ML

What can be a better way to spend your days listening to interesting bits about trending AI and Machine learning topics? Here’s a list of the 10 best AI and ML podcasts.

 

1. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Artificial intelligence and machine learning are fundamentally altering how organizations run and how individuals live. It is important to discuss the latest innovations in these fields to gain the most benefit from technology. The TWIML AI Podcast outreaches a large and significant audience of ML/AI academics, data scientists, engineers, tech-savvy business, and IT (Information Technology) leaders, as well as the best minds and gather the best concepts from the area of ML and AI.  

The podcast is hosted by a renowned industry analyst, speaker, commentator, and thought leader Sam Charrington. Artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science, and other technologies are discussed. 

 

2. The AI Podcast

One individual, one interview, one account. This podcast examines the effects of AI on our world. The AI podcast creates a real-time oral history of AI that has amassed 3.4 million listens and has been hailed as one of the best AI and machine learning podcasts. They always bring you a new story and a new 25-minute interview every two weeks. Consequently, regardless of the difficulties, you are facing in marketing, mathematics, astrophysics, paleo history, or simply trying to discover an automated way to sort out your kid’s growing Lego pile, listen in and get inspired. 

 

3. Data Skeptic

Data Skeptic launched as a podcast in 2014. Hundreds of interviews and tens of millions of downloads later, we are a widely recognized authoritative source on data science, artificial intelligence, machine learning, and related topics. 

Data Skeptic runs in seasons. By speaking with active scholars and business leaders who are somehow involved in our season’s subject, we probe it. 

We carefully choose each of our visitors using a system internally. Since we do not cooperate with PR firms, we are unable to reply to the daily stream of unsolicited submissions. Publishing quality research to the arxiv is the greatest approach to getting on the show. It is crawled. We will locate you. 

Data Skeptic is a boutique consulting company in addition to its podcast. Kyle participates directly in each project our team undertakes. Our work primarily focuses on end-to-end machine learning, cloud infrastructure, and algorithmic design. 

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. 

 

Pro-tip: Enroll in the data science boot camp today to learn the basics of the industry

 

 

 

 

4. Podcast.ai 

Podcast.ai is entirely generated by artificial intelligence. Every week, they explore a new topic in-depth, and listeners can suggest topics or even guests and hosts for future episodes. Whether you are a machine learning enthusiast, just want to hear your favorite topics covered in a new way or even just want to listen to voices from the past brought back to life, this is the podcast for you.

The podcast aims to put incremental advances into a broader context and consider the global implications of developing technology. AI is about to change your world, so pay attention. 

 

5. The Talking Machines

Talking machines is a podcast hosted by Katherine Gorman and Neil Lawrence. The objective of this show is to bring you clear conversations with experts in the field of machine learning, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers. 

 

6. Linear Digressions

If you are interested in learning about unusual applications of machine learning and data science. In each episode of linear digressions, your hosts explore machine learning and data science through interesting apps. Ben Jaffe and Katie Malone host the show, they assure themselves to produce the most exciting additions in the industry such as AI-driven medical assistants, open policing data, causal trees, the grammar of graphics and a lot more.  

 

7. Practical AI: Machine Learning, Data Science

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, businesspeople, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs (Generative adversarial networks), MLOps (machine learning operations) (machine learning operations), AIOps, and more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you! 

 

8. Data Stories

Enrico Bertini and Moritz Stefaner discuss the latest developments in data analytics, visualization, and related topics. The data stories podcast consists of regular new episodes on a range of discussion topics related to data visualization. It shares the importance of data stories in different fields including statistics, finance, medicine, computer science, and a lot more to name. The podcast’s hosts Enrico and Moritz invite industry leaders, experienced professionals, and instructors in data visualization to share the stories and the importance of representation of data visuals into appealing charts and graphs. 

 

9. The Artificial Intelligence Podcast

The Artificial intelligence podcast is hosted by Dr. Tony Hoang. This podcast talks about the latest innovations in the artificial intelligence and machine learning industry. The recent episode of the podcast discusses text-to-image generator, Robot dog, soft robotics, voice bot options, and a lot more.  

 

10. Learning Machines 101

Smart machines employing artificial intelligence and machine learning are prevalent in everyday life. The objective of this podcast series is to inform students and instructors about the advanced technologies introduced by AI and the following: 

•  How do these devices work? 
• Where do they come from? 
• How can we make them even smarter? 
• And how can we make them even more human-like? 

 

Have we missed any of your favorite podcasts?

 Do not forget to share in comments the names of your most favorite AI and ML podcasts. Read this amazing blog if you want to know about Data Science podcasts.

Be it Netflix, Amazon, or another mega-giant, their success stands on the shoulders of experts, analysts are busy deploying machine learning through supervised, unsupervised, and reinforcement successfully. 

The tremendous amount of data being generated via computers, smartphones, and other technologies can be overwhelming, especially for those who do not know what to make of it. To make the best use of data researchers and programmers often leverage machine learning for an engaging user experience.

Many advanced techniques that are coming up every day for data scientists of all supervised, and unsupervised, reinforcement learning is leveraged often. In this article, we will briefly explain what supervised, unsupervised, and reinforcement learning is, how they are different, and the relevant uses of each by well-renowned companies.

Supervised learning

Supervised machine learning is used for making predictions from data. To be able to do that, we need to know what to predict, which is also known as the target variable. The datasets where the target label is known are called labeled datasets to teach algorithms that can properly categorize data or predict outcomes. Therefore, for supervised learning:

• We need to know the target value
• Targets are known in labeled datasets

Let’s look at an example: If we want to predict the prices of houses, supervised learning can help us predict that. For this, we will train the model using characteristics of the houses, such as the area (sq ft.), the number of bedrooms, amenities nearby, and other similar characteristics, but most importantly the variable that needs to be predicted – the price of the house.

A supervised machine learning algorithm can make predictions such as predicting the different prices of the house using the features mentioned earlier, predicting trends of future sales, and many more.

Sometimes this information may be easily accessible while other times, it may prove to be costly, unavailable, or difficult to obtain, which is one of the main drawbacks of supervised learning.

Saniye Alabeyi, Senior Director Analyst at Garnet calls Supervised learning the backbone of today’s economy, stating:

“Through 2022, supervised learning will remain the type of ML utilized most by enterprise IT leaders” (Source).

Types of problems:

Supervised learning deals with two distinct kinds of problems:

1. Classification problems
2. Regression problems

 

Classification problem: In the case of classification problems, examples are classified into one or more classes/ categories.

For example, if we are trying to predict that a student will pass or fail based on their past profile, the prediction output will be “pass/fail.” Classification problems are often resolved using algorithms such as Naïve Bayes, Support Vector Machines, Logistic Regression, and many others.

Regression problem: A problem in which the output variable is either a real or continuous value, s is defined as a regression problem. Bringing back the student example, if we are trying to predict that a student will pass or fail based on their past profuse, the prediction output will be numeric, such as “68%” likely to score.

Predicting the prices of houses in an area is an example of a regression problem and can be solved using algorithms such as linear regression, non-linear regression, Bayesian linear regression, and many others.

Why Amazon, Netflix, and YouTube are great fans of supervised learning

Recommender systems are a notable example of supervised learning. E-commerce companies such as Amazon, streaming sites like Netflix, and social media platforms such as TikTok, Instagram, and even YouTube among many others make use of recommender systems to make appropriate recommendations to their target audience.

Unsupervised learning

Imagine receiving swathes of data with no obvious pattern in it. A dataset with no labels or target values cannot come up with an answer to what to predict. Does that mean the data is all waste? Nope! The dataset likely has many hidden patterns in it.

Unsupervised learning studies the underlying patterns and predicts the output. In simple terms, in unsupervised learning, the model is only provided with the data in which it looks for hidden or underlying patterns.

Unsupervised learning is most helpful for projects where individuals are unsure of what they are looking for in data. It is used to search for unknown similarities and differences in data to create corresponding groups.

An application of unsupervised learning is the categorization of users based on their social media activities.

Commonly used unsupervised machine learning algorithms include K-means clustering, neural networks, principal component analysis, hierarchical clustering, and many more.

Reinforcement learning

Another type of machine learning is reinforcement learning.

In reinforcement learning, algorithms learn in an environment on their own. The field has gained quite some popularity over the years and has produced a variety of learning algorithms.

Reinforcement learning is neither supervised nor unsupervised as it does not require labeled data or a training set. It relies on the ability to monitor the response to the actions of the learning agent.

Most used in gaming, robotics, and many other fields, reinforcement learning makes use of a learning agent. A start state and an end state are involved. For the learning agent to reach the final or end stage, different paths may be involved.

• An agent may also try to manipulate its environment and may travel from one state to another
• On success, the agent is rewarded but does not receive any reward or appreciation for failure
• Amazon has robots picking and moving goods in warehouses because of reinforcement learning

Numerous IT companies including Google, IBM, Sony, Microsoft, and many others have established research centers focused on projects related to reinforcement learning.

Social media platforms like Facebook have also started implementing reinforcement learning models that can consider different inputs such as languages, integrate real-world variables such as fairness, privacy, and security, and more to mimic human behavior and interactions. (Source)

Amazon also employs reinforcement learning to teach robots in its warehouses and factories how to pick up and move goods.

Comparison between supervised, unsupervised, and reinforcement learning

Caption: Differences between supervised, unsupervised, and reinforcement learning algorithms

	Supervised learning	Unsupervised learning	Reinforcement learning
Definition	Makes predictions from data	Segments and groups data	Reward-punishment system and interactive environment
Types of data	Labelled data	Unlabeled data	Acts according to a policy with a final goal to reach (No or predefined data)
Commercial value	High commercial and business value	Medium commercial and business value	Little commercial use yet
Types of problems	Regression and classification	Association and Clustering	Exploitation or Exploration
Supervision	Extra supervision	No	No supervision
Algorithms	Linear Regression, Logistic Regression, SVM, KNN and so forth	K – Means clustering,  C – Means, Apriori	Q – Learning,  SARSA
Aim	Calculate outcomes	Discover underlying patterns	Learn a series of action
Application	Risk Evaluation, Forecast Sales	Recommendation System, Anomaly Detection	Self-Driving Cars, Gaming, Healthcare

Which is the better Machine Learning technique?

We learned about the three main members of the machine learning family essential for deep learning. Other kinds of learning are also available such as semi-supervised learning, or self-supervised learning.

Supervised, unsupervised, and reinforcement learning, are all used for different to complete diverse kinds of tasks. No single algorithm exists that can solve every problem, as problems of different natures require different approaches to resolve them.

Despite the many differences between the three types of learning, all of these can be used to build efficient and high-value machine learning and Artificial Intelligence applications. All techniques are used in different areas of research and development to help solve complex tasks and resolve challenges.

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Artificial intelligence and machine learning are part of our everyday lives. These data science movies are my favorite.

Advanced artificial intelligence (AI) systems, humanoid robots, and machine learning are not just in science fiction movies anymore. We come across this technological advancement in our everyday life. Today our cellphones, cars, TV sets, and even household appliances are using machine learning to improve themselves.

As we advance towards faster connectivity and the possibility of making the Internet of Things (IoT) more common, the idea of machines taking over and controlling humans might sound funny, but there are some challenges that need attention, including ethical and moral dimensions of machines thinking and acting like humans.

Interstellar (2014)

Christopher Nolan’s cinematic success won an Oscar for best visual effects and grossed over $677 million worldwide.  The film is centered around astronauts’ journey to the far reaches of our galaxy to find a suitable planet for life as Earth is slowly dying. The lead character played by Oscar winner Matthew McConaughey, an astronaut and spaceship pilot, along with mission commander Brand and science specialists are heading towards a newly discovered wormhole.

The mission takes the astronauts on a spectacular interstellar journey through time and space, but at the same time they miss out on their own life back at home light years away. On board the spaceship, Endurance is a pair of quadrilateral robots called TARS and CASE. They surprisingly resemble the monoliths from 2001: A Space Odyssey.

TARS is one of the crew members of mission Endurance. TARS’ personality is witty, sarcastic, and humorous, traits programmed into him to make him a suitable companion for its human crew on this decades-long journey.

CASE’s mission is maintenance and operations of the Endurance in the absence of human crew members. CASE’s personality is quiet and reserved as opposed to TARS. TARS and CASE are true embodiments of the progress that human beings have made in AI technology, thus promising us great adventures in the future.

The Imitation Game (2014)

Based on the real-life story of Alan Turing, A.K.A. the father of modern computer science, The Imitation Game is centered around Turing and his team of code-breakers at top secret British Government Code and Cipher School. They’re determined to decipher the Nazi German military code called “Enigma”. Enigma is a key part of the Nazi military strategy to safely transmit important information to its units.

To crack this Enigma, Turing creates a primitive computer system that would consider permutations at a faster rate than any human. This achievement helped Allied forces ensure victory over Nazi German in the second world war. The movie not only portrays the impressive life of Alan Turning but also describes the important process of creating the first ever machine of its kind giving birth to the field of cryptography and cyber security.

The Terminator (1984)

The cult classic, Terminator, starring Arnold Schwarzenegger as a cyborg assassin from the future is the perfect combination of action, sci-fi technology, and personification of machine learning.

The humanistic cyborg is created by Cyberdyne Systems and is known as T-800 model 101. Designed specifically for infiltration and combat and is sent on a mission to kill Sarah Connor before she gives birth to John Connor, who would become the ultimate savior for humanity after the robotic uprising.

In this classic, we get to see advanced artificial intelligence in the works and how it has considered humanity the biggest threat to the world. Bent upon total destruction of the human race, only freedom fighters led by John Connor stand in their way. Therefore, sending The Terminator back in time to alter their future is the top priority.

Blade Runner 2049 (2017)

The sequel to the 1982 original Blade runner has impressive visuals capturing the audience’s attention throughout the film. The story is about bio-engineered humans known as “Replicants” after the uprising of 2022 they are being hunted down by LAPD Blade Runner. Blade Runner is an officer who hunts and retires (kills) rogue replicants. Ryan Gosling stars as “K” hunting down replicants who are considered a threat to the world. Every decision he makes is based on analysis. The films explore the relationships and emotions of artificially intelligent beings and raise moral questions regarding freedom to live and the life of self-aware technology.

I, Robot (2004)

Will Smith stars as Chicago policeman Del Spooner in the year 2035. He is highly suspicious of the A.I technology, data science, and robots are being used as household helpers. One of these mass-produced robots (cueing in the data science / AI angle), named Sonny, goes rogue and is held responsible for the death of its owner. Its owner falls from a window on the 15^th floor. Del investigates this murder and discovers a larger threat to humanity by Artificial Intelligence. As the investigation continues, there are multiple murder attempts on Del but he manages to barely escape with his life. The police detective continues to unravel mysterious threats from the A.I technology and tries to stop the mass uprising.

Minority Report (2002)

Minority Report and data science? That is correct! It is a 2002 action thriller directed by Steven Spielberg and starring Tom Cruise. The most common use of data science is using current data to infer new information, but here data are being used to predict crime predispositions. A group of humans gifted with psychic abilities (PreCogs) provide the Washington police force with information about crimes before they are committed.

Using visual data and other information by PreCogs, it is up to the PreCrime police unit to use data to explore the finer details of a crime in order to prevent it. However, things take a turn for the worst when one-day PreCogs predict John Anderson one of their own, is going to commit murder. To prove his innocence, he goes on a mission to find the “Minority Report” which is the prediction of the PreCog Agatha that might tell a different story and prove John’s innocence.

Her (2013)

Her (2013) is a Spike Jones science fiction film starring Joaquin Phoenix as Theodore Twombly, a lonely and depressed writer. He is going through a divorce at the time and, to make things easier, purchases an advanced operating system with an A.I. virtual assistant designed to adapt and evolve. The virtual assistant names itself Samantha. Theodore is amazed at the operating system’s ability to emotionally connect with him. Samantha uses its highly advanced intelligence system to help with every one of Theodore’s needs, but now he’s facing an inner conflict of being in love with a machine.

Ex-Machina (2014)

The story is centered around a 26-year-old programmer, Caleb, who wins a competition to spend a week at a private mountain retreat belonging to the CEO of Blue Book, a search engine company. Soon afterward Caleb realizes he’s participating in an experiment to interact with the world’s first real artificially intelligent robot. In this British science fiction, A.I do not want world domination but simply want the same civil rights as humans.

The Machine (2013)

The Machine is an Indie-British film centered around two artificial intelligence engineers who come together to create the first-ever, self-aware artificial intelligence machines. These machines are created for the Ministry of Defense. The Government’s intention is to create a lethal soldier for war. The cyborg told its designer, “I’m a part of the new world and you’re part of the old.” this chilling statement gives you the idea of what is to come next.

Transcendence (2014)

Transcendence is a story about a brilliant researcher in the field of Artificial Intelligence, Dr. Will Caster, played by Johnny Depp. He’s working on a project to create a conscious machine that combines the collective intelligence of everything along with the full range of human emotions. Dr. Caster has gained fame due to his ambitious project and controversial experiments. He’s also become a target for anti-technology extremists who is willing to do anything to stop him.

However, Dr. Caster becomes more determined to accomplish his ambitious goals and achieve the ultimate power. His wife Evelyn and best friend Max are concerned with Will’s unstoppable appetite for knowledge which is evolving into a terrifying quest for power.

A.I. ARTIFICIAL INTELLIGENCE (2001)

A.I Artificial Intelligence is a science fiction drama directed by Steven Spielberg. The story takes us to the not-so-distant future where ocean waters are rising due to global warming and most coastal cities are flooded. Humans move to the interior of the continents and keep advancing their technology. One of the newest creations is realistic robots known as “Mechas”. Mechas are humanoid robots, very complex but lack emotions.

This changes when David, a prototype Mecha child capable of experiencing love, is developed. He is given to Henry and his wife Monica, whose son contracted a rare disease and has been placed in cryostasis. David is providing all the love and support for his new family, but things get complicated when Monica’s real son returns home after a cure is discovered. The film explores every possible emotional interaction humans could have with an emotionally capable A.I. technology.

Moneyball (2011)

Billy Beane, played by Brad Pitt, and his assistant, Peter Brand (Jonah Hill), are faced with the challenge of building a winning team for the Major League Baseball’s Oakland Athletics’ 2002 season with a limited budget. To overcome this challenge Billy uses Brand’s computer-generated statistical analysis to analyze and score players’ potential and assemble a highly competitive team. Using historical data and predictive modeling they manage to create a playoff-bound MLB team with a limited budget.

Margin Call (2011)

The 2011 American drama film written and directed by J.C. Chandor is based on the events of the 2007-08 global financial crises. The story takes place over a 24-hour period at a large Wall Street investment bank. One of the junior risk analysts discovers a major flaw in the risk models which has led their firm to invest in the wrong things, winding up at the brink of financial disaster.

A seemingly simple error is in fact affecting millions of lives. This is not only limited to the financial world. An economic crisis like this caused by flawed behavior between humans and machines can have trickle-down effects on ordinary people. Technology doesn’t exist in a bubble, it affects everyone around it and spreads exponentially. Margin Call explores the impact of technology and data science on our lives.

21 (2008)

Ben Campbell, a mathematics student at MIT, is accepted at the prestigious Harvard Medical School but he’s unable to afford the $300,000 tuition. One of his professors at MIT, Micky Rosa (Kevin Spacey), asks him to join his blackjack team consisting of five other fellow students. Ben accepts the offer to win enough cash to pay his Harvard tuition. They fly to Las Vegas over the weekend to win millions of dollars using numbers, codes, and hand signals. This movie gives insights into Newton’s method and Fibonacci numbers from the perspective of six brilliant students and their professor.

Thanks for reading we hope you will enjoy our recommendations on data science-based movies. Also, check out the 18 Best Data Science Podcasts.

Want to learn more about AI, Machine Learning, and Data Science? Check out Data Science Dojo’s online Data Science Bootcamp program!

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