BIG DATA WITH MAX WELLING: BIG DATA AND AI


Big Data and AI

Max Welling, Research chair in Machine Learning at the University of Amsterdam and VP technologies Qualcomm Netherlands, talks about about machine learning, big data, artificial intelligence, historical data, deep learning and algorithms.

Luned Birutė Mag Raith

Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue.

Ruslan Grześkiewicz

In response to Sonja Ham Mac Diarmada

A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the News Feed. Similarly, when Amazon recommends “You might also like” products, or when Netflix recommends a movie based on past behaviors, machine learning is at work. 

Machine learning is just a different perspective on statistics. Here are critical skills that can help you carve out a career in this fast-growing domain:

  • Expertise in computer fundamentals
  • In depth knowledge of programming skills
  • Knowledge of probability and statistics
  • Data modeling and evaluation skills
Sonja Ham Mac Diarmada

A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the News Feed. Similarly, when Amazon recommends “You might also like” products, or when Netflix recommends a movie based on past behaviors, machine learning is at work. 

Esperanta Tomàs

A data analyst should be able to take a specific question or a specific topic and discuss what the data looks like and represent that data to relevant stakeholders in the company.  If you’re looking to step into the role of a data analyst, you must gain these four key skills:

  • Knowledge of mathematical statistics
  • Fluent understanding of  R and Python
  • Data wrangling
  • Understand PIG/ HIVE
Konstantina Branković

For centuries, people have predicted that machines would make workers obsolete and increase unemployment, although the causes of unemployment are usually thought to be due to social policy.  A recent example of human replacement involves Taiwanese technology company Foxconn who, in July 2011, announced a three-year plan to replace workers with more robots. At present the company uses ten thousand robots but will increase them to a million robots over a three-year period. Lawyers have speculated that an increased prevalence of robots in the workplace could lead to the need to improve redundancy laws

Jacquette Rollins

Here are a few problems where AI methods can be applied: 

  • Optical character recognition
  • Handwriting recognition
  • Speech recognition
  • Face recognition

As for other fields, I think Robotics is the big one right now.  Automation and data mining are two other very hot fields at the moment.

Анета Владимирова

Hi all,

I am looking for a more detailed discussion of (1) typical problems to which AI methods are applied as well as (2) other fields in which AI methods are implemented.  Any posts will be greatly appreciated.

Rosemary Vandale

One type of AI system is the so called reactive machines.  An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves -- its own and its opponent -- and chooses the most strategic move. Deep Blue and Google's Alpha.GO were designed for narrow purposes and cannot easily be applied to another situation.

Aisha Kamila Kuhn

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

Kristiāna Olga

In response to Dan

I liked very much the example with the traffic light. The machine will see that the other cars stops on red light and will do the same. But what happens if a group of people purposely starts passing on red light and the machines start doing the same thing. Chaos :)

Yes but there can be both, you can set a certain number of rules that the AI has to obey and play around with the other variables in the environment.

inesa sokoll

The biggest problem with recovering from cyber-attacks is that security professionals rarely get the chance to deal with them immediately.  Since artificial intelligence doesn’t need to sleep, though, they can set defense systems against malware the moment it begins to download.

Developers understand artificial intelligence better than ever and how to manipulate its workings. For that reason, there’s no doubt that the future holds a bit of robot-human cooperation in defense of data everywhere.

Mariana Lichtenberg

How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. Instead, AI is used to create systems that learn what types of transactions are fraudulent.  FICO, the company that creates the well-known credit ratings used to determine creditworthiness, uses neural networks to predict fraudulent transactions. Factors that may affect the neural network’s final output include recent frequency of transactions, transaction size, and the kind of retailer involved.

Gunne Tor

In response to Ingegerd Poulsen

Tomorrow’s AI won’t live up to the hype. Freeing ordinary folks from repetitive tasks and giving them personal assistants only allows people to busy themselves with other, more complex tasks. The resulting productivity will mark incremental gains for business owners, but nothing on par with the digital revolution and the industrial one before it. For that, we’ll have to wait for the robots.

Yes, but the problem is some people are not suitable for more complex tasks. Not everyone can before a doctor, or a lawyer. How can we deal with this? 

Dan

I liked very much the example with the traffic light. The machine will see that the other cars stops on red light and will do the same. But what happens if a group of people purposely starts passing on red light and the machines start doing the same thing. Chaos :)

Ingegerd Poulsen

Tomorrow’s AI won’t live up to the hype. Freeing ordinary folks from repetitive tasks and giving them personal assistants only allows people to busy themselves with other, more complex tasks. The resulting productivity will mark incremental gains for business owners, but nothing on par with the digital revolution and the industrial one before it. For that, we’ll have to wait for the robots.

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