Artificial Intelligence Systems Offer Knowledge on Human Abstract Thinking

Since the discovery of computer systems, researchers and scientists have strived to improve the systems in an attempt to compete with the human brain function. The development of these various systems has been aimed at coming up with better functional systems that could beat humans at games and other functions.

A philosopher from the University of Houston has taken up a completely different approach. Instead of studying how artificial intelligence competes with human brain function, he is looking at how humans process abstract learning by deconstructing machine learnings’ complex neural networks.

By learning how the machine learning systems work Cameron Bucker aimed to get a better understanding of how the human brain learns. His goal is to settle a decade’s long argument on whether human learning is innate or it stems from experience. In his paper that was published in the Synthese, Buckner concludes that Deep Convolutional Neural Networks show that human learning is purely based on experience. This supports the philosophical school of thought, empiricism.

The DCNNs showcases a multilayered artificial neural network that contains nodes responsible for passing along information in the brain. This is a demonstration of how abstract knowledge is acquired in the brain.

Various scientists have concentrated on the results of the neural networks rather than on how these neural networks function. Though most scientists have shadowed the thoughts of John Locke and Aristotle, who were empiricists, they have avoided finding out the “why and how” of artificial intelligence.

Buckner has however been able to decipher how abstract thinking comes about. With the advancement of artificial intelligence systems, it has become necessary to understand how abstract thinking comes about. As machines can now do what comes to humans naturally the only way to stay on top is to learn how both the machines and humans come about abstract thoughts.

 

References

https://www.sciencedaily.com/releases/2018/10/181009115022.htm

 

 

Graphene Heterostructures Creating Spintronic Devices

Spin-orbit coupling lies deep within spintronics. At room temperature, to achieve longer spin coherence length, certain factors must come into play. The fact that Graphene has high electron mobility and spin-orbit coupling enable it to accomplish the tasks. Heterostructures create by graphene as well as topological insulators induced a suppression and strong tenability of the spin signal as well as the spin lifetime.

According to the Graphene Flagship Researchers from ICREA InstitucióCatalana de RecercaiEstudisAvançats (Spain), UniversitatAutònoma de Barcelona (Spain), Catalan Institute of Nanoscience and Nanotechnology — ICN2 (Spain), and Chalmers University of Technology (Sweden) graphene can have different spintronic applications that range from novel circuits, info processing tech and non-volatile memories.

According to Saroj Prasad Dash, a Professor from the Chalmers University of Technology; graphene in close vicinity with topological insulators will support spin transport while acquiring a sturdy spin-orbit coupling. That reaction causes an advantage in using Dirac materials in building heterostructures. Another professor from ICN2, Stephan Roche who is also the Graphene Flagship’s spintronics deputy leader stated that they aim to manipulate rather than just transporting the spin.

The Graphene Flagship group foresaw the potential within graphene made spintronics devices. It shades new light as to how potential applications and new possibilities arise from combining materials with graphene to create heterostructures. Professor Roche adds that Graphene Flagship’s Spintronics Work-package finds its strength from combining experiments and theories. The Chair of the Graphene Flagship Management Panel and professor in Science and Technology, Andrea C. Ferrari says that the research gets them a step closer to creating handy spintronic devices.

References

https://www.sciencedaily.com/releases/2018/10/181016110102.htm

 

 

Hi-tech wearables, Personal Data: Blood Pressure Prediction Made Possible

The issue of fluctuating blood pressure has been a significant concern for some time now. Though normal fluctuations pose no threat, the extreme highs and lows expose one to severe health risks such as stroke, heart failure, and dementia among others.

The issue should no longer be cause for worry as UC San Diego engineers have provided a solution: Using a combination of machine learning and top-notch wearable technology, these engineers have made it possible to predict a person’s blood pressure. Better still, the system offers customized recommendations to stabilize the pressure centered on personal data.

According to the researchers, it is the first time anyone investigates daily blood pressure fluctuation and its association with health behavior data collected by smart wearables. Their work merited the Best Paper title at the IEEE Healthcom 2018.

Patients seldom take heed to their doctor’s advice to improve their lifestyles by sleeping better, reducing salt intake and exercise more. This information is often overwhelming and therefore compliance is low. According to SujitDey, director of the Engineering School’s Center for Wireless Communication and co-author of the paper, using personal data makes it possible to pinpoint the significant cause for the condition and have the patient focus on it.

Using an Omron Evolv wireless BP display and a FitBit Charge HR, the researchers collected exercise, sleep, and blood pressure data from 8 candidates for more than 90 days. They then utilized machine learning and the information on the wearable gadgets to develop an algorithm that predicts the blood pressure of the user and pinpoints the specific health behaviors that caused it most.

The study confirmed the significance of using personalized information as opposed to generalized data. Most health databanks comprise one model that contains large amounts of data from different patients; when making suggestions, all this information is considered. However, the study showed that using personalized data is the better approach.

Reference

https://www.sciencedaily.com/releases/2018/10/181004192207.htm

 

 

 

 

New Automated Models to Reduce False Positives in Credit Card Fraud Detection

False credit card fraud detection has been predicted to cost banks around $118 billion in lost revenue alone. Out of every five flagged fraud detections, only one is usually legitimate. Shopping tendencies vary from shopper to shopper with influences from outside factors that contribute to what a specific shopper would buy at a specific time. It is, therefore, hard to flag down fraud cases that are indeed fraud and not just customers changing their patterns.

New techniques have been developed to reduce the number of false positives in fraud detection that are not legitimate. Prior machines are trained to note behavioral patterns that are called features. Any variation to these features flags the system and is considered as a fraud by the machine. The machine is designed to be aware of fraud features which make it be flagged if any features match.

The new model of technology is designed in a way that for each individual transaction two hundred features are extracted. These features are then used to determine whether the new transaction is a fraud or legitimate. The increased features are designed in a way that it reduces the likelihood of false detections by 54%.

Since the biggest challenge in the credit card industry is false positives this new system is the best solution. Feature engineering to increase the number of behavioral patterns that are specific for a certain individual purchase is directly connected to the reduction of false positives.

 

References

http://news.mit.edu/2018/machine-learning-financial-credit-card-fraud-0920

 

Wheeler graphs: A BWT framework for BWT-based data structures

Burrows-Wheeler Transform (BWT)  used to be defined as one string, however, set of strings variation have been developed currently referred to as de Bruijn graphs, trees,  etc.  Wheeler graphs contain a property called path coherence. If a wheeler graph is in a finite diagram, the path becomes coherence. The nodes can be ordered such that, the reachable nodes from the prior state are consecutive. In other simple terms, despite the automaton being non-deterministic, compactly storage is still feasible, and the string processing can be quick.

Several BWT variations can be rederivedBy creating automata that are straightforward finite for the specific problems and portraying they have wheeler graph state diagrams.

BWT is a transformation that is reversible and is based on many text compressors having tools that are used in computational biology and Bioinformatics. It is not a compressor; rather it is context dependent transformation letter permutation of text input which builds runs. These runs are equal letters or clusters that have longer text as compared to the original one. This BWT plethora is normally called “clustering effect.”  From a particular combinatorial view point, huge concerns have been allocated to scenarios where BWT releases clusters that are few in numbers.

The general knowledge is that when you apply BWT at worst on any word, same letter runs doubles. In fact, there is a tight bound when upper bounds are reached. Further, binary words  BWT produces maximal cluster numbers that are connected to the primitive roots of Artin’s conjecture.

References

https://books.google.co.ke/books?id=M75sDwAAQBAJ&pg=PA16&lpg=PA16&dq=Wheeler+graphs:+A+framework+for+BWT-based+data+structures&source=bl&ots=leu-lmFAZo&sig=inV3QcFDZoA0zyWjRcsV2VpIJjM&hl=en&sa=X&ved=2ahUKEwiZ3fKoxOTdAhUIPo8KHYi_CqoQ6AEwA3oECAMQAQ#v=onepage&q=Wheeler%20graphs%3A%20A%20framework%20for%20BWT-based%20data%20structures&f=false

 

 

Technology and Future Employment

For a long time, the concept of artificial intelligence was relegated to science fiction books and movies. However, the machines being made and used these days are proof that this concept is not fictional.

Scientists have developed devices that can do the same jobs as humans, tirelessly, and in some cases with even more accuracy. Various tech companies have been venturing into the field of AI, and Google is not being left behind as evidenced by their purchase of the UK startup DeepMind.

The concept of smart machines is something that fascinates and terrifies in people in equal measures. The main problem is that the technology currently being developed will make a significant portion of the human workforce redundant. These are opinions harbored by most people and even elites like Dr. Stuart Armstrong.

On the other hand, some people are a bit unsure whether the advances in artificial intelligence should be alarming. This is because according to Dr. Murray Shanahan, from past experiences although technology has led to the scraping of some job positions it has also created other jobs.

Fields that have repetitive tasks, for example, insurance underwriting, telemarketing, data entry, manufacturing, and basic accounting, are some of the areas where AI technology was initially used. However, now scientists are developing complex algorithms to take on a broader range of jobs. This means that the jobs that required human interaction might also not be the safest bet anymore.

It is important to recognize that the management of Google for establishing an ethics committee on the purchase of Deepmind. This team of experts was tasked with finding ways to use the new technology while causing minimal damage. This is an action few tech companies have taken before, and it goes to show that while Google is willing to invest in new technology, they are not ‘persecuting’ the human workforce.

Reference

https://www.independent.co.uk/life-style/gadgets-and-tech/advances-in-artificial-intelligence-could-lead-to-mass-unemployment-warn-experts-9094017.html

 

 

 

Technical Skill and Creativity Mesh Well

An applications software developer is another area of computer science that has seen a phenomenal amount of interest. If you have ever wondered who is responsible for the variety of applications and programs available on the market today, application software developers are the answer. Although technical in nature, the work requires a degree of creativity. It is the professionals in this field that design, develop and build programs and applications that are used on all our technological devices. Whether you are playing a game on your phone or using a word program to complete a task, the application or program is the work of an application software developer.

https://www.amplify.com/viewpoints/teach-computer-science-start-creativity

The Diversity Of Computer Science

Computer science is a diverse and broad field. There are so many opportunities to find what you love to do and run with it. Web development is one area of computer science that has gained popularity over the recent years. Contrary to the understanding of many, a web developer is not a graphic designer. While a graphic designer creates the images visible on the website, it is the job of the web developer to write the code that actually makes the site one that works. In most cases, the web developer incorporates and integrates the audio, video and graphics into the working site. It is their job to monitor the traffic and keep an eye on the performance and the capacity of the site.

 

https://www.internationalstudent.com/study-computer-science/what-is-computer-science/

Fun, Fast Facts About Computer Programming

There’s a lot of things you might not know about computer programming. For instance, Ada Lovelace was the first programmer, and she was a female. Not only that, but it was 1961 when the first game was produced, but it actually didn’t rake in any cash. Unfortunately, in 1983 someone or something created the first virus.

Furthermore, the first computer ever created actually didn’t use electricity. The computer was called Jacquard Loom. As for programming language, the first high quality one was Fortran, which was created by John Backus.

The last fact, is computer programming is an amazing career to get into, especially considering it is among the quickest growing careers.

Machine Learning Benefits For Sales And Marketing

Businesses that launch sales and marketing campaigns can benefit from machine learning. For example, machine learning can help make product marketing much more simple. It can even play a role in forecasting sales accurately. Not only that, but machine learning can consume data from various sources. In fact, the sources are virtually unlimited.

Another benefit, is that machine learning can be used to identify relevant data. This means businesses can take action at the perfect time. Besides that, past customer behaviors can be better interpreted thanks to machine learning.

You can expect more and more businesses to use machine learning to help them with sales and marketing.