From artificial intelligence to artificial neural networks, from machine learning to deep learning and from computer vision to statistical pattern recognition, the nearly endless array of artificial intelligence terminology and – some might argue – buzzwords, is nothing but the inevitable side-effect of a highly accelerated advancement of this field.
But these aren’t just silly buzzwords we use to sound cool (well, not always), and while to the average Joe, all these terms tend to sound alike, it’s simply because many of them have been used more or less interchangeably in the popular press. However, there are subtle and not so subtle differences, and in this article, I’ll untangle and demystify some of these concepts for you.
Machine learning is an umbrella term that covers all technologies in which a machine is able to “learn” on its own, without having that knowledge explicitly programmed into it. Most types of machine learning are iterative in nature and become better over time at whatever task they are being asked to perform.
To understand the difference between machine learning and other forms of artificial intelligence, consider two game-playing computers: IBM’s Deep Blue, which defeated world champion Garry Kasparov at chess, and Google’s AlphaGo, which defeated world-champion Go player Lee Sedol.
Deep Blue didn’t “learn” the best moves to make; instead, it performed a sophisticated search during every one of its moves, evaluating every possible move for the current board position, the possible moves after that, and so on. From this analysis, Deep Blue selected the one move most likely to result in victory. It then repeated the process at its next turn.
By contrast, AlphaGo learned the best moves by playing thousands of matches against other computerized Go players and other instances of itself. Its proficiency increased with experience.
Deep learning is a special case of machine learning and is a type of artificial neural network. Artificial neural networks attempt to mimic, on an extremely limited scale, the workings of a biological brain. An artificial neural network is a collection of software objects, or “neurons,” each of which produces an output value according to a formula that accounts for the number of input signals and the strength of each.
When an artificial neural network “learns,” it starts with a set of training data that a human has already annotated; for example, a set of images that are re-classified as “dog” or “not dog.” An artificial neural network identifies patterns in the images, and, without human intervention, tweaks the formulas of its neurons until it settles on a set of formulas that takes the input images and reliably outputs a “dog” or “not dog” value.
In a deep learning system, the neurons are organized into sets called layers: usually an input layer, an output layer, and one or more “hidden” layers.
Neurons in one layer typically are connected only to neurons in an adjacent layer; they don’t “skip” layers or communicate with other neurons in their own layer. However, depending on the design, signals can either travel in only one direction (input to output) or travel both forward and back.
Statistical Pattern Recognition
Statistical pattern recognition has more to do with the task a machine learning system is trying to accomplish. As with humans, one way that a machine “learns” is by recognizing patterns. These can be visual patterns in images, patterns in sound or other types of signals, or patterns in text, financial data, weather data, or anything else where every example of a thing is different but they all possess certain similarities.
A machine learning system can be configured to recognize patterns using sophisticated statistical analysis to classify the data objects (images, transactions, or what have you). The results might be in the form of a likelihood value or probability percentage, rather than a strict “yes” or “no.”
At first, this approach might result in many “false positives” and “false negatives,” but over time and with additional training, those percentages should increase.
As might be expected, a human performs pattern recognition much more quickly and efficiently than a computer. Show a child three pictures of different dogs and she will immediately and reliably be able to classify pictures she has never seen before as “dog” or “not dog.”
Computers achieve this level of proficiency only after examining thousands of pictures, taking a stupendous amount of time and computing power to do so.
Machine learning and all of its types and variations are still in their infancy, and can only tackle very narrowly defined tasks. Yet, given the tremendous strides made in recent years, it is only a matter of time before practical machine learning systems are available for the average PC, laptop, or smartphone devices.
Choosing the Right Functional Testing Tool
Since technology keeps evolving from time to time, organizations need to keep up with the pace of technological changes. Previously, companies released software apps on a monthly or quarterly basis. To keep up with the current market competition, organizations that have faster delivery cycles tend to be more successful than others. Now companies are expected to deliver software products.
This rapid change in trends has both positive and negative impacts. Organizations need to be more efficient and maintain quality while ensuring quick releases. This cannot be done without research and using the right software testing tools. With a huge variety of tools available in the market, making decisions is quite complex. However, asking relevant questions can help organizations choose the best tools for various phases of a project.
Major issues organizations face when selecting tools for their testing activities is to figure out if the tools actually cover their testing requirements or not. Companies often change testing tools based on their budget constraints or if a new manager has used them previously. These factors should not affect the decision as to which testing approach is the best. So, when organizations look around for a good functional testing company, the following considerations should be made:
- Does the Tool Deal with Relevant Defects?
One of the top most asked questions testers ask when looking for a functional testing tool is, if it can deal with the relevant defects. Testers review the recent defects, detected in test and production. And then check if the tool can catch those types of defects. If the answer is negative, they might have to look for other options. This step can either be performed before tool selection or later.
- Team Selection
QA managers decide which team members will be doing the automating. If automation is to be done by developers or both developers and software testers, then they should have a complete tool package. If the tool has a record/playback feature, it will be more appropriate for the non-technical testers. Additionally, the testers assigned with specific tasks and tools should have experience using the tool otherwise it will slow down the software delivery process.
- Features and Test-Data Management
Testers looking for the right tools have to check for features like account creation, clear orders, export accounts, and associated orders and re-import it. It allows easy tests setup. In addition, its ability to create test servers on-demand according to requirements. Teams that work on Continuous Integration approach need end-to-end checking features to create this environment. Thus, testers check all the features and see if the tool follows a test-data management approach.
A testing tool can never be useful if it does not achieve its purpose. A good functional tool provides essential features like dashboard and charts. Tracking the test is another essential feature of a testing tool that helps achieve different expected results. Thus, this is a feature tester cannot ignore.
The afore-mentioned factors should be considered to get the best functional testing tools for your project.
Better Consumer Experience In Lending Assured By Innovative Technology
When it comes to online shopping, consumers look for the best experience. Thanks to the advances in digital technology more and more online platforms for different products and services are now focused on bringing a change in their customer acquisition methods to ensure better user experience.
Practically, you will see that almost every sector of the economy is using technology to the fullest to make their service the best. Among these, the automobile and lending industry seems to be the forerunners. They are always ready to make a few sweeping updates, especially in their sales approach and service processes.
With the use of new tools and technology, several financial companies have started to use the most sophisticated tools and technology available to make their online user platforms more and more attractive, useful and easy to navigate. Tools and techniques such as machine learning and AI have made it incredibly easy for the money lenders to:
- Guide their customers through the entire loan origination process
- Discover all other additional opportunities provided by them
- Use the available data from the loan applicants to customize each loan product and financial services to meet the specific needs of their customers.
This has made the process faster, better and more secure helping both the borrowers and the money lenders.
Better for the entire industry
The finance industry is wide and extensive and in fact, is a huge umbrella that covers almost all sectors of business. With such a huge area of operation and such a diverse range of customers with an equally diverse range of wants and demands to cater to, manual operation is unthinkable in the present time. Use of financial tools and technology has helped the entire finance industry and the statistics of each proves this fact significantly.
- In the mortgage sector of finance industry it was seen that within the third quarter of 2017 the total outstanding mortgage debt for residences meant for one to four family members in the US exceeded $10.5 trillion.
- It is also found that it will continue to rise even more despite the fact that the industry is yet to recover all of the lost ground in the last decade due to the financial crisis that led to significant loss in revenue and business profits of the finance industry as reveled through different experts like libertylending and research reports.
- In addition to that it is found that the home prices as well as the volume of loan originations are continually rising upwards in the past several years. This is in spite of the array of technical problems that the mortgage lenders usually face in delivering their services.
These problems are typical and coexistent in all segments of the finance industry. This is because of the complexity in the process, the time required to process a loan and the lack of available data at a given point of time. This plagues the lenders as well as the borrowers alike throughout the loan origination process.
According to facts and findings published by the Federal Housing Finance Authority it is revealed that:
- 18% of all home loan applicants are compelled to redo their paperwork and
- Nearly 24% of the applicants found that their closing dates were postponed.
The report also notes that the borrower satisfaction level with the closing process is usually high. However, the numbers indicate that this level of satisfaction is found to be low among the younger applicants.
Usability And Convenience
Now, the question that may arise is why this younger generation seems to get a low satisfaction and inferior user experience? This is because they want more convenience in the process and are more and more tech-conscious. They tend to judge a service or an online platform on the specific metrics of usability and convenience.
With the rise in demand in real estate and the prospects of lending in this specific sector such as the ever rising interest rates, more and more fintech companies are entering into the real estate market. This will ensure that the users, irrespective of their age will find more convenience when they borrow money for whatever purpose.
Implementing technology into lending will not only speed up the origination process but will also guide the borrowers from start to finish. The current efforts of the fintech companies primarily focus on two main aspects:
- Streamlining the user experience for the borrowers and
- Improving the data management process that may be required for loan underwriting.
With intent to smoothen the borrower experience the online lending platforms allow users to apply for a loan without even having to meet the loan officer or make a call. The technology provides a lot of benefits such as:
- Giving the customers the ability to upload documents
- Manage their applications on their own schedule
- Reducing the time for the loan processing and
- Lowering the cost of borrowing.
In short, consumers now can have quicker loans through a more convenient process. It is all due to the subprime lending process followed by the banks and financial organizations now. The loan origination costs are not passed on to the customers as the lenders have found the ways to reduce these expenses by automating the loan origination as well as their underwriting process.
Big data backend
Apart from the convenient and faster loan processing, the use of technology in lending has also helped the lenders to verify credit scores of the borrowers, track different information related to their payment history and finally evaluate their credit worthiness flawlessly.
All these automated processes results in the reduction of cost and this saved amount is translated into more reasonable loan fees for the borrowers.
The useful data collected and stored in the systems eliminates the need for any costly manual effort to gather the same. The technology based solutions has enabled them to find more useful and innovative ways to handle customer information and analyzing more efficiently enabling them to act in a better way according to the available data and information ensuring best user experience.
How to Protect Yourself From 5G Network?
Today we live in the era of technology where technical things are developing too fast than any other developments in this world. It seems that people of this century are hungry for the new technological ideas, instead, say that it looks more of a race between the different countries of the world to show that they are the most developed country of this world. The invention of 5g technology is rather more a result of this race.
Now, 5g is generally known as the fifth-generation cellular network that provides broadband access. The first reasonably substantial deployment was in April 2019 in South Korea SK Telecom claimed 38,000 base stations, and KT Corporations claimed 30,000 and LG U Plus claimed 18,000 of which 85% are in the six major cities of South Korea. They use 3.5 GHz sub-spectrum in a non-standalone mode, and tested speeds were from 193 Mbit/s up and 430 Mbit/s down. 260,000 signed up in the first month and goal is 10% of phones on 5g by the end of 2019.
At the current scenario, six countries currently sale 5g radio hardware and 5g systems for carriers. Those companies are Samsung, Huawei, Nokia, ZTE, Datang Telecom, and Ericsson. Here the service area covered by providers is divided into a small geographical area called cells. An analog converts analog signals found in mobile phones to digital converter and transmitted as a stream of bits. All the 5g powered devices communicate with radio waves through local antennae array and low power sound receiver in the cell. There are plans to use the millimeter waves for 5g. Millimeter waves have a shorter range than microwaves. Therefore, the cells being tiny in size. Millimeter wave antennas are smaller than large antennae used in the previous cellular networks. 5G can support up to millions of devices per square kilometer, whereas 4G supports only 100,000 devices per square kilometer.
Now many people think that the 5G must be an improvement, right? When it comes to our mobile devices, we usually want anything that can deliver us a fast, convenient service. But these things are high, and the great things always come with a price. All electronic devices create EMF or to say electromotive force, and some of them are more harmful than others. The problem is that cell phones and other devices emit EMF, which has a short of invisible radiation, that can cause adverse effects. The radiation from the mobile devices damages cell membranes and releases cancer-causing free radicals. This will, of course, bring health issues to us because of microwaves whose towers will be a size of shoe-box, unlike 4G towers which were of gigantic shape.
Here are a few ways in which EMF can affect our body:
- Changes how we metabolize cells
- Causes psychiatric effects like anxiety, depression, neurodegenerative issues
- Cardiac arrhythmias
- Health flutters
- Fertility (affects male fertility more than female but damage is seen in both the cases)
- Affects eyesight
- Damages DNA
and many others.
How to Stop Yourselves from Getting Damaged by The Radiation
See we need not throw our mobile devices to stop ourselves from getting affected by it. The goal is always to support a healthy lifestyle. There are many ways in which we can do to prevent ourselves from getting affected by the radiation. Although the 5G network is not supported to be launched till 2020 but with the rush to be the best and fastest network many cities have started to pioneer the technology. Here are a few ways we can do to minimize current contact with the harmful radiation:
Keep Your Distance
We should keep a distance from mobile devices while we are not using it. We should keep our cell phones out from our room while we sleep during the night time and to avoid screens for the few hours before bedtime anyway. This is because of the effect of the blue light that can affect the ability to sleep well during the night. Do not use your phones while they are on charge because they comparatively emit more radiation in compared to the phone without in charge.
Turn It Off
When we are not using the WIFI, we should turn it off, especially at bedtime. Although it does not entirely eliminate EMF exposure but inevitably reduces it.
Use Specially Designed Headphones
Consider utilizing wired headphones for extended phone calls to decrease close, prolonged association with the device. Maximum cell phone headsets possess a wire that can act as an antenna and amplify the radiation emissions all the way to the earpiece.
Choose Your House Wisely
If feasible, do not inhabit near a cell tower or mini-station. Also, get included in local politics so that you can hear about all the potentially hazardous plans your city council has in store for you.
Continually keeping yourself educated about the wireless industry and the global, governmental support of this insane endeavor.
Be Proactive and Stay Out of Fear
Remain positive and robust, and continue to improve your vibration through positive thinking, forgiveness and with increased attention towards mental, emotional, and physical health.
The article was prepared by the Team of Twitch Clip Downloader