Showing posts with label it industry. Show all posts

Friday, August 28, 2020

Combination Of AI & Blockchain - The Revolution Of Financial Industry (Part 1)

By Ngân Đoàn 3:45 AM Artificial Intelligence, blockchain, finance, it industry, Machine Learning

The revolution in ICT has affected finance industry at an increasing rate for decades. The financial firms are trying to cope with these changes by adopting new kinds of technology to deal with the increasing demand and the huge increase in the competition worldwide in the financial sector.

While so many industries are beginning to embrace the amazing options these technologies – Artificial Intelligence and Blockchain technology offer – helping them to create more value, boosting sales, and so on, it’s interesting to know that the combination of both will positively revolutionize the future of the fintech industry.

The convergence of Artificial Intelligence and Blockchain brings a better future for the Financial Industry.

With the superb benefits of Blockchain and AI, this combination will yield bewildering results and take an entirely new level across all industries especially with the financial sector. Here are amazing features and underlying capacities that come to play as below:

Blockchain — backbone of AI infrastructure | by Jura Protocol Media | Medium

1. A Secured Payment Network

One of the underlying benefits of blockchain technology is its unique ability to operate as a borderless payment network. Blockchain facilitates a frictionless payment with low prices and at a faster pace compared to the traditional methods.

Unfortunately, there are still some existing security concerns affecting its widespread adoption. Theft is increasing and getting more sophisticated daily since it requires a set of public and private keys to run a blockchain business.

By leveraging the power of AI and ML, any improper activities on your blockchain account can be identified, thereby alerting the account owner about some human involvement. Moreover, AI can analyze the behavior and biometrics to help eliminate any security susceptibility in payment networks.

2. Controlled Automation for Financial Activities

There is no doubt that finance is moving towards automation. On the other hand, there are pending issues that could pose threats to the finance sector if ‘calculated restrictions’ aren’t placed on automated processes. Meanwhile, to run a smooth automation financial system, it must occur synchronically with built-in checks and balances. In this case, smart contracts facilitate automatic processes, while AI and ML can look for irregularities simultaneously. Therefore, merging blockchain-powered smart contracts with AI can help verify the smart contracts and predict its vulnerability that can be exploited by theft. The resulting infrastructure would facilitate a completely secure, transparent, and efficient financial transaction.

3. An Efficient Financial Service

About 57% of businesses said that cost-saving is the primary benefit of participating in a consortia blockchain network, according to the 2019 Deloitte’s Global Blockchain Survey. The main reason why financial companies implementing both AI and Blockchain solutions is to improve their service speed and service quality. The result of this combination will help businesses minimize costs, maximize profits while still providing excellent value to customers.

Source: DZone

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.

Visit us at https://www.tmasolutions.com/

Machine Learning is Fun! (Part 2)

By Ngân Đoàn 9:29 PM AI, Artificial Intelligence, it industry, Machine Learning

That’s cool, but does being able to estimate the price of a house really count as “learning”?

As a human, your brain can approach most any situation and learn how to deal with that situation without any explicit instructions. If you sell houses for a long time, you will instinctively have a “feel” for the right price for a house, the best way to market that house, the kind of client who would be interested, etc. The goal of Strong AI research is to be able to replicate this ability with computers.

But current machine learning algorithms aren’t that good yet — they only work when focused a very specific, limited problem. Maybe a better definition for “learning” in this case is “figuring out an equation to solve a specific problem based on some example data”.

Unfortunately “Machine Figuring out an equation to solve a specific problem based on some example data” isn’t really a great name. So we ended up with “Machine Learning” instead.

Of course if you are reading this 50 years in the future and we’ve figured out the algorithm for Strong AI, then this whole post will all seem a little quaint. Maybe stop reading and go tell your robot servant to go make you a sandwich, future human.

Let’s write that program!

So, how would you write the program to estimate the value of a house like in our example above? Think about it for a second before you read further.

If you didn’t know anything about machine learning, you’d probably try to write out some basic rules for estimating the price of a house like this:

def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0 # In my area, the average house costs $200 per sqft
price_per_sqft = 200 if neighborhood == "hipsterton":
   # but some areas cost a bit more
    price_per_sqft = 400 elif neighborhood == "skid row":
   # and some areas cost less
    price_per_sqft = 100 # start with a base price estimate based on how big the place is
price = price_per_sqft * sqft # now adjust our estimate based on the number of bedrooms
if num_of_bedrooms == 0:
    # Studio apartments are cheap
    price = price — 20000
else:
   # places with more bedrooms are usually
    # more valuable
    price = price + (num_of_bedrooms * 1000) return price

If you fiddle with this for hours and hours, you might end up with something that sort of works. But your program will never be perfect and it will be hard to maintain as prices change.

Wouldn’t it be better if the computer could just figure out how to implement this function for you? Who cares what exactly the function does as long is it returns the correct number:

def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = <computer, plz do some math for me> return price

One way to think about this problem is that the price is a delicious stew and the ingredients are the number of bedrooms, the square footage and the neighborhood. If you could just figure out how much each ingredient impacts the final price, maybe there’s an exact ratio of ingredients to stir in to make the final price.

That would reduce your original function (with all those crazy if’s and else’s) down to something really simple like this:

def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0 # a little pinch of this
price += num_of_bedrooms * .841231951398213 # and a big pinch of that
price += sqft * 1231.1231231 # maybe a handful of this
price += neighborhood * 2.3242341421 # and finally, just a little extra salt for good measure
price += 201.23432095 return price

Notice the magic numbers in bold — .841231951398213, 1231.1231231, 2.3242341421, and 201.23432095. These are our weights. If we could just figure out the perfect weights to use that work for every house, our function could predict house prices!

A dumb way to figure out the best weights would be something like this:

Step 1:

Start with each weight set to 1.0:

def estimate_house_sales_price(num_of_bedrooms, sqft, neighborhood):
price = 0 # a little pinch of this
price += num_of_bedrooms * 1.0 # and a big pinch of that
price += sqft * 1.0 # maybe a handful of this
price += neighborhood * 1.0 # and finally, just a little extra salt for good measure
price += 1.0 return price

Step 2:

Run every house you know about through your function and see how far off the function is at guessing the correct price for each house:

Use your function to predict a price for each house.

For example, if the first house really sold for $250,000, but your function guessed it sold for $178,000, you are off by $72,000 for that single house.

Now add up the squared amount you are off for each house you have in your data set. Let’s say that you had 500 home sales in your data set and the square of how much your function was off for each house was a grand total of $86,123,373. That’s how “wrong” your function currently is.

Now, take that sum total and divide it by 500 to get an average of how far off you are for each house. Call this average error amount the cost of your function.

If you could get this cost to be zero by playing with the weights, your function would be perfect. It would mean that in every case, your function perfectly guessed the price of the house based on the input data. So that’s our goal — get this cost to be as low as possible by trying different weights.

Step 3:

Repeat Step 2 over and over with every single possible combination of weights. Whichever combination of weights makes the cost closest to zero is what you use. When you find the weights that work, you’ve solved the problem!

Source: Medium.com

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.

Visit us at https://www.tmasolutions.com/

3 Use Cases Of Artificial Intelligence In Business

By Ngân Đoàn 7:20 PM AI, ai/ml, Artificial Intelligence, innovation, it industry

With the rapid development of 4.0 Industrial Revolution, Artificial Intelligence was gradually applied in many fields from manufacturing, services, transportation to health and education. According to the study by Harvard Business Review, there are three main purposes for enterprises to apply AI: Robotic Process Automation, Cognitive Engagement and Cognitive Insight.

Robotic Process Automation (RPA) is the most common need when businesses use AI with low investment costs and ease of implementation. These applications can automatically update the customer information and profiles, communicate with customers through a chatbot and provide basic guidance on standardized information and documents. Those help businesses in management and customer care.

Cognitive Insight uses more complex algorithms incorporating machine learning to identify patterns from multiple data sets and extract meaning from them. The applications of Cognitive Insight are used in activities such as making prediction about purchasing options, detecting fraudulent transactions, automating advertisements in the direction of personalization, etc. Deep insights are accessed from user data, predicting customer behavior, and providing solutions with constantly updated and improved data. The goal is to improve the performance of tasks performed by machines that require a fast level of automation and massive data, beyond human capabilities.

Cognitive Engagement is a way of approaching customers by predicting analytics to provide insight into future customer behavior. Artificial Intelligence helps to provide maximum personalized content in interacting with customers quickly, right content, right audience and right time. As a result, businesses can formulate strategies and plans to increase interaction efficiency with customers and increase competitive advantage.

Depending on the specific needs and purposes, businesses can apply individually or combine the above applications for business activities in the most effective way.

Source: Toptal

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Machine Learning Is Fun!

By Ngân Đoàn 12:32 AM it industry, Machine Learning

What is machine learning?

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.

For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It’s the same algorithm but it’s fed different training data so it comes up with different classification logic.

This machine learning algorithm is a black box that can be re-used for lots of different classification problems

“Machine learning” is an umbrella term covering lots of these kinds of generic algorithms.

Two kinds of Machine Learning Algorithms

You can think of machine learning algorithms as falling into one of two main categories — supervised learning and unsupervised learning. The difference is simple but really important.

Supervised Learning

Let’s say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there’s a problem — you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don’t have your experience so they don’t know how to price their houses.

To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on its size, neighborhood, etc and what similar houses have sold for.

So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details — number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price:

Using that training data, we want to create a program that can estimate how much any other house in your area is worth:

This is called supervised learning. You knew how much each house sold for, so in other words, you knew the answer to the problem and could work backward from there to figure out the logic.

To build your app, you feed your training data about each house into your machine learning algorithm. The algorithm is trying to figure out what kind of math needs to be done to make the numbers work out.

This kind of like having the answer key to a math test with all the arithmetic symbols erased:

Oh no! A devious student erased the arithmetic symbols from the teacher’s answer key!

From this, can you figure out what kind of math problems was on the test? You know you are supposed to “do something” with the numbers on the left to get each answer on the right.

In supervised learning, you are letting the computer work out that relationship for you. And once you know what math was required to solve this specific set of problems, you could answer to any other problem of the same type!

Unsupervised Learning

Let’s go back to our original example with the real estate agent. What if you didn’t know the sale price for each house? Even if all you know is the size, location, etc of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning.

Even if you aren’t trying to predict an unknown number (like price), you can still do interesting things with machine learning

This is kind of like someone giving you a list of numbers on a sheet of paper and saying “I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”

So what could do with this data? For starters, you could have an algorithm that automatically identified different market segments in your data. Maybe you’d find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but homebuyers in the suburbs prefer 3-bedroom houses with lots of square footage. Knowing about these different kinds of customers could help direct your marketing efforts.

Another cool thing you could do is automatically identify any outlier houses that were way different than everything else. Maybe those outlier houses are giant mansions and you can focus your best sales people on those areas because they have bigger commissions.

Supervised learning is what we’ll focus on for the rest of this post, but that’s not because unsupervised learning is any less useful or interesting. In fact, unsupervised learning is becoming increasingly important as the algorithms get better because it can be used without having to label the data with the correct answer.

Side note: There are lots of other types of machine learning algorithms. But this is a pretty good place to start.

Source: Medium.com

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Vietnam’s technology skills top two in Asia Pacific

By Phương Nguyễn 1:00 AM blockchain, innovation as a service, it industry, Software Development

Vietnamese technology skills second in Asia Pacific and 22nd in global, data from the Global Skills Index 2020 report.

In this report - which was published last week, the worldwide online learning platform Coursera benchmarked 10 industries for business and technology skills. Coursera said these are its most popular domains in terms of enrollments and "encapsulate the skills most crucial to the future of work."

Source: LifeSuccess

According to the report, Vietnam’s strongest skill in the technology domain is operating systems (specifically Android and iOS software development), followed by computer networking (blockchain and wireless networking), human-computer interaction (user interface and machine translation), security engineering (cyber attacks and cryptography) and then software engineering (software development and algorithms).

In terms of the technology domain, Vietnam outranked Japan and Australia to finish in second place. The nation scooped 22nd place in the global ranking. Whereas in the business domain, Vietnam ranked seventh in Asia Pacific and 33th in the world.

Jeff Maggioncalda, CEO of Coursera, stated that in workforce recovery in a post-pandemic world depends on broad-base re-skilling. Educational institutions must lead this effort by proving learners with equal access to skills required for jobs of the future.

Coursera's data indicated every skill proficiency percent gained in a country’s average proficiency (across domains) is associated with a $600 increase in per capita GDP.

Source: VnExpress

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers.
Visit us at https://www.tmasolutions.com/

3 Big Challenges for Artificial Intelligence

By Ngân Đoàn 12:15 AM AI, Artificial Intelligence, it industry, vietnam ai

With the fast-growing of digital technology, the term “Artificial Intelligence” has become more popular, along with the applications of AI in modernizing life. While the potential for development of AI is immense, nothing is 100 percent accurate. AI still has the probability of making mistakes even though humans are always trying to improve and complete. One reason that is indisputable is that: There are always unpredictable definitions, problems or variables that create barriers for people to apply intelligence to teach and program machines.

Prejudice/ Partial: AI will be taught and learned by humans, it will have the intelligence and emotions we desire. But in the process of acquiring that knowledge, if humans interpersonal thoughts such as bias or personal prejudice about a certain subject then AI will certainly learn and dig deep to default that is right things.

Difficulty in acquiring data: If comparing AI as a human, the data will be their food source. Huge data sources are not necessarily good, it is important to identify and filter out what data is needed for AI to learn because excess or inaccurate data can lead to wrong conclusions in decision making.

High expectations: With the ability to learn and think deeply, people expect AI to have a high degree of accuracy in problem-solving. But AI is still just a machine, and machines are not 100% perfect because they learn from the data provided by humans, but humans were not perfect, nor could machines have the exact ratio. In some areas, especially medicine, while AI has a much lower probability of error than humans, most people will rely on human hands rather than machines.

The approach and becoming part of life have made AI become more and more important. However, nothing is perfect, not even humans. We can only try to reduce the probability of error to the lowest possible level.

Source: VNExpress

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Plugin Online Tracking

By Ngân Đoàn 3:39 AM innovation, it industry

COVID 19 promoted the process of digital transformation quickly and urgently. Education has also become one of the leading areas in demand of transformation. Not only during but also after the COVID 19 pandemic, many people probably realize the importance of online learning.

Will online learning really work without someone to supervise children? That is also the question of many parents when they observe the situation of "funny tricks" that their children behave during the online class. Many teachers have tried their best in order to maintain the quality of the lesson but it not really works.

Understanding the concern of parents as well as schools, TMA Innovation Center has created an online learning behavior monitoring plugin. This system will act as a supervisor and record the learning behavior of students. Any attitude of distraction, doing their own works, leaving the class, or even feeling sleepy, et cetera, will inform the teacher/lecturer in charge. Therefore, the quality and effectiveness of the lesson will be improved.

Source: VN AI Hub

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Vietnam accelerate cloud computing for digital transformation

By Phương Nguyễn 4:09 AM Cloud Applications, it industry, Software Development

The Ministry of Information and Communication launched a campaign to promote digital transformation using cloud computing.

With regards to the post-coronavirus period, Vietnam is steadily recovering its economy, experts say digital transformation will aid enterprises adopt a more efficient and cost-effective business model.

Cloud computing is regarded as one of the most fundamental parts of this process, the demand for this has been increasing in the past few years.

Added to that, Minister Nguyen Manh Hung expressed that domestics enterprises have aced telecommunications infrastructure and a similar accomplishment must be achieved to further refine such infrastructure.

He focused great emphasis on the critical role of cloud infrastructure development, given the idea that digital infrastructure is basically the combination of telecommunications infrastructure and cloud computing.

Vietnam is one of the pioneer countries to have adopted a set of criteria and technical specifications for cloud computing services and it is expected that many more Vietnamese cloud providers will reach such standards in the future.

Source: Nhan Dan

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Artificial Intelligence helps to identify human preferences

By Ngân Đoàn 4:02 AM AI, Artificial Intelligence, it industry

Applications of Artificial Intelligence have been used widely in various activities but the potential has been not still discovered yet. Recently, the researchers of a university located in Finland which specialized in science have developed a technique by analyzing people’s electroencephalograms (EEGs) with the help of AI techniques. This technique is called “brainsourcing”, which can clarify the image or content, analyze options, and conclude using brain activities that we have not demonstrated before.

It works based on the method that breaks up a complex task into smaller tasks to learn individual recognition. Every response is used to create a large instructional data for the system. With more samples of the brain activity, the AI algorithm learned to recognize the relevant contents or images and the result will be more accurate.

The technology used by the researchers still needs improvements to capture more brain activity and preference information directly from the brain.

Source: ScienceDaily

About us: TMA Solutions was established in 1997 to provide quality software outsourcing services to leading companies worldwide. We are one of the largest software outsourcing companies in Vietnam with 2,500 engineers
Visit us at https://www.tmasolutions.com/