Category: Technology of Tomorrow

Home / Category: Technology of Tomorrow

Business post-COVID19

The coronavirus pandemic has created unforeseen challenges for businesses across industries. It’s safe to conclude that COVID19 has put world business on a standstill. A return to normal entails a vaccine or an anti-viral, and both are at least a year away from being released. So as of today, the only effective strategy we have is social distancing and hygienic practices, such as wearing masks and using hand sanitizers.

Out of 2.1 million positive cases globally, 145,000 patients have died. The only reason these numbers aren’t higher is due to Government-enforced lockdowns. While these lockdowns are reducing infection spread, they’ve had a severe toll on businesses. Mass unemployment has ensued, with the US alone reporting 22 million displaced workers. Companies in the Tourism and Hospitality industries have seen revenues drop down to 0, with no light at the end of the tunnel. Well, not yet at least. 

Kristalina Georgieva, the head of the IMF announced that the recession the global economy is currently in could be worse than the major crash of 2008. Millennial and Gen Z business owners may not remember how bad things got during the last financial crisis. But, entrepreneurs who were operating at the time can testify to the severity of the struggle. Companies today have chosen to re-group and re-strategize to make it through these unprecedented times. 

I’ve compiled the following pointers that my company is keeping in mind to navigate around these uncertain times:

A change in the online landscape

The demand for essential commodities is at an all-time high despite the lockdowns across countries. People are also spending more time than usual on their devices. This is changing the online landscape significantly and allowing for all companies – big or small, to capture the online marketplace. 

Canada and the United States experienced a 56% rise in online orders (between 22nd March and 4th April 2020). Amazon’s sales have increased significantly ever since the lockdown, and their first quarterly figures are poised to be 22% higher than that of last year. 

The internet will become more important than it has ever been.

In Indonesia, ride-hailing companies Gojek and Grab are offering “ready-to-cook” features on their respective apps, allowing consumers to get frozen meal-kits delivered to their homes in order to break their fast. 

Bengaluru’s civic corporation body, the Bruhat Bengaluru Mahanagara Palike, or BBMP has managed to assemble, on Whatsapp,  merchants, hyperlocal logistics companies, and even on-demand service providers, to cater to the public’s needs. 

Money transfer companies are providing secure and user-friendly online platforms for remittances. This is an important step for them to recover from the instant elimination of their offline stores.

Late-night shows in the United States have already begun hosting on social media platforms. Across industries, companies are making the best use of the online medium to deliver to their existing customers, but more importantly, to build new ones. 

Perhaps there’s an opportunity here for your business as well. 

Globalism under threat

While member countries of the European Union are generally in sync with the body’s one Europe view, they acted independently to tackle COVID 19. Due to the outbreak, pre-existing sentiments of populism and xenophobia will amplify. The case for a world without borders will seem unrealistic. Governments will pass stricter legislations surrounding foreign travel and immigration.

Countries will become shy of globalism.

As a result, companies that rely on other nations to complete their supply chains will have to explore alternatives to accomplish the same from their home countries. COVID19 has exhibited how the world’s heavy dependency on China has today stunted their economies and hurt business. Businesses might have to learn how to survive without significant foreign outsourcing.

Low/no contact solutions

Times are hard for all high-touch, close contact businesses. This is especially true for those businesses in the Hospitality, Manufacturing, and Healthcare industries, as they cannot operate online. Since there is a general trust deficit across customers, here is a list of possibilities that could help your business cope:

  • Advanced online payment options will become the norm, while cash transactions will reduce
  • Food and grocery delivery services will become more popular
  • Several healthcare companies have introduced chatbots where patients can key in their symptoms. This can help doctors arrive at a quick diagnosis, in a remote manner
  • Contactless dining options will be introduced in most restaurants – with the food aggregator companies leading the way 
  • Manufacturers could introduce remote-controlled manufacturing processes, thereby reducing the number of workers in close proximity to each other

If low contact solutions prove that they can effectively tackle infection spread, they could then lay the foundation for bringing back the most affected industries – Hospitality and Tourism.  

Blurred lines between formal and online education

COVID19 has thrust several educational institutions to shift online. With many reputed institutions making a quick transition, it is more or less established that education does not require a physical classroom or a campus for that matter. But are they able to continue to engage their students? That remains a looming question. 

The idea of formal education will be under threat in a post COVID19 world.

Post COVID19, any business in the education industry will have to think beyond the traditional notions of learning. Professionals are opting for online courses to further excel in their careers. This signals a massive opportunity for traditional schools and universities to reinvent learning. Learning that meets the needs of a new economy. 

Accordingly, recruitment criteria may change, by focusing more on provable skills and less on expensive degrees. 

Tech companies, in particular, will find this highly applicable.

Plan for today, prepare for tomorrow

Although the coronavirus pandemic has fundamentally changed the business landscape, tomorrow’s environment will be different, but no less rich in possibilities for those who are prepared. 

It’s pertinent for businesses to understand that everything we know ceases to operate in the same manner. COVID19 has shown that it’s time to steer the world business according to a “new normal” and build based on that premise.

Latest Innovations in the field of AI & ML

Artificial Intelligence perfectly captures the zeitgeist of today’s technology. With each day progressing, we are discovering new use cases for AI. Use cases that can improve our quality of life, and also a few that threaten it if development isn’t kept in check. 

AI & ML innovations

Businesses from banking to healthcare are implementing AI in their operations. We’ve reached a stage wherein an AI strategy is a must-have for businesses, and a lack thereof can prove to be a serious disadvantage.

 AI is here to stay, and it is revolutionizing businesses, with an impact quite similar or perhaps stronger than that of the industrial revolution. 

Here are some of the latest innovations in the field:

Advanced autonomous vehicles

Autonomous vehicle manufacturers are figuring out ways to tackle complex computer vision problems. A common problem in this field has been the lack of access to edge-case training data. For example, most autonomous vehicles are only trained to identify pedestrians, road markings, other cars, and signals. But, how should such a vehicle react to a group of 4th of July celebrators storming onto the street? 

With the advent of synthetic data and edge-case emphasis, computer vision systems can be trained to understand these rare exceptional cases, thus improving vehicle quality and overall safety. 

Besides, computer vision systems have become better at identifying transparent objects, by a mid-step process of converting the object into its opaque version, before being fed into the system.

Improvements to public safety

The governments of nations from India to France are planning to implement computer vision cameras across streets, to curb crime. Computer vision has shown that it can be used to identify missing individuals, monitor criminal activities in real-time, and locate areas that require additional police attention.

Smarter money

The financial services industry has discovered the potential of Big Data and Machine Learning. Money transfer companies are discovering the importance of competitive intelligence, for increasing churn rate and exploring multiple avenues for payment-based opportunities. 

Also, AI helps financial firms mitigate fraudulent transactions and provides more accurate ways of assigning credit scores. 

Laws surrounding AI

Another contribution to AI’s innovation is the governments’ understanding of AI’s capabilities and potential threat. The EU announced that it will restrict the development of high-risk AI applications. The type that might hurt employment opportunities and reduce the quality of human life. 

Lawmakers are beginning to understand that not all AI development will benefit society, thus allowing only the most important and life-satisfying innovations to see the light of day.

Accuracy in patent visualization

As AI innovations increase, so do the patents filed. Patent visualization platforms hold large data that can allow patent holders to identify potential users, and for tech developers to adhere to patent-related legislation.

Predictive medicine in healthcare

With the recent coronavirus outbreak, AI developers are looking for ways to use machine learning to track the spread of a virus. ML is also being used to identify health-related patterns and symptoms in patients, for implementing predictive medicine and treating avoidable diseases.

Improving workplace performance

Many factors add up to determine results at the workplace. Businesses are now using AI for inventory management, evaluating employee productivity, and identifying flaws in ongoing workflows. 

AI in inventory management can be used to warn teams about shortages in resources. With the right algorithms, AI can also replace these resources on time and ensure no outages from its end. Employees can be monitored and areas of improvement, skill, and bandwidth, can be identified to make the best use of their abilities.

Music and entertainment

AI and Big Data are entering subjective fields such as the arts. Musicians can now use AI to understand trends in Billboard charting music, and even mimic traits of popular music to improve music streams and ticket sales. 

Mobile gaming has benefited from augmented reality, thus creating virtual scenarios in the real world. In filmmaking, facial expressions can be adjusted and synthetic video effects can be introduced for a more vivid watching experience.

Conclusion

AI is developing at an incredible pace. We are seeing countries across the globe planning AI programs. An AI race between the US and China has begun, with Europe looking to give them a run for their money. 

Digital Advertising

And, our understanding of AI has improved significantly, as shown by the numerous applications we have discovered for them. From deepening our technological knowledge to having an idea of AI could turn rogue, we are becoming mature advocates of AI.

The purpose of AI is to make the lives of humans simpler and add value. Hopefully, in a year from now, we’ll have even more to celebrate as AI strengthens its position as a technological juggernaut.

Applications of Computer Vision in Healthcare

Computer vision is a field that explores ways to make computers identify useful information from images and videos. Think of it as training computers to see as humans do. While this technology has numerous applications in fields such as autonomous vehicles, retail supermarkets, and agriculture, let’s focus on the ways computer vision can benefit healthcare.

In the present scenario, doctors rely on their educated perception to treat patients. Since doctors are also prone to human error, computer vision can guide them through their diagnosis, and thus increase the treatment quality and the doctor’s focus on the patient. Further, patients can have access to the best healthcare services available, all through the swiftness and accuracy of computer vision. While still in its nascent stage, computer vision has already revealed ways in which it can improve multiple aspects of medicine. Here are a few notable ones:

Swift diagnosis:

Applications of Computer Vision

Many diseases can only be treated if they are diagnosed promptly. Computer vision can identify symptoms of life-threatening diseases early on, saving valuable time during the process of diagnosis. Its ability to recognize detailed patterns can allow doctors to take action swiftly, thus saving countless lives.

A British startup, Babylon Health, has been working to improve the speed of diagnosis using computer vision. To see this goal through, they have developed a chatbot which asks health-related questions to patients, whose responses are then, in turn, sent to a doctor. To pull out useful information from patients, the chatbot employs NLP algorithms.

In another example, scientists at the New York City-based Mount Sinai have developed an artificial intelligence capable of detecting acute neurological illnesses, such as hemorrhages or strokes. Also, the system is capable of detecting a problem from a CT scan in under 1.2 seconds — 150x faster than any human.

To train the deep neural network to detect neurological issues, 37,236 head CT scans were used. The institution has been using NVIDIA’s graphics processing units to improve the functioning and efficiency of their systems. 

Computer vision also allows doctors to spend less time analyzing patient data, and more time with the patients themselves, offering helpful and focused advice. This leads to improved efficiency of healthcare and can help in enabling doctors to treat more patients per year.

Health monitoring:

The human body goes through regular changes, but some of the issues it faces on the surface can, at times, represent symptoms of impending disease. These can often be overlooked through human error. With computer vision, there exists a quick way to access a variety of the patient’s health metrics. This information can help patients make faster health decisions and doctors make more well-informed diagnoses. Surgeries could also benefit from such technology.

For example, let’s consider the case of childbirth, based on the findings of the Orlando Health Winnie Palmer Hospital for Women and Babies. The institute has developed an artificial intelligence tool that employs computer vision to measure the amount of blood women lose during childbirth. Since its usage, they have observed that doctors often overestimate blood loss during delivery. As a result, computer vision allows them to treat women more effectively after childbirth.

There are also efforts such as AiCure, another New York-based startup that uses computer vision to track whether patients undergoing clinical trials are adhering to their prescribed medication using facial recognition technology. The goal behind this project is to reduce the number of people who drop out of clinical trials, aka attrition. This can lead to a better understanding of how medical care affects patients, and why.

Computer vision, paired with deep learning, can also be used to read two-dimensional scans and convert them into interactive 3D models. The models can then be viewed and analyzed by healthcare professionals to gain a more in-depth understanding of the patient’s health. Also, these models can provide more intuitive details than multiple stacked 2D images from a wide variety of angles.

Significant developments have taken place in dermatology. Computers are better than doctors at identifying potential health hazards in human skin. This allows for the early detection of skin diseases and personalized skincare options.

Further, no time is lost laboring over hand-written patient reports, since computer vision is capable of automatically drawing up accurate reports using all of the available patient data.

Precise diagnosis:

 The accuracy that computer vision provides eliminates the risk that comes with human judgment. These reliable systems can quickly detect minute irregularities that even skilled doctors could easily miss. 

When these kinds of symptoms are identified quickly, it saves patients the trouble of dealing with complicated procedures later on. Thus, it has the potential to minimize the need for complex surgical procedures and expensive medication.

One example of this would be computer vision’s use in radiology. Computer vision systems can help doctors take detailed X-rays and CT scans, with minimal opportunity for human error. These AI systems allow doctors to take advantage of the systems’ exposure to thousands of historical cases, which can be helpful in scenarios that doctors might not have come across before. The common uses of computer vision within radiology include detecting fractures and tumors.

Preemptive strategies

Computer Vision In Healthcare

Using machine learning, computer vision systems can sift through hundreds of thousands of images, learning with each scan how to better analyze and detect symptoms, possibly even before they present themselves.

This allows the medical professional to pre-emptively treat patients for symptoms of diseases they could develop in the future. Using input data from thousands of different sources, these AI systems can learn what leads to disease in the first place.

Present barriers

While computer vision is a revolutionary technology that will likely change healthcare as it is known today, there are some notable problems associated with the technology.

Firstly, interoperability. The computer vision AI from one region or hospital may not necessarily yield accurate or reliable results for patients outside of its sample data set. Of course, the machine learns with time, but overcoming this barrier could lead to faster adoption of this ground-breaking technology.

Also, there are privacy concerns around the digitization of patient medical data and its provision to artificial intelligence systems. This data vault needs to be stored in secure storage which can be easily accessed by the system, to avoid users with malicious intent.

And these systems aren’t perfect. Even the smallest margin of error cannot be tolerated in this space, because the consequences for wrong diagnoses are very real. These are human lives being dealt with, and the artificial intelligence systems aren’t responsible for providing treatment, only suggesting it. 

Also, there may be cases where the healthcare provider comes up with a diagnosis that conflicts with the computer vision system, leaving patients with a tough decision to make, and the doctors with all the responsibility.

Conclusion:

When computer vision is employed effectively in healthcare, it truly holds the potential to improve diagnoses and the standard of healthcare worldwide. This makes sense because doctors rely on images, scans, patient symptoms, and reports to make health-related decisions for their patients. The sheer abundance of use cases employed by computer vision systems make their analysis accurate. Thus, it allows doctors to make these crucial decisions with confidence.

Computer vision systems also allow for quality-of-life improvements, such as less time spent drafting reports, analyzing scans and procuring data. These systems could even be deployed remotely, enabling patients to receive professional medical attention from areas that don’t have easy access to healthcare services. All this lets doctors spend more time with patients, which is what healthcare should be about.

Technology Trends

As trends develop, it empowers considerably quicker change and progress, causing the increasing speed of the pace of progress, until, in the long run, it will wind up exponential.

Technology-based vocations don’t change at that equivalent speed; however, they do advance, and the smart IT expert perceives that their job won’t remain the equivalent.  Here are eight evolution patterns that have prominently developed in 2019.

trends in tech

Artificial Intelligence (AI)

Man-made brainpower, or AI, has just gotten a great deal of buzz as of late, however it keeps on being a pattern to watch since its impacts on how we live, work and play are just in the beginning periods. Moreover, different parts of AI have created, including Machine Learning, which we will go into beneath. Man-made intelligence alludes to PCs frameworks worked to imitate human insight and perform assignments, for example, acknowledgment of pictures, discourse or examples, and basic leadership.

Simulated intelligence has been around since 1956 is now generally utilized. Truth be told, five out of six people use AI benefits in some structure each day, including route applications, gushing administrations, cell phone individual associates, ride-sharing applications, home individual partners, and brilliant home gadgets. Notwithstanding buyer use, AI is utilized to timetable trains, survey business hazards, anticipate support, and improve vitality proficiency, among numerous other cash sparing undertakings.

Machine Learning

Machine learning is a subset of AI. With Machine Learning, PCs are customized to figure out how to accomplish something they are not modified to do: They truly learn by finding examples and bits of knowledge from information. All in all, we have two kinds of learning, managed and unaided.

While Machine Learning is a subset of AI, we additionally include subsets inside the space of Machine Learning, including neural systems, characteristic language handling (NLP), and profound learning

AI is quickly being conveyed in a wide range of ventures, making a gigantic interest for talented experts. The Machine Learning business sector is relied upon to develop to $8.81 billion by 2022. AI applications are utilized for information examination, information mining and example acknowledgment. On the buyer end, Machine Learning forces web indexed lists, constant advertisements, and system interruption identification, to give some examples of the numerous undertakings it can do.

Cyber Security

Cybersecurity probably won’t appear among developing innovation, given that it has been around for some time, yet it is advancing similarly as different advancements seem to be. That is to some extent since dangers are continually new. The pernicious programmers who are attempting to wrongfully get to information won’t surrender at any point shortly, and they will keep on discovering technologies to traverse even the hardest safety efforts. It’s likewise to a limited extent because innovation is being adjusted to upgrade security. Three of those headways are equipment confirmation, cloud innovation, and profound getting the hang of, as per one master.

Another includes information misfortune counteractive action and social investigation to the rundown. For whatever length of time that we have programmers, we will have cybersecurity as a rising innovation since it will always develop to safeguard against those programmers.

As verification of the solid requirement for cybersecurity experts, the quantity of cybersecurity employments is growing multiple times quicker than other tech occupations. Nonetheless, we’re missing the mark with regards to filling those occupations. Subsequently, it’s anticipated that we will have 3.5 million unfilled cybersecurity occupations by 2021.

Cyber Security

Chatbots

Chatbots are PC programs that copy composed or spoken human discourse for the motivations behind reproducing a discussion or collaboration with a genuine individual. Today, chatbots are generally utilized in the client care space for assuming jobs which are customarily performed by absolutely real people, for example, client care agents and consumer loyalty delegates. The utilization of chatbots is required to increment radically in 2019.

Blockchain

Albeit a great many people consider blockchain innovation in connection to cryptographic forms of money, for example, Bitcoin, blockchain offers security that is valuable from multiple points of view. In the least difficult of terms, blockchain can be portrayed as information you can just add to, not detract from or change. Not having the option to change the past squares is the thing that makes it so secure. Moreover, blockchains are agreement driven, as clarified in this Forbes article, so nobody substance can assume responsibility for the information.

This increased security is the reason blockchain is utilized for cryptographic money, and why it can assume a critical job in ensuring data, for example, individual restorative information. Blockchain could be utilized to radically improve the worldwide inventory network, as portrayed here, just as secure resources, for example, workmanship and land.

Virtual Reality and Augmented Reality

Computer-generated Reality (VR) drenches the client in a domain while Augment Reality (AR) improves their condition. Even though VR has essentially been utilized for gaming up to this point, it has likewise been utilized for preparing, similarly as with VirtualShip; a recreation programming used to prepare U.S. Naval force, Army and Coast Guard ship chiefs. The famous Pokemon Go is a case of AR.

Both have tremendous potential in preparing, diversion, instruction, promoting, and even recovery after damage. Either could be utilized to prepare specialists to do the medical procedures, offer historical center goers a more profound encounter, upgrade amusement leaves, or even improve advertising, similarly as with this Pepsi Max transport cover.

Edge Computing

Earlier an innovation pattern to watch, distributed computing has moved toward becoming standard, with significant players AWS (Amazon Web Services), Microsoft Azure and Google Cloud ruling the market. The selection of distributed computing is as yet developing, as an ever-increasing number of organizations relocate to a cloud arrangement. Be that as it may, it’s never again the rising innovation. Edge is. Move over, distributed computing, and clear a path for the edge.

As the amount of information, we’re managing keeps on expanding, we’ve understood the deficiencies of distributed computing in certain circumstances. Edge figuring is intended to help tackle a portion of those issues as an approach to sidestep the idleness brought about by distributed computing and getting information to a server farm for handling. It can exist “on the edge,” maybe, closer to where figuring needs to occur. Consequently, edge registering can be utilized to process time-touchy information in remote areas with constrained or no availability to a unified area. In those circumstances, edge registering can act like small datacenters.

Edge processing will increment as utilize the Internet of Things (IoT) gadgets increments. By 2022, the worldwide edge figuring business sector is required to reach $6.72 billion.

Internet of Things

Even though it seems like a game you’d play on your cell phone, the Internet of Things (IoT) is what’s to come. Many “things” are presently being worked with a WiFi network, which means they can be associated with the Internet—and to one another. Consequently, the Internet of Things, or IoT. IoT empowers gadgets, home apparatuses, vehicles and substantially more to be associated with and trade information over the Internet. What’s more, we’re just first and foremost phases of IoT: The quantity of IoT gadgets arrived at 8.4 billion out of 2017 is and expected to arrive at 30 billion gadgets by 2020.

As purchasers, we’re now utilizing and profiting by IoT. We can bolt our entryways remotely on the off chance that we neglect to when we leave for work and preheat our broilers on our route home from work, all while following our wellness on our Fitbits and hailing a ride with Lyft. Yet, organizations additionally have a lot to pick up now and sooner rather than later. The IoT can empower better wellbeing, effectiveness, and basic leadership for organizations as information is gathered and broke down.

It can empower prescient upkeep, accelerate therapeutic consideration, improve client assistance, and offer advantages we haven’t envisioned at this point. Nonetheless, in spite of this aid in the advancement and reception of IoT, specialists state insufficient IT experts are landing prepared for IoT positions. An article at ITProToday.com says we’ll require 200,000 more IT laborers that aren’t yet in the pipeline, and that a study of designers found 25.7 percent accept deficient ability levels to be the business’ greatest obstruction to development.

Even though advancements are developing and developing surrounding us, these eight spaces offer promising profession potential now and for a long time to come. And each of the eight are experiencing a deficiency of talented specialists, which means everything looks good for you to pick one, get prepared, and jump aboard at the beginning times of the innovation, situating you for progress now and later on.

Development tools for AI and ML

Artificial Intelligence a popular technology of computer science is also known as machine intelligence. Machine Learning is a systematic study of algorithms and statistical models.

AI creates intelligent machines that react like humans as it can interpret new data. ML enables computer systems to perform learning-based actions without explicit instructions.

AI global market is predicted to reach $169 billion by 2025. Artificial Intelligence will see increased investments for the implementation of advanced level software. Organizations will strategize technological advancements.

Various platforms and tools for AI and ML empower the developers to design powerful programs.

Tools for AI and ML

Tools for AI and ML:

Google ML Kit for Mobile:

Software development kit for Android and IOS phones enables developers to build robust applications with optimized and personalized features. This kit allows developers to ember the machine learning technologies with cloud-based APIs. This kit is integration with Google’s Firebase mobile development platform.

Features:

  1. On-device or Cloud APIs
  2. Face, text and landmark recognition
  3. Barcode scanning
  4. Image labeling
  5. Detect and track object
  6. Translation services
  7. Smart reply
  8. AutoML Vision Edge

Pros:

  1. AutoML Vision Edge allows developers to train the image labeling models for over 400 categories it capacities to identify.
  2. Smart Reply API suggests response text based on the whole conversation and facilitates quick reply.
  3. Translation API can convert text up to 59 languages and language identification API forms a string of text to identify and translate.
  4. Object detection and tracking API lets the users build a visual search.
  5. Barcode scanning API works without an internet connection. It can find the information hidden in the encoded data.
  6. Face detection API can identify the faces in images and match the facial expressions.
  7. Image labeling recognizes the objects, people, buildings, etc. in the images and with each matched data; ML shares the score as a label to show the confidence of the system.

Cons:

  1. Custom models can grow in huge sizes.
  2. Beta Release mode can hurt cloud-based APIs.
  3. Smart reply is useful for general discussions for short answers like “Yes”, “No”, “Maybe” etc.
  4. AutoML Vision Edge tool can function successfully if plenty of image data is available.

Accord.NET:

This Machine Learning framework is designed for building applications that require pattern recognition, computer vision, machine listening, and signal processing. It combines audio and image processing libraries written in C#. Statistical data processing is possible with Accord. Statistics. It can work efficiently for real-time face detection.

Features:

  1. Algorithms for Artificial Neural networks, Numerical linear algebra, Statistics, and numerical optimization
  2. Problem-solving procedures are available for image, audio and signal processing.
  3. Supports graph plotting & visualization libraries.
  4. Workflow Automation, data ingestion, speech recognition,

Pros:

  1. Accord.NET libraries are available from the source code and through the executable installer or NuGet package manager.
  2. With 35 hypothesis tests including two-way and one-way ANOVA tests, non-parametric tests useful for reasoning based on observations.
  3. It comprises 38 kernel functions e.g. Probabilistic Newton Method.
  4. It contains 40 non-parametric and parametric statistical distributions for the estimation of cost and workforce.
  5. Real-time face detection
  6. Swap learning algorithms and create or test new algorithms.

Cons:

  • Support is available for. Net and its supported languages.
  • Slows down because of heavy workload.

Tensor Flow:

It provides a library for dataflow programming. The JavaScript library helps in machine learning development and the APIs help in building new models and training the systems. Tensorflow developed by Google is an opensource Machine Learning library that aids in developing the ML models and numerical computation using dataflow graphs. Use it by installing, use script tags or through NPM.

Features:

  1. A flexible architecture allows users to deploy computation on one or multiple desktops, servers, or mobile devices using a single API.
  2. Runs on one or more GPUs and CPUs.
  3. It’s yielding scheme of tools, libraries, and resources allow researchers and developers to build and deploy machine-learning applications effortlessly.
  4. High-level APIs accedes to build and train for ML models efficiently.
  5. Runs existing models using TensorFlow.js, which acts as a model converter.
  6. Train and deploy the model on the cloud.
  7. Has a full-cycle deep learning system and helps in the neural network.

Pros:

  1. You can use it in two ways, i.e. by script tags or by installing through NPM.
  2. It can even help for human pose estimation.
  3. It includes the variety of pre-built models and model subblocks can be used together with simple python scripts.
  4. It is easy to structure and train your model depending on data and the models with you are training the system.
  5. Training other models for similar activities is simpler once you have trained a model.

Cons:

  1. The learning curve can be quite steep.
  2. It is often doubtful if your variables need to be tensors or can be just plain python types.
  3. It restricts you from altering algorithms.
  4. It cannot perform all computations on GPU intensive computations.
  5. The API is not that easy to use if you lack knowledge.

Infosys Nia:

This self-learning knowledge-based AI platform accumulates organizational data from people, business processes and legacy systems. It is designed to engage in complicated business tasks to forecast revenues and suggest profitable products the company can introduce.

Features:

  1. Data Analytics
  2. Business Knowledge Processing
  3. Transform Information
  4. Predictive Automation
  5. Robotic Process Automation
  6. Cognitive Automation

Pros:

  1. Organizational Transformation is possible with enhanced technologies to automate and increase operational efficiency.
  2. It enables organizations to continually use previously gained knowledge as they grow and even modify their systems.
  3. Faster data processing adds to the flexibility of data visualization, analytics, and intelligent decision-making.
  4. Reduces human efforts involved in solving high-value customer problems.
  5. It helps in discovering new business opportunities.

Cons:

  1. It is difficult to understand how it works.
  2. Extra efforts needed to make optimum use of this software.
  3. It has lesser features of Natural Language Processing.

Apache Mahout:

Mainly it aims towards implementing and executing algorithms of statistics and mathematics. It’s mainly based on Scala and supports Python. It is an open-source project of Apache.
Apache Mahout is a mathematically expressive Scala DSL (Domain Specific Language).

Features:

  1. It is a distributed linear algebra framework and includes matrix and vector libraries.
  2. Common maths operations are executed using Java libraries
  3. Build scalable algorithms with an extensible framework.
  4. Implementing machine-learning techniques using this tool includes algorithms for regression, clustering, classification, and recommendation.
  5. Run it on top of Apache Hadoop with the help of the MapReduce paradigm.

Pros:

  1. It is a simple and extensible programming environment and framework to build scalable algorithms.
  2. Best suited for large datasets processing.
  3. It eases the implementation of machine learning techniques.
  4. Run-on the top of Apache Hadoop using the MapReduce paradigm.
  5. It supports multiple backend systems.
  6. It includes matrix and vector libraries.
  7. Deploy large-scale learning algorithms using shortcodes.
  8. Provide fault tolerance if programming fails.

Cons:

  1. Needs better documentation to benefit users.
  2. Several algorithms are missing this limits the developers.
  3. No enterprise support makes it less attractive for users.
  4. At times it shows sporadic performance.

Shogun:

It provides various algorithms and data structures for unified machine learning methods. Shogun is a tool written in C++, for large-scale learning, machine learning libraries are useful in education and research.

Features:

  1. Huge capacity to process samples is the main feature for programs with heavy processing of data.
  2. It provides support to vector machines for regression, dimensionality reduction, clustering, and classification.
  3. It helps in implementing Hidden Markov models.
  4. Provides Linear Discriminant Analysis.
  5. Supports programming languages such as Python, Java, R, Ruby, Octave, Scala, and Lua.

Pros:

  1. It processes enormous data-sets extremely efficiently.
  2. Link to other tools for AI and ML and several libraries like LibSVM, LibLinear, etc.
  3. It provides interfaces for Python, Lua, Octave, Java, C#, C++, Ruby, MatLab, and R.
  4. Cost-effective implementation of all standard ML algorithms.
  5. Easily combine data presentations, algorithm classes, and general-purpose tools.

Cons:

Some may find its API difficult to use.

Scikit:

It is an open-source tool for data mining and data analysis, developed in Python programming language. Scikit-Learn’s important features include clustering, classification, regression, dimensionality reduction, model selection, and pre-processing.

Features:

  1. Consistent and easy to use API is also easily accessible.
  2. Switching models of different contexts are easy if you learn the primary use and syntax of Scikit-Learn for one kind of model.
  3. It helps in data mining and data analysis.
  4. It provides models and algorithms for support vector machines, random forests, gradient boosting, and k-means.
  5. It is built on NumPy, SciPy, and matplotlib.
  6. BSD license lets you use commercially.

Pros:

  1. Easily documentation is available.
  2. Call objects to change the parameters for any specific algorithm and no need to build the ML algorithms from scratch.
  3. Good speed while performing different benchmarks on model datasets.
  4. It easily integrates with other deep learning frameworks.

Cons:

  1. Documentation for some functions is slightly limited hence challenging for beginners.
  2. Not every implemented algorithm is present.
  3. It needs high computation power.
  4. Recent algorithms such as XGBoost, Catboost, and LightGBM are missing.
  5. Scikit learns models take a long time to train, and they require data in specific formats to process accurately.
  6. Customization for the machine learning models is complicated.
AI and ML development

Final Thoughts:

Twitter, Facebook, Amazon, Google, Microsoft, and many other medium and large enterprises continuously use improved development tactics. They extensively use tools for AI and ML technology in their applications.

Various tools for AI and ML can ease software development and make the solutions effective to meet customer requirements. Make user-friendly mobile applications or other software that are potentially unique. Using Artificial Intelligence and Machine Learning create intelligent solutions for improved human life. New algorithm creation, using computer vision and other technology and AI training requires skills and development of innovative solutions that need powerful tools.

Computer Vision Advances and Challenges

Computer vision refers to the field of training computers to visualize data as humans do. This technology has the potential to reach a stage wherein computers can understand images and videos better than humans. Also, the use cases are practically limitless, despite the technology still existing in its nascent stage of exploration. 

Computer Vision

Computer vision as a concept has been around since the 1950s. In its infancy, computers were trained to distinguish between shapes such as squares and triangles. Later on, training shifted towards distinguishing between typed and handwritten text.

Reasons for popularity

The main reason for computer vision’s popularity is its potential to revolutionize many every-day aspects of our lives. Computer vision drives autonomous vehicles and allows them to distinguish between traffic signal lights, medians, pedestrians, etc. It can also be used in healthcare, for detecting tumors in advance and identifying skin issues. 

There is a huge opportunity for employing computer vision in agriculture as well. It can be used to monitor the quality of crops, locate weeds and pests, based on which farmers can take action. 

Applications of Computer Vision

How about facial recognition? Yes, computer vision is already being used in new-generation smartphones to detect the user’s face. Even QR code scanning is an example of the adoption of computer vision. This technology can also be used in supermarkets to identify which users are making which purchases. 

Amazon is testing a convenience store called Amazon Go, which doesn’t have a billing counter. Instead, the store uses computer vision to identify customers and the items they add to their cart. A bill is sent to them online through the Amazon Go App once they leave the store with these items.

Advantages of computer vision

While computer vision has a lot more to achieve, it has already achieved ground-breaking innovations. That makes sense because this technology brings a lot of advantages to daily and professional life. 

Reliability

The human eye grows tired of scanning its environment. Factors such as fatigue and health come into the picture. With computer vision, this is eliminated because cameras and computers never get tired. Since the human factor is removed, it is easier to rely on the result. 

Numerous use cases 

From healthcare and agriculture to banking and automobiles, if explored smartly, computer vision can be employed in almost every aspect of our lives. These machines learn by viewing thousands of labeled images, thus understanding the traits of what’s being visualized. The same primary computer vision technology that evaluates the quality of packages in a factory can also be used to identify trends in the stock market.

Cost reduction

Computer vision can be used to increase productivity in operations and eliminate faulty products from hitting the shelves. This technology will also allow companies to manage their teams efficiently by identifying staff that could be used for other activities that require attention. For example, in Amazon fulfillment centers, productivity among workers is measured to improve efficiency and resource allocation.

Challenges faced by Computer Vision

Every emerging technology starts with a few significant drawbacks. From this technology’s development to its impact on society, there is a lot to look forward to, but a lot to be concerned about as well.

The challenge of making systems human-like

As much as computer vision is making huge leaps in its progress, it is difficult to simulate something as complex as the human visual system. The human brain-eye coordination is a marvel to behold, and its ability to understand its environment and make decisions is unparalleled by computer vision systems, at least at the moment.

Tasks such as object detection are complicated since objects of interest in images and videos may appear in a variety of sizes and aspect ratios. Also, a computer vision system will have to distinguish one object from multiple others within its view. This is a skill that computers are taking time to get better at.

Computer vision also hasn’t reached the stage wherein it can identify the difference between handwritten and typed text. This is due to the variety of handwriting styles, curves, and shapes employed while writing.

Privacy

This is arguably the biggest social threat that computer vision poses. The qualities that make computer vision effective are also the concerns of humans that value their privacy. With computers learning from thousands and thousands of images and videos, computers are getting better at recognizing individuals by their facial features, and everyone’s information is stored on a cloud.

Computer vision can track people’s whereabouts and monitor their habits. With such information, governments and businesses could be lured into penalizing and rewarding workers based on their actions. China, a nation with strong AI capabilities, is already looking to use computer vision to monitor its citizens and provide information that funds its controversial social credit system. On the other hand, San Fransisco has banned the use of facial recognition technology by the police and other related agencies.

It is psychologically unhealthy for humans to know that they are constantly being observed and monitored during every aspect of their lives. It would be interesting to see how governments intend to tackle this issue.

Final Thoughts

Computer vision’s progress can make people truly feel like they’re living through a sci-fi film. The future of this technology is filled with a range of use cases to be catered to. Numerous businesses within this realm are collecting millions of images and videos that can be used to train their computer vision systems. Also, existing businesses are exploring ways to employ computer vision into their operations. 

Challenges of Computer Vision

Computer vision has its present challenges, but the humans working on this technology are steadily improving it. Every emerging technology brings its fair share of advantages and disadvantages. While it is important to celebrate its progress, it is equally important to gauge its potential negative effect on society. This is the only way to ensure that computer vision makes our lives more comfortable and less constrained.

Jobs Artificial Intelligence

In the previous couple of years, computerized reasoning has progressed so rapidly that it presently appears to be not a month passes by without a newsworthy Artificial Intelligence (AI) achievement. In territories as wide-running as discourse interpretation, medicinal analysis, and interactivity, we have seen PCs beat people in frightening manners.

This has started an exchange about how AI will affect work. Some dread that as Artificial intelligence improves, it will replace laborers, making a consistently developing pool of unemployable people who can’t contend monetarily with machines.
This worry, while reasonable, is unwarranted. Truth be told, AI will be the best employment motor the world has ever observed.

2020 will be a significant year in AI-related work elements, as indicated by Gartner, as AI will turn into a positive employment helper. The number of occupations influenced by Artificial Intelligence will shift by industry; through 2019, social insurance, the open division, and instruction will see constantly developing employment requests while assembling will be hit the hardest. Beginning in 2020, AI-related occupation creation will a cross into positive area, arriving at 2,000,000 net-new openings in 2025, Gartner said in a discharge.

Numerous huge advancements in the past have been related to change the time of impermanent occupation misfortune, trailed by recuperation, at that point business change and AI will probably pursue this course.

Jobs by Artificial Intelligence (AI) and ML

JOBS CREATED BY AI AND MACHINE LEARNING

A similar idea applies to AI. It is an instrument that individuals need to figure out how to utilize and how to apply to what’s going on with as of now. New openings are now being made that are centered around applying AI to security, improving basic AI methods, and on keeping up these new apparatuses.

Plenty of new openings will develop for those with mastery in applying center Artificial Intelligence innovation to new fields and applications. Specialists will be expected to decide the best sort of AI (for example master frameworks or AI), to use for a specific application, create and train the models, and keep up and re-train the frameworks as required. In fields, for example, security, where sellers have enabled security programming with AI, it’s up to clients – the security investigators – to comprehend the new capacities and put them to be the most ideal use.

Training is another field where AI and machine learning is making new openings. As of now, over the US, the main two situations in the rundown of scholastic openings are for Security and Machine Learning specialists. Colleges need more individuals and can’t discover educators to show these fundamentally significant subjects.

FUTURE JOBS PROSPECTS BECAUSE OF AI AND MACHINE LEARNING

In a few businesses, AI will reshape the sorts of employments that are accessible. What’s more, much of the time, these new openings will be more captivating than the monotonous errands of the past. In assembling, laborers who had recently been attached to the generation line, looking for blemished items throughout the day, can be redeployed in increasingly profitable interests — like improving procedures by following up on bits of knowledge gathered from AI-based sensor and vision stages.

These are increasingly specific errands and retraining or uptraining might be important for laborers to successfully satisfy these new jobs — something the two organizations and people should address sooner than later.

Man-made intelligence-based arrangements in any industry produce monstrous measures of information, frequently from heterogeneous sources. Successfully saddling the intensity of this information requires human abilities. Profound learning researchers have come to comprehend that setting is basic for preparing powerful AI models — and people are important to clarify this information to give set in uncertain circumstances and help spread all this present reality varieties an AI framework will experience.

Keeping that in mind, Appen utilizes more than 40,000 remote contractual workers a month to perform information explanation for our customers, drawing from a pool of more than 1 million talented annotators around the world.

These occupations wouldn’t exist without the profound learning innovation that makes AI conceivable. As researchers and designers make immense advances in innovation, organizations and laborers may need to adopt new mechanical aptitudes to remain aggressive.

Simulated intelligence is helping drive work creation in cybersecurity

As the worldwide economy is progressively digitized and mechanized, effectively unavoidable criminal ventures – programmers, malware, and different dangers – will develop exponentially, requiring engineers, analyzers, and security specialists to alleviate dangers to crucial open framework and meet expanding singular personality concerns.

In the previous couple of years there has been an enormous increment in cybersecurity work postings, a large number of which stay unfilled. With this deficiency of cybersecurity experts, most security groups have less time to proactively protect against progressively complex dangers. This interest has made a significant specialty for laborers to fill.

The stream down impact of industry-wide digitalization

In a roundabout way, the efficiencies and openings that profound learning and computerization empower for organizations can make a great many employments. While mechanized conveyance strategies, for example, self-driving conveyance trucks will take a great many drivers off the street, an ongoing Strategy + Business article proposes that, “In reality as we know it where organizations are progressively made a decision on the nature of the client experience they give, you will require representatives who can consolidate the aptitudes of a client care specialist, advertiser, and sales rep to sit in those trucks and connect with clients as they make conveyances.”

Additionally, the higher profitability and positive development empowered by AI will positively affect employing as organizations will just need to procure more laborers to take on existing assignments that require human abilities. Consider client support, publicists, program administrators, and different jobs that require abilities, for example, compassion, moral judgment, and inventiveness.

Growing new aptitudes to endure and flourish

It’s anything but difficult to perceive any reason why laborers and administrators the same may be hesitant to execute AI-controlled mechanization. Be that as it may, as their rivals receive this innovation and start to outpace them in deals, creation, and development, it will expect them to adjust. The two organizations and laborers should put resources into developing new innovative aptitudes to enable them to remain significant in this information-driven scene. If they can do this, the open doors for business and expert development are perpetual.

Development in AI and ML jobs

DEVELOPMENT IN THE FIELD OF AI and ML

Man-made reasoning is a method for making a PC, a PC controlled robot, or a product think keenly, in the comparative way the insightful people think.
Man-made brainpower is a science and innovation dependent on orders, for example, Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A significant push of Artificial Intelligence (AI) is in the advancement of PC capacities related to human knowledge, for example, thinking, learning, and critical thinking.

AI is a man-made consciousness-based method for creating PC frameworks that learn and advance dependent on experience. Some basic AI applications incorporate working self-driving autos, overseeing speculation reserves, performing legitimate disclosure, making therapeutic analyses, and assessing inventive work. A few machines are in any event, being educated to mess around.

Man-made intelligence and MACHINE LEARNING isn’t the eventual fate of innovation — it’s nowhere. Simply see how voice aides like Google’s Home and Amazon’s Alexa have turned out to be increasingly more unmistakable in our lives. This will just proceed as they adapt more aptitudes and organizations work out their associated gadget biological systems. The accompanying can be viewed as a portion of the significant advancements in the field of AI.

Artificial intelligence in Banking and Payments

This report features which applications in banking and installments are most developed for AI. It offers models where monetary organizations (FIs) and installments firms are as of now utilizing the innovation, talks about how they should approach actualizing it, and gives depictions of merchants of various AI-based arrangements that they might need to think about utilizing.

Computer-based intelligence in E-Commerce

This report diagrams the various uses of AI in retail and gives contextual analyses of how retailers are increasing a focused edge utilizing this innovation. Applications incorporate customizing on the web interfaces, fitting item suggestions, expanding the hunt significance, and giving better client support.

Computer-based intelligence in Supply Chain and Logistics

This report subtleties the variables driving AI appropriation in-store network and coordinations, and looks at how this innovation can decrease expenses and sending times for activities. It likewise clarifies the numerous difficulties organizations face actualizing these sorts of arrangements in their store network and coordinations tasks to receive the rewards of this transformational innovation.

Artificial intelligence in Marketing

This report talks about the top use cases for AI in advertising and looks at those with the best potential in the following couple of years. It stalls how promoting will develop as AI robotizes medicinal undertakings, and investigates how client experience is winding up increasingly customized, pertinent, and auspicious with AI.

CONCLUSION

To close, AI introduces a colossal open door for venturesome individuals. Representatives have the chance to jump into another field and conceptual their business to another, more significant level of investigation and vital worth. Businesses need to help these moves and for the most part remain open to representatives rethinking themselves as they hold onto innovations, for example, AI.

Virtual Assistants - Alexa, Siri, Google Assistant

Siri was introduced as a feature of the iPhone 4S in 2010. While it could only answer simple questions such as “What’s today’s weather like?” and “Who is Barack Obama?”, users praised the potential of the new voice assistant. Quite a feat for that time for a virtual assistant.

Expectations were high, and Siri fell short. Users complained about inaccurate responses to simple questions or commands. If Siri didn’t know the answer to a question, she’d crack a bad joke, which can seem like an unacceptable excuse for not having the ability to answer a question.

While Apple made improvements to its voice assistant, it wasn’t able to meet a lot of high expectations, and that frustrated users.

Alexa

Three years later, Amazon introduced its own voice assistant named Alexa, and it was instantly pitted against Apple’s alternative. Users observed that Alexa was quicker with responses, and was answering more questions right than wrong. Alexa fell short next to Siri when it comes to the fluidity and flow of requests and conversations. Siri could respond to commands better, and it had no problems understanding multiple sentence structures that conveyed the same message.

In 2016, Google came out with an answer to Siri and Alexa in the form of Google Assistant. It became the gold standard for how natural language processing (NLP) should be implemented with a voice assistant. The drawback of Google Home was that it didn’t have the broad integrations that Alexa had with Amazon’s devices.

These three voice assistants are the most popular in the market and each of them has their own strengths and weakness. But, how exactly do they stand against each other? 

The main tests we will conduct for these voice assistants are commands, conversation flow, music requests, home automation, and technology. MKBHD and Undecided with Matt Farell have given us interesting demonstrations and questions that can be used to test each of these three voice assistants. Let’s compare them using the following parameters:

Commands

Voice assistants started off as devices that could answer simple questions such as the time and the weather. Accuracy of response is key here and speed is an additional bonus.

What’s the weather?

Siri, Alexa, and Google Home have no problem answering this. Google tends to have a slight delay in its response generally, but nothing that could test a user’s patience.

How far away is London?

Siri and Google answered this right in miles as the crow flies, while Alexa provided an inaccurate response, or the answer to a different London (there are 29 places in the world called London). 

Conversation Flow 

When humans have conversations, the talking points build naturally and flow from one topic to another seamlessly. For a voice assistant, understanding context while having a conversation is key. 

Conversation

The following questions were asked one after the other to each voice assistant separately.

Who is the 45th President of the United States?

All three voice assistants provide the right answer. Siri cites the source and asks users if they’d like more information.

Where is he from?

When asked immediately after the previous question, Siri and Google fail. Alexa seems to handle context better than its two competitors.

Music

Since all voice assistants communicate with speakers, they need to understand song, artist and album requests. But before we get into their ability to play a track on-demand, its important to note that each voice assistant only plays music from a select set of streaming services. Alexa wins here as it plays from most major services. Google works only with Google Play Music, YouTube Music, Spotify, and Deezer. And Siri, not surprisingly, only plays from Apple Music.

Play Get Lucky by Daft Punk

Simple task. No losers here.

Play the song that goes “like the legend of the phoenix”

Alexa fails here while Siri and Google Assistant get it right.

Home Automation

Home automation refers to command-based control over home appliances such as fans, den lights, television, heaters, etc. Here’s how the voice assistants fared with the following two questions.

Turn off the den lights

All assistants successfully turned the lights off. 

Set the room temperature to 70 F

Google Assistant and Siri got this right, while Alexa adjusted the room temperature to a value between 65 and 70. 

Technology

Siri primarily works on Natural Language Processing (NLP) integrated with Machine Learning (ML), and voice recognition. Alexa operates on similar tech such as Automated Speech Recognition (ASR), and Natural Language Understanding (NLU). The technology isn’t too different from google either, its voice assistant employs NLP and ML.

Yes, the three voice assistants use ML and NLP to understand what the user is saying and to make suggestions or respond to the user’s language input. While the primary technology is the same or at least similar, the end result is what separates the three. As observed in the tasks assigned to them earlier, certain aspects of each voice assistant’s tech, such as the ability to understand speech patterns and words,  give them an advantage and a disadvantage.

Conclusion

The aim isn’t to be diplomatic, but there isn’t exactly a winner among the three. All the voice assistants can, for the most part, do the same things. Alexa has the largest home-integration options among the three, while Google Assistant and Siri are a lot more natural to talk to. 

Virtual Assistants

If you’re big on home automation and having wide music streaming options, Alexa is the voice assistant for you.

If you find yourself comfortable with Google’s streaming services such as Google Music and Youtube, Google Assistant is a smart pick. It also comes with a formidable range of home automation.

And finally, if your household is equipped with Apple’s products, it’s a no brainer to pick Siri, who’s device also has the best speakers among the three. Siri also has an advantage concerning privacy, as it encrypts all data, unlike its competitors that use it for targetted ad campaigns.

As a consumer, your goal is to see which one of these fits your requirement and aligns with what you’re looking for from a voice assistant.

Machine Learning

What is Machine Learning?

Machine learning (ML) is fundamentally a subset of artificial intelligence (AI) that allows the machine to learn automatically. No explicit programs are needed instead of coding you gather data and feed it to the generic algorithm. It is a scientific study of algorithms and statistical models used by computers to perform specific tasks.

The machine builds a logic based on that data. It can access data and teach itself from various instructions, interactions, and queries resolved. ML forms data patterns that help in making better decisions. The machines learn without human interference even in fields where developing a conventional algorithm is not workable. ML includes data mining, data analysis to perform predictive analytics.

Machine learning facilitates the analysis of substantial quantities of data. It can identify profitable opportunities, risks, returns and much more at a very high speed and accuracy. Costs and resources are involved in training the agent to process large volumes of information gathered.

Working of Machine Learning:

Machine Learning algorithm obtains skill by using the training data and develops the ability to work on various tasks. It uses data for accurate predictions. If the results are not satisfactory, we can request it to produce other alternative suggestions. ML can have supervised, semi-supervised, unsupervised or reinforcement learning.

Supervised learning is the machine is trained by the dataset to predict and take decisions. The machine applies this logic to the new data automatically once learned. The system can even suggest new input after adequate training and can even compare the actual output with the intended output. This model learns through observations, corrects the errors by altering the algorithm. The model itself finds the patterns and relationships in the dataset to label the data. It finds structures in the data to form a cluster based on its patterns and uses to increase predictability.

Semi-supervised learning uses labeled and unlabelled data for the training purpose. This is partly supervised machine learning, and it considers labeled data in small quantities and unlabelled data in large quantities. The systems can improve the learning accuracy using this method. If the companies have acquired and labeled data; have skilled and relevant resources in order to train it or learn from it they choose semi-supervised learning.

Unsupervised machine learning algorithms are useful when the information used to train is not classified or labeled. Studies that include unsupervised learning prove how systems can conclude a function to depict a hidden structure from the unlabelled data. The system explores data supposition to describe the obscure structures from the unlabelled data.

Reinforcement machine learning, these algorithms can interact with its environment by generating actions. It can find the best outcome from some trial and errors and the agent earns reward or penalty points to maximize its performance. The model trains itself to predict the new data presented. The reinforcement signal is a must for the agent to find out the best action from the ones its suggestions.

Future of ML

Evolution of Machine Learning:

Machine learning has evolved over a period and experiences continuous growth. It developed the pattern recognition and non-programmed automated learning of computers to perform simple and complex tasks. Initially, the researchers were curious about whether computers can learn with the least human intervention just with the help of data. The machines learn from the previous methods of computations, statistical analysis and can repeat the process for other datasets. It can recommend the users for the product and services, respond to FAQs, notify for subjects of your choice, and even detect fraud.

Machine Learning as of today:

Machine Learning has gained popularity for its data processing and self-learning capacity. It is involved in technological advancements and its contribution to human life is noteworthy. E.g. Self-driving vehicles, robots, chatbots in the service industry and innovative solutions in many fields.

Currently, ML is widely used in :

1. Image Recognition: ML algorithms detect and recognize objects, human faces, locations and help in image search. Facial recognition is widely used in mobile applications such as time punching apps, photo editing apps, chats, and other apps where user authentication is mandatory.

2. Image Processing: Machine learning conducts an autonomous vision useful to improve imaging and computer vision systems. It can compress images and these formats can save storage space, transmit faster. It maintains the quality of images and videos.

3. Data Insights: The automation, digitization, and various AI tools used by the systems provide insights based on an organization’s data. These insights can be standard or customized as per the business need.

4. Market Price: ML helps retailers to collect information about the product, its features, its price, promotions applied, and other important comparatives from various sources, in real-time. Machines convert the information to a usable format, tested with internal and external data sources, and the summary is displayed on the user dashboard. The comparisons and recommendations help in making accurate and beneficial decisions for the business.

5. User Personalisation: It is one of the customer retention tactic used in all the sectors. Customer expectations and company offerings have a commercial aspect attached; hence, personalization is introduced on a wide variety of forms. ML processes massive data of customers such as their internet search, personal information, social media interactions, and preferences stored by the users. It helps companies increase the probability of conversion and profitability with reduced efforts with ML technology. It can help branding, marketing, business growth and improve performance.

6. Healthcare Industry: Machine learning assists to improve healthcare service quality; reduce costs, and increase satisfaction. ML can assist medical professionals by searching the relevant data facts and suggest the latest treatments available for such illnesses. It can suggest the precautionary measures to the patient for better healthcare. AI can maintain patient data and use it as a reference for critical cases in hospitals across the globe. The machines can analyze images of MRI or CT Scan, process clinical procedures videos, check laboratory results, sort patient information and use efficiently. ML algorithms can even identify skin cancer and cancerous tumors by studying mammograms.

7. Wearables: These wearables are changing patient care, with strong monitoring of health as a precaution or prevention of illness. They track the heart rate, pulse rate, oxygen consumption by the muscles and blood sugar level in real-time. It can reduce the chances of heart attack or injury, and can recommend the user for medicine dose, health check-up, type of treatment, and help the faster recovery of the patient. With an enormous amount of data that gets generated in healthcare, the reliance on machine learning is unavoidable.

8. Advanced cybersecurity: Security of data, logins, and personal information, bank and payment details is necessary. The estimated losses that organizations face because of cybercrime are likely to reach $6 trillion yearly. Threat is raising the cybersecurity costs and increasing the burden on the operational expenses of organizations. The ML implementation protects user data, their credentials, saves from phishing attacks and maintains privacy.

9. Content Management: The users can see sensible content on their social media platforms. The companies can draw the attention of the target audience and it reduces their marketing and advertising costs. Based on human interactions these machines can show relevant content.

10. Smart Homes: ML does all mundane tasks for you, maintaining the monthly grocery, cleaning material, and regular purchase lists. It can update the list when there are input and order material on the scheduled date. It increases the security at home by keeping the track of known visitors and barring the other from entering the premise or specifies suspicious activities.

11. Logistics: Machine learning can keep track of the user’s choices for delivery and can suggest based on the instructions and addresses they use often. The confirmations, notifications, and feedback about the delivery is processed by the machines more efficiently and in real-time.

Future of ML:

Do not be surprised if we are found learning dance, music, martial arts, and academic subjects from the Bots. We will shortly experience improved services in travel, healthcare, cybersecurity, and many other industries as the algorithms can run throughout with no break, unlike humans. They not only deal but respond and collect feedback in real-time.

Researchers are developing innovative ways of implementing machine-learning models to detect fraud, defend cyberattacks. The future of transportation is great with the wide-scale adoption of autonomous vehicles.

The voice, sound, image, and face recognition, NLP is creating a better understanding of customer requirements and can serve better through machine learning.

Autonomous Vehicles like self-driving cars can reduce traffic-related problems like accidents and keep the driver safe in case of a mishap. ML is developing powerful technologies to let us operate these autonomous vehicles with ease and confidence. The sensors use the data points to form algorithms that can lead to safe driving.

Deeper personalization is possible with ML as it highlights the possibilities of improvement. The advertisements will be of user choice as more data is available from the collective response of each user for the text or video they see.

The future will simplify the machine learning by extracting data from the devices directly instead of asking the user to fill the choices. The vision processing lets the machine view and understands the images in order to take action.

You can now expect cost-effective and ingenious solutions that will alter your choices and change your set of expectations from the companies and products.

According to the survey by Univa 96% of companies think there will be outbursts in Machine Learning projects by 2020. Two out of ten companies have ML projects running in production. 93% of companies, which participated in the survey, have commenced ML projects. (344 Technology and IT professionals were part of the survey)

Approximately 64% of technology companies, 52% of the finance sector, 43% of healthcare, 31% of retail, telecommunications, and manufacturing companies are using ML and overall 16 industries are already using machine-learning processes.

Final Thoughts:

Machine Learning is building a new future that brings stability to the business and eases human life. Sales data analysis, streamlining data, mobile marketing, dynamic pricing, and personalization, fraud detection, and much more than the technology has already introduced, we will see new heights of technology.

Artificial Intelligence Applications

Artificial Intelligence is here to change the way humans interact with their world, and it’s poised to make life easier. Today, numerous applications of Artificial Intelligence for business solutions exist. From voice assistants playing music at our behest to phones unlocking themselves by viewing our faces, AI has shown us that the future is here.

AI is also here to make life simpler for employees and businesses. A lot of business processes are waiting to be automated, and data analytics is offering more insights than ever for decision making and identifying opportunities. AI can manage a company’s workflow and predict trends. 

There are a variety of applications for AI in business. Let’s do a rundown of the eight most popular ones:

Serve your customers better

Every business needs to keep its customers happy and satisfied. They also need to know how to empathize and deal with unhappy ones. A strong customer base is integral to a business’s success, and AI is making it easier to achieve this. 

Applications of Artificial Intelligence for business

Businesses can use conversational AI to provide a personalized platform for customer interaction. Customers love immediate responses, and research exists to back this up. Econsultancy reports that 79% of customers prefer to chat with a customer support rep to solve issues and queries.

Businesses can employ chatbots to make sure customers always have someone to go to instantly if and when there’s a problem. Chatbots can handle simple queries and lead customers to a human support representative if the issue is complex. 

Predict online behavior

Understanding online customer behavior is essential to e-commerce. Factors such as product clicks, bounce rate, purchases, etc. determine the success or failure of products sold by online businesses.

Applications of Artificial Intelligence for business

Data analytics allows online businesses to study the data that they’ve captured. It’s a great way to understand which products are helping the business and also the ones that need to be discontinued. New products can also be launched if certain product categories are proving to be popular.

Machine Learning algorithms can track user behavior on websites. With the information collected, businesses can personalize a customer’s experience. Customers could be shown products that they are likely to buy. 

Optimize workflow

Manufacturing businesses can make use of computer vision to monitor factory operations. Such technology can measure employee productivity and the efficiency of processes. Industrial robots can replace repetitive tasks or tasks that eliminate possible human error.

Improve physical checkouts

With the help of computer vision, retail stores can save customers a lot of time while checking out. Computer vision cameras across store premises can identify customers and the items they pic. Once customers are done picking what they require, the retailer can send an invoice online, thus avoiding any reasons to wait in a long queue.

Strengthen your cybersecurity efforts

 Every business has data that needs to be protected. They generally store this data on common/public infrastructure, which makes the data more prone to cybersecurity attacks. 

Applications of Artificial Intelligence for business

Businesses can employ AI/ML to strengthen their cybersecurity efforts. They can use ML to detect malicious activities in data storage systems and improve human analysis, from detecting attacks of a malicious nature to endpoint protection. Also, businesses can automate mundane tasks, thus allowing less room for human error due to fatigue, and more accurate results.

Market yourself with data

With the help of AI and ML, advertising campaigns can be planned with less subjectivity and more data-backed decision making. AI models that can analyze the most successful advertisement campaigns of the past are available in the market (IBM Watson, for example). These models can study advertisement parameters such as audiences, click rate, transaction rate, overall spend, etc. 

Applications of Artificial Intelligence for business

AI can also identify and segment audiences that are most likely to respond to a certain ad positively. By understanding their audiences, ads creatives, while subjective in nature, can be provided with an objective touch, to increase conversions.

Today, most brands use AI to prepare their ad campaigns. Using data, ads of the future can learn from the past to hack the future in their favor.

Detect fraud and anomalies

The banking industry is a sensitive one since issues in this field affect customers more than any other industry. Now that we’ve got Big Data, banks and financial firms can now access data on customer spending habits. So, if bank officials observe any anomalies in any transaction from a customer’s bank account, they can alert customers.

AI-inspired fraud detection applications review a customer’s social media, employment statistics, high school & college education, etc. to determine whether their expenditures and financial activities are in sync. Businesses can continuously update such applications as customer data change, thus more accurately determining what accounts for financial fraud.

Predict outages

To execute any strategy successfully, the resources that aid the execution process need to be abundant. Outages can slow down industry processes and hamstring operations. 

AI can monitor teams and their inventory to determine whether a plan will be executed on time or not. Teams can be alerted if new additions need to be made to their inventory and if any resources aren’t being used effectively.

Applications of Artificial Intelligence for business - outages

For example, in a factory setup, monitoring storage locations allows businesses to identify missing items and raw materials that need to be replaced or replenished. These raw materials are crucial to the final product’s creation, and AI can ensure that any possible hurdles are taken care of.

Conclusion

Despite AI being in its nascent stage, it has already proven to be a technological juggernaut. In business, AI can improve manufacturing processes, reduce financial fraud, and improve marketing campaigns, among many other applications as discussed above. 

With extraordinary leaps made in machine learning and computer vision, it will be interesting and exciting to see AI developers discover new applications. We will definitely update this piece once further applications of Artificial Intelligence for business present themselves.