these technologies. The advent of electricity and the steam motor allowed the displacement of large masses of workers from more archaic and manual jobs to their equivalent jobs in the new industrial era, where not many skills were required. The advent of ICT, on the other hand, has generated enormous job opportunities, but also the need to develop meaningful skills to pursue them.

As a result, an increasingly large share of the economic benefit deriving from the advent of ICT has gradually been polarized towards people who had (and have) these skills in society. Suffice it to say that, already in 2017, the richest 1% of America owned twice the wealth of the “poorest” 90%.

It is difficult to make predictions about how the advent of AI will impact this trend already underway. But there are some very clear elements: one of these is that quality education in technology (and not only) will increasingly play a primary role in being able to secure the best career opportunities for a successful future in this new era.

To play a “lead actor” role in this change, though, the world of education – and in particular that of undergraduate and postgraduate education – requires a huge change towards being much more flexible, aligned to today’s needs of students and companies, and affordable.

This model needs to be seriously challenged and adapted to the times: solid foundational learning remains an essential prerogative. But in a “fast” world in rapid change like today’s, knowledge acquired along this “linear” path will not be able to accompany people in their professions until the end of their careers. The “utility period” of the knowledge we acquire today reduces every day, and this emphasizes how essential continuous learning is throughout our lives.

The transition must therefore be towards a more circular pattern for learning. A model in which one returns “to the school desk” several times in life, in order to update oneself, and forget “obsolete” knowledge, making room for new production models, new ways of thinking, organizing, and new technologies.

In this context, Education providers must rethink the way they operate and how they intend to address this need for lifelong learning.

Higher Education Institutions, as accredited bodies and guarantors of the quality of education (OPIT – Open Institute of Technology among these), have the honor of playing a primary role in this transition.

But also the great burden of rethinking their model from scratch which, in a digital age, cannot be a pure and simple digital transposition of the old analog learning model.

The Institutions Universities are called upon to review and keep updated their own study programmes, think of new, more flexible and faster ways of offering them to a wider public, forge greater connections with companies, and ultimately provide them with students who are immediately ready to successfully enter the dynamics of production. And, of course, be more affordable and accessible: quality education in the AI era cannot cost tens of thousands of dollars, and needs to be accessed from wherever the students are.

With OPIT – Open Institute of Technology, this is the path we have taken, taking advantage of the great privilege of being able to start a new path, without preconceptions or “attachment” to the past. We envision a model of a new, digital-first, higher education institution capable of addressing all the points above, and accompany students and professionals throughout their lifetime learning journey.

We are at the beginning, and we hope that the modern and fresh approach we are following can be an interesting starting point for other universities as well.

Authors

Prof. Francesco Profumo, Rector of OPIT – Open Institute of Technology
Former Minister of Education, University and Research of Italy, Academician and author, former President of the National Research Council of Italy, and former Rector of Politecnico di Torino. He is an honorary member of various scientific associations.

Riccardo Ocleppo, Managing Director of OPIT
Founder of OPIT, Founder of Docsity.com, one of the biggest online communities for students with 19+ registered users. MSc in Management at London Business School, MSc in Electronics Engineering at Politecnico di Torino

Prof. Lorenzo Livi, Programme Head at OPIT
Former Associate Professor of Machine Learning at the University of Manitoba, Honorary Senior Lecturer at the University of Exeter, Ph.D. in Computer Science at Università La Sapienza.

Reinforcement learning is a very useful (and currently popular) subtype of machine learning and artificial intelligence. It is based on the principle that agents, when placed in an interactive environment, can learn from their actions via rewards associated with the actions, and improve the time to achieve their goal.

In this article, we’ll explore the fundamental concepts of reinforcement learning and discuss its key components, types, and applications.

Definition of Reinforcement Learning

We can define reinforcement learning as a machine learning technique involving an agent who needs to decide which actions it needs to do to perform a task that has been assigned to it most effectively. For this, rewards are assigned to the different actions that the agent can take at different situations or states of the environment. Initially, the agent has no idea about the best or correct actions. Using reinforcement learning, it explores its action choices via trial and error and figures out the best set of actions for completing its assigned task.

The basic idea behind a reinforcement learning agent is to learn from experience. Just like humans learn lessons from their past successes and mistakes, reinforcement learning agents do the same – when they do something “good” they get a reward, but, if they do something “bad”, they get penalized. The reward reinforces the good actions while the penalty avoids the bad ones.

Reinforcement learning requires several key components:

• Agent – This is the “who” or the subject of the process, which performs different actions to perform a task that has been assigned to it.
• Environment – This is the “where” or a situation in which the agent is placed.
• Actions – This is the “what” or the steps an agent needs to take to reach the goal.
• Rewards – This is the feedback an agent receives after performing an action.

Before we dig deep into the technicalities, let’s warm up with a real-life example. Reinforcement isn’t new, and we’ve used it for different purposes for centuries. One of the most basic examples is dog training.

Let’s say you’re in a park, trying to teach your dog to fetch a ball. In this case, the dog is the agent, and the park is the environment. Once you throw the ball, the dog will run to catch it, and that’s the action part. When he brings the ball back to you and releases it, he’ll get a reward (a treat). Since he got a reward, the dog will understand that his actions were appropriate and will repeat them in the future. If the dog doesn’t bring the ball back, he may get some “punishment” – you may ignore him or say “No!” After a few attempts (or more than a few, depending on how stubborn your dog is), the dog will fetch the ball with ease.

We can say that the reinforcement learning process has three steps:

1. Interaction
2. Learning
3. Decision-making

Types of Reinforcement Learning

There are two types of reinforcement learning: model-based and model-free.

Model-Based Reinforcement Learning

With model-based reinforcement learning (RL), there’s a model that an agent uses to create additional experiences. Think of this model as a mental image that the agent can analyze to assess whether particular strategies could work.

Some of the advantages of this RL type are:

• It doesn’t need a lot of samples.
• It can save time.
• It offers a safe environment for testing and exploration.

The potential drawbacks are:

• Its performance relies on the model. If the model isn’t good, the performance won’t be good either.
• It’s quite complex.

Model-Free Reinforcement Learning

In this case, an agent doesn’t rely on a model. Instead, the basis for its actions lies in direct interactions with the environment. An agent tries different scenarios and tests whether they’re successful. If yes, the agent will keep repeating them. If not, it will try another scenario until it finds the right one.

What are the advantages of model-free reinforcement learning?

• It doesn’t depend on a model’s accuracy.
• It’s not as computationally complex as model-based RL.
• It’s often better for real-life situations.

Some of the drawbacks are:

• It requires more exploration, so it can be more time-consuming.
• It can be dangerous because it relies on real-life interactions.

Model-Based vs. Model-Free Reinforcement Learning: Example

Understanding model-based and model-free RL can be challenging because they often seem too complex and abstract. We’ll try to make the concepts easier to understand through a real-life example.

Let’s say you have two soccer teams that have never played each other before. Therefore, neither of the teams knows what to expect. At the beginning of the match, Team A tries different strategies to see whether they can score a goal. When they find a strategy that works, they’ll keep using it to score more goals. This is model-free reinforcement learning.

On the other hand, Team B came prepared. They spent hours investigating strategies and examining the opponent. The players came up with tactics based on their interpretation of how Team A will play. This is model-based reinforcement learning.

Who will be more successful? There’s no way to tell. Team B may be more successful in the beginning because they have previous knowledge. But Team A can catch up quickly, especially if they use the right tactics from the start.

Reinforcement Learning Algorithms

A reinforcement learning algorithm specifies how an agent learns suitable actions from the rewards. RL algorithms are divided into two categories: value-based and policy gradient-based.

Value-Based Algorithms

Value-based algorithms learn the value at each state of the environment, where the value of a state is given by the expected rewards to complete the task while starting from that state.

Q-Learning

This model-free, off-policy RL algorithm focuses on providing guidelines to the agent on what actions to take and under what circumstances to win the reward. The algorithm uses Q-tables in which it calculates the potential rewards for different state-action pairs in the environment. The table contains Q-values that get updated after each action during the agent’s training. During execution, the agent goes back to this table to see which actions have the best value.

Deep Q-Networks (DQN)

Deep Q-networks, or deep q-learning, operate similarly to q-learning. The main difference is that the algorithm in this case is based on neural networks.

SARSA

The acronym stands for state-action-reward-state-action. SARSA is an on-policy RL algorithm that uses the current action from the current policy to learn the value.

Policy-Based Algorithms

These algorithms directly update the policy to maximize the reward. There are different policy gradient-based algorithms: REINFORCE, proximal policy optimization, trust region policy optimization, actor-critic algorithms, advantage actor-critic, deep deterministic policy gradient (DDPG), and twin-delayed DDPG.

Examples of Reinforcement Learning Applications

The advantages of reinforcement learning have been recognized in many spheres. Here are several concrete applications of RL.

Robotics and Automation

With RL, robotic arms can be trained to perform human-like tasks. Robotic arms can give you a hand in warehouse management, packaging, quality testing, defect inspection, and many other aspects.

Another notable role of RL lies in automation, and self-driving cars are an excellent example. They’re introduced to different situations through which they learn how to behave in specific circumstances and offer better performance.

Gaming and Entertainment

Gaming and entertainment industries certainly benefit from RL in many ways. From AlphaGo (the first program that has beaten a human in the board game Go) to video games AI, RL offers limitless possibilities.

Finance and Trading

RL can optimize and improve trading strategies, help with portfolio management, minimize risks that come with running a business, and maximize profit.

Healthcare and Medicine

RL can help healthcare workers customize the best treatment plan for their patients, focusing on personalization. It can also play a major role in drug discovery and testing, allowing the entire sector to get one step closer to curing patients quickly and efficiently.

Basics for Implementing Reinforcement Learning

The success of reinforcement learning in a specific area depends on many factors.

First, you need to analyze a specific situation and see which RL algorithm suits it. Your job doesn’t end there; now you need to define the environment and the agent and figure out the right reward system. Without them, RL doesn’t exist. Next, allow the agent to put its detective cap on and explore new features, but ensure it uses the existing knowledge adequately (strike the right balance between exploration and exploitation). Since RL changes rapidly, you want to keep your model updated. Examine it every now and then to see what you can tweak to keep your model in top shape.

Explore the World of Possibilities With Reinforcement Learning

Reinforcement learning goes hand-in-hand with the development and modernization of many industries. We’ve been witnesses to the incredible things RL can achieve when used correctly, and the future looks even better. Hop in on the RL train and immerse yourself in this fascinating world.