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Artificial intelligence vs. Data Science: top 5 differences | by Rijul Singh Malik | Aug, 2022 – DataDrivenInvestor



A blog about the top 5 differences between AI and data science.

Photo by Possessed Photography on Unsplash

1. What is the difference between AI and Data science?

Artificial intelligence (AI) is an umbrella term that encompasses all efforts to create machines that can perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. Data science is the scientific approach to extracting knowledge from data in various forms, including structured and unstructured data, for example, text and images, in order to solve business problems. Data science is a relatively new term that refers to both the process and the people involved in analyzing data and developing new algorithms to extract insights from the data. Data science is a more general term, which subsumes a number of more focused disciplines, including machine learning, statistics, data mining and others.

The field of artificial intelligence (AI) is still in its infancy. There are many different types of AI, and each has its own sub-field of research. While some types of AI are more mature than others, AI is still evolving toward greater autonomy and more human-like intelligence. Data science is an umbrella term used to describe a number of disciplines, often used in the same context as artificial intelligence. Data science is the application of statistical analysis, machine learning, and other data-oriented concepts to solve a problem. It is not a single field, but rather a combination of fields. Data science, at its core, is about problem-solving and the construction of models for your data.


2. What is AI?

Artificial Intelligence is the general term for software performing tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence is a field of computer science that studies the theory behind intelligent behavior and, in particular, the ability to solve problems automatically. Artificial Intelligence is also referred to as AI and can be found in all forms of computers. One of the greatest applications of AI is in machine learning, which is a subset of AI. Machine learning and artificial intelligence are often used interchangeably, but they are different in that machine learning is a technique for programming a computer to learn how to do a task or make a decision on its own. Machine learning is related to but different from the broader field of Artificial intelligence. Artificial intelligence is a broad and loosely defined field that studies agents that perceive their environment and take actions that maximize their chances of success. This definition of artificial intelligence is very different from the one that is most often used in the mainstream media.

Artificial Intelligence (AI) is a booming technology in the world today. We see it in televisions, cars, and even our phones. But what is AI? Perhaps it’s better to ask what it is not. AI is not a science fiction movie villain that is out to destroy our world. AI is not a robot with a gun that is on a mission to take over. AI is not just a buzzword either. It’s much more than that. AI is a technology that is only as good as the data that feeds it.

3. What is Data Science?

Data science is a hot topic in the business world, but what exactly is it? Data science is a combination of statistics, computer science, and mathematics. Data scientists play a crucial role in a lot of business decisions, especially for big data and analytics. But what about artificial intelligence (AI)? Are the two terms interchangeable? What are the top 5 differences between AI and data science?

What is Data Science? Data science is the application of data mining, machine learning, artificial intelligence, statistics and other information-related disciplines in order to extract knowledge from data and turn it into useful information. Data science is not one specific field of study, but a set of skills that are used in many different disciplines. Data scientists are behind almost every big data success story. The data scientist of the future will be able to ask the right questions and develop the most important data-based products and services. Data science is evolving, but currently it is an exciting mix of statistics, machine learning, artificial intelligence, applied mathematics, programming, visualization, and communication.

Data science is a relatively new field that deals with the analysis and manipulation of large datasets. The main objective of data science is to make sense of a huge amount of data and to extract useful information from it. With the rise of the Internet, the number of data points available has increased exponentially. According to Forbes, a single human’s lifetime of social media data is equal to 5.2 billion books. As a result, data science has become a relevant field in today’s world, allowing businesses to collect and analyze vast amounts of information.

4. What do data science and AI have in common?

Artificial Intelligence (AI) and data science are two of the hottest technologies around today, but the two are often confused with one another. Data science and artificial intelligence are not the same thing. Data science is a collection of techniques for extracting knowledge from data, mainly for business and research purposes. Artificial intelligence is the ability for computers to learn to perform tasks that usually require human intelligence.

Artificial intelligence (AI) and data science are two popular fields, but what do they have in common? In reality, these terms have very little to do with each other and can be used interchangeably. That said, both fields are concerned with the way we use data to make better decisions. They both use various techniques to analyze sets of data to see if any correlations can be found between them. Data scientists use the findings from their analyses to decide which fields they want to explore further. This is where the two fields diverge. Artificial intelligence is the field of study dedicated to making computers do what they’re programmed to do — think. Data science is the field of study dedicated to making humans do what they’re programmed to do better.

5. How do AI and data science differ?

In the last few years, AI has been all the buzz in the media. And while it can seem like this technology has been around forever, in actuality, it’s only been around for a relatively short period of time. The first AI program was designed by Arthur Samuel in 1959, and the term AI was coined in the 1960s. And while the idea of AI has been around for decades, the technology behind it is still relatively new. A lot of people don’t know the difference between AI and data science, and even fewer know the top differences between the two. In this blog, I’m going to go over the top five differences.

In today’s world, artificial intelligence (AI) is all the buzz. But what is artificial intelligence? Does it have something to do with data science? Artificial intelligence is the study of making computer systems that mimic the way that humans think and learn, while data science is the application of statistical models, data sets, and statistical software to help solve problems and make predictions. Artificial intelligence is generally used to make predictions or to help computers learn, while data science is used to solve problems, help businesses, and make predictions.

Photo by Alexas_Fotos on Unsplash


Is it AI or data science? Both are helping businesses to make better decisions.

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New film by Calgary's Tank Standing Buffalo streams on HBO | CTV News – CTV News Calgary



A Calgary animator’s newest cartoon started streaming Thursday night on HBO Max.

Tank Standing Buffalo’s MONSTR was one of eight animated shorts chosen from more than 1,200 submissions to be part of the HBO Max series Only You: An Animated Shorts Collection.

MONSTR deals with Standing Buffalo’s fight with inner demons while apprenticing with a northwest totem carver following the death of his wife Marsha.


“My partner Marsha died suddenly in my arms of a brain aneurysm,” Standing Buffalo said in a release.  “One moment she was there, the next she was gone. Without her, I was lost.

“I left Calgary to walk the west coast until I couldn’t walk, and ended up on carver Phil Ashbee’s doorstep. He saw I was in trouble, and took me in. I began a tough year-and -a-half apprenticeship, learning from him and another carver. The teachings were harsh, but helped me to heal.

Tank Standing Buffalo’s next project MONSTR is part of an HBO Max program for animators

MONSTR takes place during my time with Phil, and brings to life how I confronted the grief of Marsha’s passing. It is my story, one only I can tell.”

Standing Buffalo worked with co-writer Xstine Coo, producer Amanda Miller and composers Cara Adu-Darko and Brandon Smith on the film, which features music by Walter MacDonald White Bear.

The film features the voices of Corey Feldman and Tristan Risk.

It’s Standing Buffalo’s third animated short, following RKLSS (2020), which screened at TIFF, and SAVJ (2021), which is currently being screened at a variety of film festivals.

HBO flew Standing Buffalo to Los Angeles for the Hollywood premiere of MONSTR Tuesday night.

Scene from MONSTR by Tank Standing Buffalo

 In his artist statement, Standing Buffalo said art has literally saved his life – and his emergence as a rising animation star was launched by a scholarship he received to attend a Calgary animation workshop.

“I came to love animation six years ago when I received a scholarship through Quickdraw Animation Society in Calgary,” he said. “I am a person who thrives on routine and discipline. I appreciate the meditative repetition required to create animation.

“Through making my first two autobiographical shorts with monster and fantasy elements, I’ve found telling my story through animation is a form of time travel; my art is healing the person who I was in the past.”

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Databricks pushes open-source chatbot as cheaper ChatGPT alternative




March 24 (Reuters) – Databricks, a San Francisco-based startup last valued at $38 billion, on Friday released open-source code that it said companies could use to create their own chatbots along the lines of OpenAI’s ChatGPT.

The code is an AI model, an algorithm that is trained on sets of data and can then learn from new data to perform a variety of tasks.

Databricks CEO Ali Ghodsi said the release was aimed at demonstrating a viable alternative to training a kind of AI model called a large language model with enormous resources and computing power.


A large language model underpins OpenAI’s viral chatbot ChatGPT. OpenAI, valued at $29 billion, trains its AI models with huge troves of data on a supercomputer from investor Microsoft Corp (MSFT.O). The computing costs are “eye-watering”, OpenAI CEO Sam Altman has said.

OpenAI charges business for access to its models for their own applications and has projected $1 billion in sales by 2024.

Databricks’ effort comes with caveats. Ghodsi told Reuters that, while the open-source chatbot displayed impressive capabilities at such tasks as drafting blog posts, the company had not released formal benchmark tests to show that the bot matched ChatGPT’s performance.

Databricks sells cloud-based data mining and analytics software to businesses and said last year it had surpassed $1 billion in annualized revenue.

Databricks wants enterprises to train their own AI models using its software. Ghodsi said the company’s researchers had taken a two-year-old model that was freely available and trained it with a small amount of data for three hours on single computer that anyone with a credit card could rent.

“The future will be that everyone has their own model, and they can actually train it, and they can make it better,” Ghodsi said. “And that way, they also don’t have to give away their data to someone else.”

Databricks’ move comes at a time when startups are raising millions of dollars of venture capital investment to train their AI models and as big tech firms such as Alphabet’s Google (GOOGL.O) and Meta Platforms (META.O) rush to shrink the size and cost of AI models while improving their accuracy.

“My belief is that in the end, you will make these models smaller, smaller and smaller, and they will be open-sourced,” Ghodsi said. “Everyone will have them.”

Reporting by Krystal Hu in New York and Stephen Nellis in San Francisco; Editing by Bradley Perrett

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British regulator softens stance on Microsoft-Activision deal competition concerns



An Activision Blizzard’s Call of Duty: Modern Warfare video game is inserted into the Microsoft’s Xbox One video game console arranged in Denver, Colorado, on Wednesday, Jan. 19, 2022.

Shares of Activision Blizzard surged Friday, after the U.K.’s Competition and Markets Authority narrowed the scope of its investigation into Microsoft‘s takeover of the games publisher.

The development marks a partial win for Microsoft, as it pursues an expansion of its video game business. The Redmond, Washington-based technology giant has deepened its focus on gaming through blockbuster acquisitions, such as its purchase of ZeniMax Media, the parent company of Bethesda Softworks.

In February, the CMA published provisional findings from its probe into the takeover, stating at the time that the transaction may result in higher prices, fewer choices and less innovation. Among its concerns, the regulator flagged that the deal would cause a substantial lessening of competition in the console gaming market.

Since then, the regulator has received a “significant amount” of feedback from various industry participants on the deal. With this new evidence, the CMA now says it no longer believes the transaction will hamper competition in console games.


“Having considered the additional evidence provided, we have now provisionally concluded that the merger will not result in a substantial lessening of competition in console gaming services because the cost to Microsoft of withholding Call of Duty from PlayStation would outweigh any gains from taking such action,” Martin Coleman, chair of the independent panel of experts conducting the CMA investigation, said in a statement Friday.

“Our provisional view that this deal raises concerns in the cloud gaming market is not affected by today’s announcement. Our investigation remains on course for completion by the end of April.”

Shares of Activision Blizzard surged more than 6% in U.S. premarket trading. Microsoft shares declined slightly amid a broad market slump.

‘Call of Duty’ distribution in focus

The CMA announcement comes after the U.S. technology giant has also won support from some companies that were against the deal, or sitting on the fence.

One of the major concerns from Microsoft’s competitors was that the transaction would block distribution access to Activision’s crown jewel franchise — “Call of Duty.” Last month, Microsoft said it signed a “binding 10-year legal agreement” to bring Call of Duty to Nintendo players on the same day as Microsoft’s Xbox, “with full feature and content parity.”

Additionally, Microsoft signed a deal with Nvidia to bring its Xbox games to Nvidia’s GeForce Now cloud gaming service. Microsoft said it would also bring the Activision games library to Nvidia’s service, if the acquisition closes. Nvidia was reportedly against Microsoft’s Activision takeover.

But Microsoft has yet to bring onside its biggest rival, Sony, which owns the PlayStation console. Microsoft President Brad Smith told CNBC last month that the company is offering Sony the same agreement as it did Nintendo — to make Call of Duty available on PlayStation at the same time as on Xbox, with the same features. Sony still opposes the deal.

“We appreciate the CMA’s rigorous and thorough evaluation of the evidence and welcome its updated provisional findings,” a Microsoft spokesperson told CNBC via email.

“This deal will provide more players with more choice in how they play Call of Duty and their favorite games. We look forward to working with the CMA to resolve any outstanding concerns.”

An Activision spokesperson told CNBC that the CMA’s updated provisional findings “show an improved understanding of the console gaming market and demonstrate a commitment to supporting players and competition.”

“Sony’s campaign to protect its dominance by blocking our merger can’t overcome the facts, and Microsoft has already presented effective and enforceable remedies to address each of the CMA’s remaining concerns. We know this deal will benefit competition, innovation, and consumers in the UK.”

Microsoft is not completely off the hook.

The CMA says it still has reservations about the deal as it pertains to cloud gaming, where delivery of games content is handled from remote servers rather than from a device’s internal memory. Notably, cloud gaming is still in its infancy and not yet a mass-market technology.

In its provisional conclusions, the CMA suggested that Microsoft may need to divest part or all of Activision — or its CoD franchise alone — to resolve its concerns. The CMA did not provide an update as to whether it believes this remains a potential resolution.

The watchdog will make its final decision on April 26.

Microsoft also still faces uncertainty from regulators in the U.S. and European Union. Smith travelled to Brussels last month to meet with EU regulators. In the U.S., the Federal Trade Commission filed an antitrust case against Microsoft attempting to block the Activision deal.

Some major companies retain reservations about the acquisition, which includes Google parent Alphabet, according to Bloomberg.

CNBC’s Steve Kovach contributed to this report



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