Topics

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Teaching & Research Forum / Important Artificial Intelligence Predictions (For 2019)

« on: January 15, 2019, 01:39:04 PM »

Artificial Intelligence – specifically machine learning and deep learning – was everywhere in 2018 and don’t expect the hype to die down over the next 12 months.

The hype will die eventually of course, and AI will become another consistent thread in the tapestry of our lives, just like the internet, electricity, and combustion did in days of yore.

But for at least the next year, and probably longer, expect astonishing breakthroughs as well as continued excitement and hyperbole from commentators.

This is because expectations of the changes to business and society which AI promises (or in some cases threatens) to bring about go beyond anything dreamed up during previous technological revolutions.

AI points towards a future where machines not only do all of the physical work, as they have done since the industrial revolution but also the “thinking” work – planning, strategizing and making decisions.

The jury’s still out on whether this will lead to a glorious utopia, with humans free to spend their lives following more meaningful pursuits, rather than on those which economic necessity dictates they dedicate their time, or to widespread unemployment and social unrest.

We probably won’t arrive at either of those outcomes in 2019, but it’s a topic which will continue to be hotly debated. In the meantime, here are five things that we can expect to happen:

• AI increasingly becomes a matter of international politics
• A Move Towards “Transparent AI”
• AI and automation drilling deeper into every business
• More jobs will be created by AI
• AI assistants will become truly useful

Source: https://www.forbes.com/sites/bernardmarr/2018/12/03/5-important-artificial-intelligence-predictions-for-2019-everyone-should-read/#30c89696319f

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Teaching & Research Forum / Machine Learning and Artificial Intelligence Trends in 2018

« on: September 09, 2018, 05:53:03 PM »

Artificial Intelligence (AI) and Machine Learning(ML) are jointly contributing to the gradual build-up of the “Intelligent Digital Mesh” so candidly described in Gartner’s Top 10 Strategic Technology Trends for 2018.

Artificial Intelligence and Machine Learning have been forecasted to be the game-changers of the coming decade. Most of the tech trends discussed really fall under the umbrella field of Data Science, as they directly or indirectly affect Data Management. However, some specific innovations like Augmented Reality (AR), Virtual Reality (VR), or Digital Twins also indicate hardware enhancements and overlapping of the physical and digital worlds.

In a blog post tiled Forrester’s Top Emerging Technologies To Watch: 2017-2021, Forrester made strong assertions about an impending disruptive technology world, where suddenly an avalanche of ground-breaking digital technologies such as mobile, social, Big Data, Cloud, IoT, Ai, and ML will all emerge and converge to turn the global businesses upside down.

In another Forrester’s post tiled The Top Technology Trends To Watch: 2016 To 2018, the author claims that Big Data and IoT have suddenly catapulted the need for Intelligent Analytics systems powered by robotic assistants and self-teaching algorithms. What this trend indicates is that the future business owners, leaders, and managers will have more time to invest on critical business issues as day-to-day Data Management tasks will be taken over by intelligent machines. The additional layer of ML-driven AI capabilities has taken the afore-mentioned technologies to the ultimate era of technological freedom, where man and machine co-habitate happily.

The transformative outcomes of such a “connected business world” are participative customer relationships, data-powered decision making, implementation of innovative risk and security strategies, and superfast technology maturity driven by global customer obsession with everything tech. The businesses, big or small should take advantage of this era of digital disruptions and empower their workforces and customers alike.

In the next five years, say by 2021, the fully automated, intelligent Data Management systems may actually end up chopping about 6 percent of US-based Data Science jobs. In 2021, we may even see a virtually connected, business ecosystem once again transforming the customer engagement platforms and methods.

Source: http://www.dataversity.net/machine-learning-artificial-intelligence-trends-2018/

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Teaching & Research Forum / How To Use Classification Machine Learning Algorithms in Weka

« on: August 26, 2018, 03:02:37 PM »

A useful article about using machine learning algorithm in weka:

https://machinelearningmastery.com/use-classification-machine-learning-algorithms-weka/

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Teaching & Research Forum / Top 10 Breakthrough Technologies For 2018

« on: July 12, 2018, 11:48:30 AM »

MIT Technology Review unveils its breakthrough technology list for 2018 – a rundown of 10 awe-inspiring scientific and technological advances that have the potential to change our lives in dramatic ways.

https://www.forbes.com/sites/bernardmarr/2018/02/21/here-are-the-top-10-breakthrough-technologies-for-2018/#68bc4e445d25

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Teaching & Research Forum / How to keep your interest while learning a new tool or technology?

« on: July 05, 2018, 12:24:23 AM »

Most often we start learning a new tool or technology with great interest. But as time passes by, we lose our interest in it. So, how should we approach to hold our interest in it?

• Firstly, we have to ask ourselves why we want to learn that technology and determine whether our basic foundation is up to the mark.
  
• Secondly, we must not take load more than what we are capable of. If we take too much load in a small amount of time, we will fail to keep pace in the long run. We should try to learn things in an efficient way.
  
• Thirdly, we must maintain a connection with a group that knows about that technology. This will help us find a solution when we get stuck at a problem.
  
• Fourthly, we should try to make something with the little things we are learning. This will increase our thirst to learn more.
  
• Fifthly, we must find a way to find interest by looking at others working with the same technology.
  
• Finally, we must be consistent in our learning.
  

All these ideas have been nicely presented by Hasin Hayder. Watch the full video for details.

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Teaching & Research Forum / What is machine learning?

« on: July 02, 2018, 02:29:50 PM »

What is machine learning? Machine learning is being used in a wide range of applications today. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

“Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  
• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
  

Machine learning platforms are among enterprise technology's most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.

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Allah: My belief / Proof that God exists

« on: May 24, 2018, 04:39:03 PM »

Does God really exist? Did God create the universe or did it just occur naturally? Before we look
at it, let us see what science says about the origin of the Universe. One of the most popular
theory is the Big Bang theory where it is believed that a massive blast allowed all the
universe's known matter and energy even space and time themselves to spring from some
ancient and unknown type of energy. But what existed before the big bang? Can we really
explain what causes something out of noting? Let us see what Quran says about the big bang.

“Have those who disbelieved not considered that the heavens and the earth were a joined
entity, and We separated them and made from water every living thing? Then will they not
believe?” (Quran 21: 30)

So, it is the Almighty Allah under whose instruction the entire universe was created. But who
created God? What existed before God? Before we answer the question, we must realize
that we cannot compare God, the Creator with the creations. Every creation has a beginning
but the characteristics of the ultimate Creator differs from His creations that makes him unique.
Allah says in the holy Quran:

“He neither begets nor is born. Nor is there to Him any equivalent.” (Quran 112:3-4)

We have to realize the fact that Allah existed when there was nothing. He created the
universe and placed mankind and other creations for His worship. There can be none
equivalent to Him.

The question that may arise, "can there be multiple Gods" Allah says about it in the Quran:

Say, “He is Allah, [who is] One” (Quran 112:1)

“Had there been within the heavens and earth gods besides Allah, they both would have
been ruined. So exalted is Allah, Lord of the Throne, above what they describe.” (Quran
21:22)

It is quite easy to understand that there is only one God under the supervision of whom the
entire universe is running in a planned manner.