Tag: Artificial Intelligence

Facial Recognition Technology – What’s In Store For The Future

When Facebook started automatically tagging faces in uploaded images, nobody realized that this facial recognition technology would hike up to tracking people down while walking on the streets. In the past several years, this disruptive technology has gained immense popularity, that it is now used everywhere, from airports to shopping centers, to law enforcement. With its growing predominance in national safety and security, the face recognition market is estimated to reach USD 11.30 Billion by 2026. 

“Facial recognition has been around for a long time—like the 1960s. Perhaps the father of facial recognition, Woodrow Wilson Bledsoe, an American mathematician and computer scientist who classified photos of faces all by hand, (RAND tablet), even he might have been alarmed at how facial recognition technology is supercharged today by advances in computing power, 5G speeds and AI paired with machine learning.”   

– Tamara McCleary, CEO of Thulium, and a unique advisor to leading global technology companies such as SAP, Dell, Oracle, IBM.

Moreover, the advancements in artificial intelligence and machine learning are bringing about an active expansion to this technology. It won’t be long when the automation of facial recognition technology will fundamentally change the way we do many things. However, many minds still doubt on the path this revolution is leading to. 

Let’s dig deeper into the advancements of the facial recognition technology, what it holds for the future and whether it’s completely safe to rely on such a disruptive technology that fiddles with personal identities.

Facial Recognition Technology In-Depth

So what is facial recognition technology and how exactly does it work?

Facial recognition is a biometric technology that utilizes unique facial features to recognize individuals. Today’s plethora of innumerable photos and videos make the dataset for this technology to work. Through artificial intelligence and machine learning capabilities, software mathematically maps distinguishable facial features, to compare patterns in newly available images with visual data stored in the database. Such a recognition process allows the simple unlocking of phones to security checks at airports. 

In a way, artificial intelligence plays a vital role in the complete identity recognition process. A branch of artificial intelligence known as computer vision works through measuring nodal points on a face to make a face-print. This faceprint is a unique code that is applicable only to a particular person. This enables identification.

“I believe AI in Facial Recognition could add great value to society but we have to be careful to use clean data and we have to educate the public for the need for good, clean, accurate data to be sure we do not accidentally disenfranchise certain groups even more in the future. We must assure the data does not include unconscious bias or even deliberate bias programmed into the code. It is also important to note that we have a major lack of data for many disenfranchised groups including the community of persons with disabilities.”

– Debra Ruh, CEO, Ruh Global IMPACT, Global Disability, and Aging Inclusion Strategist.

Once this faceprint is made, the technology runs through an identity database to match this face with a name and other required details. Thus, the probability of error is near to rare; maybe an eight out of 1000 scans could mistakenly identify the person. This is what makes this technology an excellent prospect for performing crucial functions.

Read more: How Fingent helped develop a unique mixed reality application for a leading university to identify people using facial recognition

Innovative Uses Of Facial Recognition Technology

As facial recognition technology evolves with time, few industries and countries apply the technology in innovative ways.

China is rising to be the leader in facial recognition technology. Although part of the technology remains a perspective, its innovative use is what amazes the audience. A few other countries following the trend are Japan and the United Arab Emirates. The US doesn’t stand back either. Look at these impressive ways of face recognition technology implementation.

• Face recognition is on its go, replacing cash and credit cards. At fast-food units like KFC, customers can just smile into a self-serve screen to automate the identification and withdrawal of cash from banks. Some banks are also allowing customers to use face recognition instead of bank cards.
• The automobile brand Subaru has integrated facial recognition cameras to its Forester brand of SUVs. This is intended to detect when a driver is tired or about to sleep to take necessary actions to prevent accidents. This indeed is a tremendous innovation towards road safety.
• The 2020 Tokyo Olympics, is reported to make use of facial recognition to boost their security systems. Instead of relying on ID cards that have a high probability of being fake, the authorization is now implementing the FR technology to allow media, competitors or other such people to enter the premises.
• Dubai Airport also makes use of the FR technology to strengthen their security. A virtual aquarium fitted with 80 facial recognition cameras examines every passerby to easily recognize criminals or offenders. Also, police cars are on their go-to implement FR cameras to identify criminals and wanted vehicles quickly.
• Facial recognition technology is no doubt making a great impact on national security systems, promising a safe and crime-free future. The US government is also making use of biometric exits and AI cameras to track people crossing their international boundaries without proper documents. 

The Growing Concern

Though face recognition technology offers innovative and impressive use cases in security and surveillance, there are numerous challenges that it faces. Privacy being a major concern, not everybody is happy with the storage of sensitive and personal data. A potential downside of this technology is the data and privacy breaches. The databases containing facial scans and identities are being used by multiple parties such as banks, police forces, and other defense firms and are hence prone to misuse. 

Considering the face recognition tech as a threat to their citizens’ privacy, many cities including San Francisco, Massachusetts, Cambridge, and others are planning to put a complete ban on real-time face recognition surveillance. 

“Concerns around AI’s practical applications like facial recognition have begun crystallizing over the last few years and will continue unabated. Current AI-based face recognition systems possess a grave threat to individual privacy, which if unregulated may end up jeopardizing sensitive user data to the wrong hands in times to come.” 

– Varghese Samuel, CEO & MD Fingent.

Moreover, how much can this technology eliminate crime is still being discussed. The accuracy of the system in detecting people who cover their faces from cameras or disguise themselves is yet a topic of dispute. However, to everyone’s relief, the technology is showing constant improvement in this matter. According to the U.S. National Institute of Standards and Technology (NIST), facial recognition systems got 20 times better at finding a match in a database over a period that covered 2014 to 2018.

“Artificial intelligence has made great strides, but still has a long way to go. It is powerful to use on a daily basis, when the stakes are low (for example, in tagging photos or recommending advertisements), but not yet trustworthy enough to stand fully on its own in high-stakes applications, such as driverless cars, medical diagnosis, and face recognition, where errors can deeply affect people’s lives. 

– Gary Marcus, Founder and CEO, Robust.AI Professor Emeritus, New York University, Author of book: REBOOTING AI

The Untold Future

It is pretty much tough to predict where the facial recognition technology would be in the coming years, but the increase in AI advancements is sure to widespread this technology around the globe. Major industries have already capacitated the FR capabilities to replace the traditional process of paying bills, opening bank accounts, checking controls at airports, and such. A few of these industries include healthcare, retail, marketing, and social media platforms. 

In a nutshell, face recognition technology is expected to predominate the globe in the near future. The increasing usage of mobile devices and demand for robust fraud detection and prevention is predicted to majorly drive the implementation of this technology. As per the predictions made by Markets and Markets, a prominent research firm, the global facial recognition market size is expected to grow from USD 3.2 billion in 2019 to USD 7.0 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 16.6% during 2019–2024.

“The more people grow accustomed to using facial recognition products and services that enhance efficiency and that can, at the moment, seem altogether too fun or mundane to be harmful — whether it’s tagging photos, unlocking a phone, or projecting how your face might look in the future — the more facial recognition technology becomes normalized.”

– Jarno M. Koponen, Head of AI & Personalization at Yle News Lab. His work has been covered by The New York Times, New Scientist, Oxford Reuters Institute, Mashable, TechCrunch.

Face recognition technology is revolutionizing the world more than you think. It’s time to figure out how this technology could bring added value to your firm. Contact our experts today!

How to Attain More Business Value with AI Implementation

The potential of Artificial Intelligence (AI) is being harnessed by businesses to implement automation across several industries and to augment their efforts to provide stellar customer experiences. The incomparable precision, speed, and accuracy of AI-enabled solutions are driving an AI revolution and this blog will discuss five specific ways you can attain business value with AI adoption.

AI: A new approach to customer experience

IBM, one of the leading universal AI champions reveals that 74% of customer experience executives feel that AI will change how customers view their brand. AI provides customers with an experience that appears very similar to human-like interaction. AI-powered apps are highly efficient in enhancing customer interactions. 

Automation of “the customer experience journey” through AI enables your business to personalize your marketing campaigns, which will boost the email opening rate. Among other things, AI provides improved customer insights and better customer satisfaction which results in increased conversion and strengthens brand loyalty. 

Related Reading: How AI is Redefining the Future of Customer Service 

Steps to Gain Business Value with AI Adoption

A study from Market Research Engine estimates that the Artificial Intelligence Market is expected to exceed more than US$ 191 Billion by 2024. The vast growth of information in huge quantities, growth in the adoption of cloud-based applications and services, and an increase in the demand for intelligent virtual/ personal assistants are the major driving factors behind the booming AI market. 

Unfortunately, many are still unsure of how to adopt AI, and how it can be used to yield maximum results. We present you these 5 steps to adopt AI which will help you gain the maximum business advantage. 

1. Map out a clear customer-centric strategy

The best way to do this is to analyze recent customer journeys with your brands such as discovery, presales, sales, and customer service. Such analysis will help you understand the experience your customers are having with your brand. Research director at Gartner, Olive Huang said, “Your business results depend on your brand’s ability to retain and add customers.” Hence it’s important to deliver a highly personalized experience to every one of your customers. Once that strategy is crafted, it can be delivered through AI.

Related Reading: 6 Ways Artificial Intelligence Is Driving Decision Making

2. Diagnose the problem

Just as the prudent diagnosis of a disease is crucial to helping a patient improve their health, finding out your specific business problems will help you use AI to improve business value. Organizations need to be clear about what AI can help them accomplish. Once the problem is identified, aligning AI to business priorities becomes easier. The beauty of AI algorithms is that once algorithms are enabled to solve one aspect of the business successfully, it can easily be adapted for use in other aspects of the business too.

3. Establish a governance program for customer experience

Before you begin your journey with customer experience, make sure to establish a governance program. The benefit of having a central team working on the AI project is that you can integrate program oversight with a complete comprehension of AI-related initiatives. This is true even when your customer experience teams are just in the learning process. Build a program to supervise development activities and business implications and set brand guidelines for AI technologies such as NLP. Also, establish standards to gauge the impact of customer experience initiatives and its correlation with ROI.

4. Bring the best onboard

Creating a custom AI solution is recommended over buying it readymade. It is important however that you bring in the best talent. Bringing in a team of dynamic people who can create exciting ways to engage with the customers sends a powerful message to your competitors. It will also put your organization on the radar as a tech-savvy innovator. 

Read our latest white paper: How Could Your Business Use AI to Achieve Higher Profits & Growth

5. Prepare to store

A business that fails to look into the future fails to grow. Gathering relevant data allows organizations to derive greater benefits far into the future. Meaningful data help AI systems in achieving your organization’s objectives. Insufficient data could compromise the accuracy of AI applications, so get your team to build relevant data including cases and codes.

Drive Business Value With AI 

Organizations are adopting AI faster than anticipated. To gain long-term and sustainable business value from Artificial Intelligence, you need to develop a robust implementation approach that takes into its fold, even the minute aspects of your enterprise. Contact us to adopt the power of AI into your business.  

How AI and Machine Learning are Driving Cyber Security in FinTech?

Being a subset of the financial services domain, FinTech is targeted by hostile cyber villains. Industries thus require secure mechanisms to keep their data safe and secure. Preventing data losses are critical for Fintechs. 

The World Economic Forum states that cyber-security is the Number One risk associated with the financial services industry.   

Cyber Security Risks Associated With FinTech

Cybersecurity has remained a pressing concern for FinTech. Ever since the global financial crisis of 2008 that challenged the traditional financial institutions significantly, technology-driven start-ups have started evolving increasingly to cater to finance, risk management, digital investments, data security, and so on. Presently, we are in the FinTech 4.0 era. 

The major cybersecurity risk that enterprises implementing FinTech face are from integration issues such as data privacy, legacy, compatibility, etc. Hackers target FinTech as they handle large volumes of customer data that include personal, financial, and other critical information.

FinTech offers a multitude of easily accessible services via its APIs. For instance, API banking. Here, the APIs are developed for the banks to access the FinTech platforms. It becomes open, API banking when open APIs enable third-party developers to build banking applications and services. 

Let us walk through the major cybersecurity challenges triggered by FinTech.

• Data Integrity Challenge

Mobile applications deployed for FinTech services play a predominant role in cybersecurity assurance. FinTech services require strong encryption algorithms to avoid integrity issues that can arise while transferring financial data. 

• Cloud Environment Security Challenge

Cloud computing services such as Payment Gateways, Digital Wallets including other secure online payment solutions are key enablers of the FinTech ecosystem. Though it is simple to make payments via cloud computing, it is equally crucial to maintain the security of data as far as banks are concerned. Appropriate cloud security measures are thus critical while dealing with sensitive information.

• Third-Party Security Challenge

Third-party security challenges include data leakage, service challenges, litigation damages, and so on. Banks should be aware of FinTech service relationships while associating with third-parties. 

• Digital Identity Challenges

Major FinTech applications are web apps that have mobile devices working at the front-end. Banks and other financial institutions need to learn about the security architecture of the electronic banking services offered by these applications before implementing the FinTech application.

• Money Laundering Challenges

The use of cryptocurrency for financial transactions makes FinTech-drive banks prone to money laundering activities. Thus, the FinTech ecosystem needs to be formally regulated based on global standards.

• Blockchain Challenges

Private keys can be stolen in case of weak security in blockchain architecture. Cryptographic algorithms need to be strong and transactions need to be confidential.  

The increase in the number of FinTech implementation of interfaces will cause a rise in the number of cybersecurity challenges as well. 

Can Machine Learning Predict And Prevent Fraudsters?

How Artificial Intelligence And Machine Learning Enables Cyber Security For FinTech?

Artificial Intelligence is both reactive as well as proactive or preventative. AI reinvents FinTechs by bringing in behavioral biometrics solutions. These solutions are used to monitor customer and device interactions that take place during transactions that enhance security and authentication. BB or behavioral Biometrics with AI provides problem-solving capabilities for FinTechs. FinTechs utilize Artificial Intelligence is an expert system that enhances decision-making abilities using deductive reasoning. Big Data analytics is used here to focus on quality data. 

The underlying technology in using Artificial Intelligence involves reasoning, learning, perception, problem solving, and linguistic intelligence to provide critical insights. It helps in understanding business in real-time operations. 

In this digital era of increasing cybersecurity attacks and malpractices, AI can be used effectively to prevent risks and attacks. The following are major ways of how AI and ML protect FinTechs:

1. Fraud Detection

AI and machine learning algorithms are used to detect frauds in FinTechs by being able to identify transactions in real-time accurately. The traditional strategy of fraud detection involved analyzing large volumes of data against sets of defined rules using computers. This process was time-consuming and complex. Unlike this traditional method, more intelligent data analytics tools for fraud detection such as KDD (Knowledge Discovery In Databases), Pattern Recognition, Neural Networks, Machine Learning, Statistics, and Data Mining have evolved. 

2. Controlling Access

Access control to critical data is crucial when it comes to security. Machine learning is used to derive critical insights from previous behavioral patterns such as geolocation, log-in time, etc to control access to endpoints. The risk scores are fine-tuned by combining supervised and unsupervised machine learning methods to reduce fraud and thwart breach attempts as well. 

3. Smart Contracts

Smart contracts are coded in a programming language and stored on the blockchain. With blockchain, new contracts can be added to existing ones without having to change the individual contracts, in case of agreement expansion. Artificial Intelligence has become an integral part of FinTech as more traditional banks are teaming up with FinTechs to leverage the benefits of both worlds. For instance, when customers face issues with a poor credit history while applying for loans. 

Artificial Intelligence is yet to be transforming the face of FinTechs in a multitude of ways. Drop-in a call right away and our strategists will guide you on how to leverage the benefits of AI and ML to secure operations and prevent breach attacks.

How Chat Bots Can Enhance Student Onboarding

Conversational interfaces have gone mainstream. The technology behind keeps crossing new milestones, the result of which chatbots have transformed from simple Q&A systems to intelligent personal assistants. As a result, bots found widespread application in diverse areas, most recently in education. 

Although education stayed backward in terms of technology adoption, lately it took on a renewed quest to incorporate it. Educators are on the lookout for innovative ed-tech systems for efficient tutoring and students increasingly prefer personalized learning environments. 

Deploying chatbots at numerous front-ends like college/university websites, internal student communication portals or even popular instant messaging platforms can help with that. Here’s how? 

Chatbots bring in a personalized and engaging learning experience optimized to the learning pace of each learner, which actively drives student-centered learning at the forefront. Configuring a bot to answer student inquiries related to curriculum, courses, admissions, etc. as well as deliver learning resources on request makes way for a personal always available assistant that every student can engage with. 

That’s exactly what we did, though in a different way. 

Recently Fingent was approached by a client, a leading public research university based in Australia to develop an intelligent chatbot for assisting prospective and freshly enrolled candidates with onboarding and orientation, course-related information, credit scores, etc. The client wanted to streamline its entire student orientation process using a chatbot and make all related information better accessible and context-based as well as systematically tackle the ‘summer melt’ rates. 

Here, we lay down a high-level abstract of this chatbot development experience powered by IBM Watson Assistant and backed by .NET Core. It briefs various facets and challenges faced during the design and development of the system.

The Plot (Objective)

“Build a chatbot to assist candidates during the orientation process of Monash University. The chatbot should be capable of handling different context-based scenarios such as listing available courses, providing credit score information, course structure, projects associated with each course and many more.“

Since it is a Proof of Concept (POC) project, and Monash University offers a wide range of courses based on various areas of education, the team decided to choose one particular area and focus on only two of the selected courses (Bachelor of Accounting & Bachelor of Actuarial Science). This is to repress the scope in control, considering the timespan and resource availability.

Foundation

Keeping in mind the idea of building a highly sophisticated chatbot, an ideal and matured chatbot assistant technology had to be finalized, which provides both comprehensive user intent identification and processing as well as a satisfactory response according to the user query. The system should also provide an extensive and less technicality included training interface. The hunt for such a tech ended up in IBM Watson Assistant.

Terminologies

The world of chatbots has some common terms which are essential key knowledge required while developing a chatbot. We can call them as the pillars of a chatbot.

• #Intent – Intent is nothing but the user’s intention in a query – basically covers all types of questions and their varieties, the user probably may ask. This can be queries within the scope or related to the scope.

Examples:

“What are all the courses available?”
– Intent associated: #KnowCourseInfo

“How much credit I require in the first semester?”
– Intent associated: #KnowCreditInfo

Remarks – There will be some stock #intent collection depending upon the chatbot engine, which is designed to handle the general greetings and conversation-oriented chunks. We can import or enable the intents as we want to make our chatbot more conversational and human-friendly.

• @Entity – An entity is a subject addressed in the user query. There are mainly two categories of entities. They are Scope-based entities and System entities. Scope-based entities are entities that belong to the scope we address whereas System entities are “primitive system-aware” entities.

Examples:

“What are all the courses available?”
– Entities associated: @Course

“How much credit I require in the 1st semester?”
– Entities associated: @Credit, @Semester, @system_number:1st

Remarks – On diving deeper, we may need the support of multiple types of scope-based entities and a system-aware way of specifying the relationship between the entities (which lacks in IBM Watson Assistant). This is to specify the entity characteristics as more descriptive as well as with the notion of “the system knows” the given attributes and relationships of an entity.

• Dialog – A dialog is a declarative way of specifying the possible questions the user may ask, and how should the bot respond to the corresponding questions. Generally, this will be a tree-based structure, rooted in the key user intentions and scope covered features. We will be handling the different scenarios of a single #intent as well as the edge cases.
• $Context Variable – A context variable is to store information, collecting from a dialog context or it can be any information related to the dialog context. It helps us to keep the dialogue context and facilitates conversational flow.
• Skill/Workspace (IBM Watson based) – A skill is a package that consists of the above-mentioned factors, in which all are aligned into a single chatbot capability, in our case, it was Onboarding skill.

Implementation

The entire development process streamlined into two major sections. The first one is aligned to the chatbot engine intelligence building and improvement activities while the other one is for the middleware and UI development.

1. Intelligence Build-up on top of IBM Watson Assistant

• Analyzed the requirements and fixed the boundaries of the scope. It includes what all are the functional areas to be covered by the proposed chatbots.
• Prepared the possible user queries and categorized them as #intents.
• Identified the underlying @entities in each question and classified them to form the actual set of primitive entities.
• Designed the dialog structure based on the prepared user query sets. See the resources: Intent structure and Dialogue flow. 

Fig 1. Intent Structure

• Continuously refined the dialogue structure based on detecting each edge cases and to incorporate new scenarios.
• Used some conventions on responses to extend the chatbot response capabilities, according to the requirements. This is to handle specific use cases such as clickable action list image response, map response, and show a list of items.
• Implemented WebHooks (IBM Watson based) to talk to external APIs to fetch the values for a dialogue node as well as validating user input (Not a comprehensive solution).

2. Middleware and UI Development

• Built a middleware backed by .NET Core with an intention to plug any chatbot service to the UI module. In fact, it is designed as a standard-framework to separate the chatbot logic from the application logic. This enables hassle-free maintenance of the app logic, code reusability, and extensibility.
• Built the UI using Angular to provide a sophisticated face for our chatbots.

Fig 2. Dayton Interface

Also, we built a diagnostics module, as part of the UI, which provides the service configuration information and session-based transcripts of conversations held with the chatbot.

Fig 3. The architecture of the Chatbot Middleware Application, Source Code

Challenges

During the development, we came across some development challenges with IBM Watson, which are listed below.

1. Unable to map relationships between entities. Due to this limitation, we were unable to link and pull the related values of the entities.
2. Conflicts between various entity values (Solved partially via entity split-up method)
3. API Limitations to manage chatbots dialog schema
4. IBM Watson doesn’t provide active learning, the self-learning capability to learn from user conversation sessions.
5. It also doesn’t provide an efficient way to talk to external APIs. Only one external API can be called, which leads to a bottle-neck on executing the webhook actions.
6. No built-in user input validation. This has to be done via WebHooks.

Capitalizing on AI Chatbots Will Redefine Your Business: Here’s How

Final Words

The application is now in a showcase/UAT (User Acceptance Testing) mode, also the refinement process being in progress. It has miles to go to reach the capability to converse with the user as a comprehensive onboarding assistant.

To know how chatbots can enhance your business growth, get in touch with our experts today!

Can Empowering AI and IoT Bring In Competitive Advantage To Industries?

It takes more than forward-thinking employees to gather customer purchasing trends and improve the customer experience. International companies depend on Artificial Intelligence (AI) and the Internet of Things (IoT) to drive data and forecast the next big wave of trends.

Studies predict Asia and North America to lead in the innovation of AI and IoT. Also, embedded AI in support of IoT smart objects will reach $4.6B globally by 2024.

Major vendors of IoT platforms such as IBM, Amazon, and Microsoft have started offering integrated AI capabilities like ML-based analytics. Scalable digital platforms are designed daily to understand the way customers think while using predictive maintenance in real estate, eCommerce, healthcare, and other industries.

It’s time for us to share the leading examples of how businesses use AI and IoT, and how these technologies benefit them.

AI and IoT: Leading Use Cases

Smart Cities: Making Life Easier

What happens when AI and IoT run a city? It turns into a smart city. Smart city technology can solve an energy crisis, help manage traffic, or improve the healthcare experience. 

One example of a smart city is the use of Advanced Transportation Controller technology linked to a 5G network in Los Angeles. There are road-surface sensors throughout the city, and cameras that monitor traffic, sending information to traffic management systems. Municipal employees can now analyze the data of traffic congestion and issues with traffic lights in high traffic areas. Overall, this improves the quality of living in Los Angeles and helps a business run smoothly without delays.

Convenience in Property Management

One of Fingent’s clients WRI Property Management, a US-based single-family rental provider with 10,000+ leased properties and 20,000+ managed houses experienced many challenges. Here are a few of the issues WRI Property Management faced:

• Tenant eviction
• Rent collection/accounting
• Scheduling property inspections
• Leasing properties
• Screening tenants

What happened next? Fingent introduced an advanced software platform, Honey Badger. The AI and IoT technology-supported WRI managers to conveniently communicate with multiple parties, renovating properties, view lives auction feed, track the construction of new properties, etc.

5G Network Vehicle Safety and Security

Machine Learning technology is improving the autonomous vehicle experience. How does it work? An automobile can stop when a driver is in dangerous tragic weather or unexpected situation. 

The 5G network can cause the brakes of a car to operate by tracking vehicle sensors of other drivers near prevent or relieve car crashes. 

The network can also send drivers a traffic update to use detours and avoid certain roads that are under construction or is unsafe.

AI and IoT Business Benefits 

1. Guaranteed Security and Safety

A company’s highest priority is protecting data in the workplace. As Artificial Intelligence scans security footage, IoT can close gates or doors if an intruder attempts to enter the premises of a head office. 

Organizations are now using machine-to-machine communication to determine potential security threats with an automated response to hackers or intruders. 

An example of AI and IoT in banking security is the detection of fraudulent activity in ATMs to communicate updates to law enforcement to protect customers.

The unexpected workplace accidents can be prevented by using sensors that monitor safety hazards as employees work. Employees at some organizations now wear wearable devices that alert the management of undetected dangers such as carbon monoxide released into the air on a work site. 

2. Convenient Shopping Automated Experience

Online shopping is more convenient than ever as websites personalize real-time suggestions to consumers based on a customer’s shopping history. As a result of this investment, Kinsta predicts that by 2021, Artificial Intelligence in e-commerce will increase sales to $4.5 billion from $2.3 billion in 2017.

3. Enhanced Healthcare Experience

NovitaCare, a Netherlands based healthcare company that treats patients with chronic and multiple disorders, wanted to improve the caregiver experience using an effective online platform. 

With Fingent’s help, NovitaCare now can communicate with non-profit organizations, patients, providers and researchers with an online platform that is HIPPA compliant.  

4. Simplified Management Of Supply Chain

The supply chain industry has experienced challenges in managing unexpected events that happen due to inaccurate forecasting. A solution to the problem is implementing AI and IoT. 

Supply Chain Digital recently stated the following about these technologies:

“Intel highlights that the world of IoT is growing rapidly, from 2 billion objects in 2006 to a projected 200 billion by 2020.” 

“AI is on most companies’ radars, with 78% of organizations implementing it to enhance operational efficiency by at least 10%.”

The use of real-time devices will feed data to executives to help create contingency plans for preventing unexpected challenges in the industry. As a result, the supply chain and a company’s reputation can experience fewer impacts.

A Guide for AI-Enhancing Your Existing Business Application

How Fingent Helps Businesses Achieve Success With AI and IoT?

Fingent has mastered the art of technology infrastructure to help companies resolve AI and IoT processes. As a result, it creates efficiencies in managing smart devices.

Implementing these technologies are small changes that can have a huge impact on your business. The ability to use raw data to understand customer behavior and forecast trends in the market can improve customer loyalty. Also, companies can track employees working in multiple departments and locations across the globe by partnering with Fingent.

Fingent is confident that AI and IoT work in your business context by delivering technologies to enable solutions in the cloud, networks and gateways, heterogeneous device support, systems capabilities, and data analytics. 

To Conclude 

Business Insider predicts that there “will be more than 64 billion IoT devices by 2025, up from about 10 billion in 2018.” 

Gartner observes that in three years (by 2020), more than 80 percent of enterprise IoT projects will incorporate at least one AI component. Artificial Intelligence and the Internet of Things is used to improve the safety of drivers on the road, enhance healthcare experiences, automate and streamline enterprise processes, stop intruders from hacking into IT systems or large organizations, and in numerous other ways. 

The combination of these technologies not only delivers a superior customer experience, but also forecasts what customers want in real-time, improves their experience of living in smart cities, maintains a high safety rating in challenging workplaces, and reinforces physical and cybersecurity. AI-IoT duo also avoids any unplanned downtime, increases operating efficiency, helps develop new products and services, and improves your risk management. 

Are you looking for an AI and IoT partner? Get in touch with Fingent experts today for a streamlined and error-free IoT implementation for your business.

How Is AI Changing the Game For HR?

With Artificial Intelligence (AI) making sweeping reforms in every aspect of a business, it comes as no surprise that Human Resources have discovered just how beneficial this technology can be. From talent acquisition to policy planning, payroll engagement efforts,  and reporting, AI recruitment is enabling businesses to save time and costs, increase productivity and improve accuracy.

This blog will discuss 3 ways in which AI has proved to be a game-changer in HR.

1. Selecting and Training Employees

Hiring the right employee and ensuring that he is a good fit in the organization is a fundamental tenet of success in business. Let’s consider the hiring process. AI recruitment can surpass biases, eliminate manual and machine limitations, and enhance the screening process by better identifying skills gaps and selecting candidates that have the highest potential for success.

Dr. Jeremy Nunn, Founder, and Director of Workmetrics, a leader in workforce software say: “Thanks to AI, organizations are able to better grade and rank qualifications during the screening process, develop candidate profiles, interact with job candidates and quickly reach out to the best ones before other companies take action.” Consider the hours of work that this will save for recruiters!  

AI can also enhance existing technologies and refine the application process. In an earlier blog, we had talked about how the Applicant Tracking System (ATS) is Transforming Hiring Norms. AI can further enhance these technologies in several ways. For example, instead of the ATS relying only on a set of manually entered keywords to qualify a resume, AI helps the system look beyond the keywords and understand the concept behind the requirement. Deep Learning, Sentiment Analysis, Predictive Analysis, and other AI concepts can be used to enhance the candidate screening and qualifying process. 

AI recruitment technology has also proved its mettle in the training and development of existing employees. It has the ability to get an overview of the entire workforce at once and automatically analyze the skills in each employee. It can then match these skills to the organization’s objectives and provide insights into specific areas for improvement and recommend the most effective training for each employee. 

2. Improve Employee Experience 

Improving the overall experiences of the employee with the company through every touchpoint is now made possible with AI technology. AI can enhance factors that affect an employee’s experience like compensation, communication, opportunities to grow and much more. 

Let’s consider Compensation Benchmarking for example. The traditional benchmarking strategy based on titles doesn’t cut it anymore, because the functions performed under a title is seldom the same across different companies or even across departments. AI recruitment overcomes this issue by processing large amounts of employee-level responsibility data and providing accurate comparisons between employees through machine learning models. This helps businesses identify and accurately compensate employees with similar responsibilities and caliber. 

Another important aspect of employee experience is access to information and communication. Discussing a survey by ServiceNow, their HR Evangelist & Transformation Leader Jennifer Stroud says: “30 percent of survey respondents said they want functionality that mimics Google to easily search for answers to their questions as well as policies and other critical company information.”

Conversational AI for the HR system will help in this regard. AI-powered programs including chatbots can be used to provide employees with instant answers to their questions right from the onboarding process. Access to basic training modules, guidelines on business ethics and conduct and other information can also be provided this way. Chatbots can govern access to documents based on the employee’s title and other factors. Natural language processing (NLP) has been vital in the success of conversational AI. 

Content personalization by using predictive analytics has also been instrumental in enhancing the employee experience by recommending the most suited professional development programs and career paths for each employee. 

3. Critical Decision Making

With data-backed insights and intelligent recommendation engines, AI can help solve persistent HR challenges. By aligning functions of HR to the overall business strategy and organizational goals, AI helps HR managers come to the right decisions that will be beneficial to the organization in the long run.  

In the context of strategic HR decisions, the role of AI in Manpower Planning and Productivity Management is gaining recognition. Let’s consider the measurement of productivity of the Sales Function as an example. Traditionally, this has been measured by the achievement of targets. This, however, rules out considerations of missed opportunity or optimal revenue extracted from the market. AI helps in this regard by providing an overall analysis of the market, the competition and runs comparisons with the skills, attitude, and effort of the sales professional. Drawing this data from CRM, accounting, Geo-position tracking, sales logs and more is no easy task but is achievable by AI. 

Transforming HR with AI

Although two-thirds of CEOs say AI will drive significant value in HR, only 11% of CHROs say that their organization has the skills to implement it. Don’t let your business be one of the 11%. Give us a call and let’s make AI happen for you. 

A Guide for AI-Enhancing Your Existing Business Application

How Artificial Intelligence Is Simplifying Business Decision Making?

Technology in 2019 is moving with the speed of light. Immense breakthroughs in the field of deep learning and machine learning have allowed machines to process and analyze information in ways that we could never have imagined. 

The role of Artificial Intelligence (AI) is noteworthy in this regard. One definition of AI is that it is “a collective term for computer systems that can sense their environment, think, learn, and take action in response to what they are sensing and their objectives.” This makes it a powerful tool, which when used the right way can radicalize decision making and completely changes the way we do business. This article discusses how AI achieves that. 

AI – A Boon to Business

AI includes the automation of cognitive and physical tasks. It helps people perform tasks faster and better and make better decisions. It enables the automation of decision making without human intervention. AI can enhance automation thus reducing intensive human labor and tedious tasks. There are many more ways in which AI is making a difference. With smart weather forecasting, for example, AI is bridging the gap between data scientists and climate scientists. This gives companies the opportunity to fight disaster with algorithms. 

The world is about to witness a great impact of AI on the economy and humans. According to McKinsey Global Institute’s research, AI could deliver an additional output of $13 trillion to the world economy by 2030, which would boost global GDP by nearly 1.2 percent a year. Acting as a capital-hybrid, AI can aid the growth of both the economy and humans. It will definitely have a revolutionary impact on the decision-making process. 

Top Artificial Intelligence Trends to Watch Out for In 2019

AI the Game Changer

From tarot cards to time machines and more, the quest of man to know the future has been relentless. The ability to make decisions based on a knowledge of the outcome is no more fantasy, however. AI has brought this to the realm of reality and has revolutionized business decision making.  

In the recent past, we have embraced analytics-driven decision making. Along with ever-increasing data storage and computing power, AI has the potential to augment human intelligence and enable smarter decision-making. AI could eliminate the huge costs of a wrong decision because it can practically eliminate human biases and errors. This could in turn speed up the decision-making process. The focus of the next few points is to highlight the ways in which AI can make a difference in business.

1. Marketing Decision-Making

In today’s customer-driven market complexities involved in decision making is increasing every day. This includes understanding customer needs and desires and aligning products to those needs and desires. A handle on changing customer behavior is vital to make the best marketing decisions. 

AI simulation and modeling techniques provide reliable insight into the consumers’ persona. This will help predict consumers’ behavior. Through real-time data gathering, trend analysis and forecasting, an AI system can help businesses make insightful marketing decisions.

2. CRM

Organizations can identify a consumer’s lifetime value with the help of AI’s buyer persona modeling. It can help organizations manage multiple inputs. During a complex decision-making process, AI can efficiently manage and control different factors at the same point in time. It can source and process large amounts of data within minutes while providing valuable business-based insights. While we humans face decision fatigue, algorithms do not have such limitations, which make AI-based decisions faster and better.

3. Recommender System

Recommender system (engine) is a technology that recommends products or other items to users. Although recommendation systems were initially used for music content sites, now it’s used has expanded to various industries. In this, an AI system learns a consumer’s preference based on ‘explicit’ or ‘implicit’ feedbacks. This information can help the organization reduce bounce rate and craft better customer-specific targeted content. 

4. Problem Solving 

An expert system is a kind of problem-solving software which tries to replicate the knowledge and reasoning methods of the experts. This system uses expert thinking processes to provide data, which includes assessment and recommendations for your problem. This makes it easier to make the right decision and respond swiftly when faced with issues and problems.

5. Opinion Mining

AI has been able to provide reliable insight to decision-makers. For example, in marketing, AI has provided businesses invaluable insight about consumers, which helps them enhance their communication with the consumers. It also helps retailers predict product demand and respond to it quickly. 

To that end, opinion mining helps businesses understand why people feel the way they feel. Most often a single customer’s concerns might be common among others. When sufficient opinions are gathered and analyzed correctly, the information gleaned will help organizations gauge and predict the concerns of the silent majority.  AI has improved this mining process through automation, which is quicker and more reliable, helping organizations in making critical business decisions. 

6. Augmented Analytics

According to a recent press release by Gartner, Augmented Analytics is going to be the next big trend which will transform the way analytics content is advanced, expended and shared. VP analyst at Gartner, Rita Sallam said, “The story of data and analytics keeps evolving, from supporting internal decision making to continuous intelligence, information products and appointing chief data officers.”

Wise business decisions are made when business executives and decision-makers have reliable data and recommendations. AI not only improves the performance of both the individual members of the team but also the competitive edge of the business.

Make Bigger, Faster, Better decisions with AI

Common sense and experience are no longer enough to anticipate the risks and consequences of critical business choices. AI with its varied applications helps businesses make informed and effective decisions which will have a positive impact on their business. 

Implementing AI in your business isn’t as tall an order as you may imagine. It can be incorporated with your existing business applications to enhance them and make them invaluable.  To learn how your business can avail the best of AI, get in touch with our experts today!

Related Reading: You might also like to take a look at this guide to help you enhance your existing business app with AI.

What Are Cobots?

Cobots have been around since the 1990s. Cobots operate in conjunction with humans to perform given tasks. They are built to interact physically with humans in a shared workspace. 

In other words, cobots or collaborative robots can be defined as the hardware version of Augmented Intelligence. Rather than replacing humans with their autonomous counterparts, collaborative robots augment artificial intelligence technologies to physical bots. According to Barclays, cobots can revolutionize production. This is effective, especially for smaller companies, that account for 70% of the manufacturing industries, globally.

Cobots help in improving human capabilities in performing tasks with greater strength, accuracy, and data capabilities. The first collaborative robot was a device used to directly interact physically with a manipulator that was computer-controlled’. It was invented by J Edward Colgate and Michael Peshkin in the year 1996. Later, Kuka Robotics launched its first collaborative robot in the year 2004, named LBR 3. 

How Are Cobots Useful In Various Industries?

Since Cobots are capable of performing tasks alongside humans instead of replacing them, there is a multitude of ways in which Cobots are used in different industries for varying purposes. The major ones are as follows:

1. Hand–guiding: This cobot has an additional hand – a pressure-sensitive device at the end of its arm. With this arm, the human operator can teach the collaborative robot how to hold an object or how to move, or how fast to move, and so on. It also ensures that nothing gets damaged.

2. Speed and Separation Monitoring Cobots: This particular cobot operates in safety zones. Instead of stopping to perform its task when it senses an outsider’s presence in the safety zone, this cobot slows down and then tracks the location of the human. It stops as the human gets too close.

3. Power And Force Limiting Cobots: These collaborative robots are designed to frequently interact with humans. This specific cobot stops or reverses its movement on encountering any abnormality.

4. Safety Monitored Stop Cobots: These cobots are designed to work independently, but stop whenever a human needs to intervene. This cobot senses human presence and stops all movement until the human has left the safety zone. 

Related Read: Check out how Robotic Process Automation Is Revolutionizing Industries

How Collaborative Robots Offer Game-Changing Benefits

Cobots have been brewing in the web-space for quite some time. In general, robots have replaced human labor from the industrial workforce since the industrial revolution. Robots, but operated within safe environments. Cobots, on the other hand, help in putting away some of the major spatial and environmental dangers that robots may cause, whilst working alongside humans!

Cobots can also be easily reprogrammed. Many businesses and industries are skeptical about falling behind their competitors, especially in situations where irrecoverable disasters are likely to take place. Here is when cobots can be an effective solution. Let us walk through the major benefits that collaborative robots offer in various industries:

1. Increased and efficient Human-Robot Interaction

In any given industry utilizing bots for performing tedious tasks, time, cost, and floor space are the three major critical factors to be considered. This is because the operator can work alongside the cobot, without having to leave the workspace. Cobots are known to reduce idle time of human workforce by 85%. 

Consider a traditional assembly line that is set up in a workspace. Here the human workforce sets up the mechanical robots with required parts to perform the rest of the tasks. The entire production will be put to halt for a long time or stopped from its current operations, in case of any required human intervention. While on the other hand, a collaborative robot works along with the human workforce, which increases the efficiency significantly.  

2. Applicable To Small And Mid-Sized Industries

Strategies to optimize costs are a major concern in every industry. This is because larger industries that have a higher production volume prefer a robot to perform tedious tasks. On the other hand, smaller industries prefer manual labor. 

Implementing cobots can be beneficial across a range of industry sizes, as these bots do not require a heavy set up process. 

3. Safety In Handling Dangerous And Tedious Tasks

Preventing human error is critical in every industry. For instance, steadying the movement of tools used in surgeries is complex. The process needs to be highly accurate as well. Cobots ensure a safer working environment by preventing human errors that can hinder operation accuracies.

4. Increased ROI

Cobots can be conveniently relocated whenever required. This makes it easy to eliminate any non-productive activities during working hours. In addition to being highly efficient and flexible, cobots ensure increased ROI due to significantly reduced labor and maintenance costs. This also results in an increased profit margin as well. 

Related Reading: Read on to learn how you can accelerate your business growth with Robotic Process Automation. 

Future Of Cobots

By the year 2020, cobot sales are expected to cross $3.1 billion. The sales of cobots are increasing every year. According to Barclays Equity Research, analysts state that the global sales of cobots have crossed US$120 million in the year 2015. This figure is forecasted to grow to $12 billion by the year 2025!

The figures above illustrate that the sales of cobots are just 5% of the total robot market. This figure is forecasted to grow exponentially as more industries start to explore the multitude of possibilities of these bots. 

To know more about how cobots can be leveraged for your business, drop a call to our strategists right away!

Five Ways AI Is Accelerating Mobile App Technology

Artificial Intelligence (AI) has permeated the tech world. It is enhancing everything from your car to your toothbrush.  It is influencing the decisions that affect your life. Artificial Intelligence is the term used to define a machine’s ability to simulate human intelligence. Actions which were once considered unique to humans are now being stimulated by technology and used in every industry. This includes mobile app technology. 

Mobile phones have been using AI for some time now. The earlier generation of phones was cloud-based and Internet-dependent. The difference today is that the latest smartphones integrate cloud-based AI along with built-in AI. The rate at which AI is expanding is accelerating. 

Let us now discuss how AI is enhancing mobile app technology.

Related Reading: Take a look at the top AI trends of 2019.

AI is the Catalyst in Mobile Apps

As per a study by McKinsey Global Institute, AI expansion brought in an investment of $39 billion back in 2016 which was three times the amount invested in AI three years prior to that. Acting as a catalyst, AI is continuing to enhance mobile apps. It empowers the evolution of mobile apps by making them intelligent pieces of software that can predict user behavior and make decisions. AI allows mobile apps to learn from data generated by the user.

Mobile developers are adapting quickly to changing innovations. There are over 5 million apps in the leading app stores, which stands to show how AI is creating personalized app experiences for users and is adapting to various situations due to automated learning capabilities. Here are five specific real-life benefits of AI on mobile apps. 

1. The Wrapping of Artificial Intelligence with the Internet of Things (IoT)

The combination of AI and IoT is powerful in creating a personalized experience for users. A large amount of varied data is collected from the customer in real-time as he uses the device. Each usage has commands or interactions that are being used with mobile apps. AI can then leverage this real-time data to deliver an enhanced personalized experience.

IoT reduces app development time significantly. In mobile app development, IoT along with Artificial Intelligence can lead to better utilization of resources and higher efficiency. Together they free up a good part of employee bandwidth. It delivers modified and more efficient apps and strengthens data security measures, which is vital because the future of mobile apps will always be revolving around connected devices.

2. Enhances Search Engines

Text and voice have been the traditional search modes. But say, you saw something you would love to buy, but you don’t know what it is called or how to find it. Visual search helps you find what you want even when you don’t know the words to describe it. The smartphone is the best launchpad for visual search technology. An example of this is Google Lens. 

In some cases, visual search is more accurate and faster than a voice or a text search. Due to integrated AI in mobile applications, android developers are bound to develop an image recognition system and a voice recognition system. To increase conversion rates AI will provide localization of applications.

Related Reading: Learn more on how to build an intelligent app ecosystem with AI.

3. Empowers Real-Time Translation

There are so many translation apps which enable translation. However, most of these apps do not work without the internet. AI could enable your smartphone to translate different languages in real-time without the need for an internet connection.

Much like a digital version of what interpreters do, Artificial Intelligence can provide a simultaneous translation tool which allows sentences to be translated almost instantly without a time lag. AI allows the translation tool to be adjusted for latency. This would mean that a user can set the lag between a spoken word and its translation. This would be especially useful for certain languages which would require a longer time lag for better translation. Example: Baidu.

4. Improved Security with Face Unlock 

Face Unlock was launched in September 2017 by Apple. Combined with Apple’s elaborate hardware, Apple iPhone X uses an AI-based algorithm for its face unlocking system. Using AI processing, the phone can easily identify the user’s face even with facial changes like specs or beard.

Recently, Google announced radar-based, hands-free gestures to face unlock. With its hands-free system, unlocking the phone would be easier, faster and secure. It claims that it can unlock the phone almost in any orientation.

5. Enhanced Mobile App Authentication

As AI is becoming easily available as a commercial technology, both criminals and organizations are taking full advantage of it. There are predictions by cyber-security experts that the world might witness many AI-powered cyber-attacks in the future. This necessitates the development of more sophisticated cyber defense systems. Also, with the increasing use of smartphones, we all need an advanced level of data security. Security has been one of the biggest concerns for Android developers. 

Thanks to enhanced artificial intelligence with machine learning and deep learning algorithms we can look forward to a time when authentication becomes a smooth experience, which allows users to enjoy security without trading convenience. AI can be enabled to give alerts to users about possible threats. AI can also add a level of augmentation to biometric authentication which makes it almost hack-proof. Another benefit of AI algorithms is that they can find and alert potentially compromised accounts in real-time.

A Guide for AI-Enhancing Your Existing Business Application

AI With You Now and Into the Future

AI presents numerous possibilities for innovation in the mobile app industry. AI is the future of mobile app development. Artificial Intelligence is changing how users interact with app services and products. Mobile app users will be linked to an ecosystem of intelligent applications and will work together to deliver a personalized user experience. 

Businesses developing AI-enhanced mobile apps will benefit from the predictive analysis these apps can create. According to a study conducted by Callsign, the user preference for authentication is shifting. Such changing preferences would mandate further enhancements in the development of mobile apps.

An intelligent ecosystem will gather a large pool of social data and behavioral interest, which can be used to further increase revenue and improve user experience. It is not an exaggeration to say that the smartphone industry is being revolutionized by AI. This makes it important to enable AI in your business and mobile applications. Give us a call to discuss how this can be done.

Key Differences Between Machine Learning And Deep Learning Algorithms

Artificial Intelligence is on the rise in this digital era. According to IDC’s latest market report, global investment of businesses in AI and cognitive systems is increasing and will mount to $57.6 billion by the year 2021. 

Artificial Intelligence holds a high-scope in implementing intelligent machines to perform redundant and time-consuming tasks without frequent human intervention. AI’s capability to impart a cognitive ability in machines has 3 different levels, namely, Active AI, General AI, and Narrow AI. Artificially intelligent systems use pattern matching to make critical decisions for businesses.

Related Reading: Know the different types of Artificial Intelligence.

Categories Of Artificial Intelligence

Machine learning and Deep learning are 2 categories of AI used for statistical modeling of data. The paradigms for the 2 models vary from each other. Let us walk through the key differences between the two:

• Machine Learning: Process Involved

Machine learning is a tool or a statistical learning method by which various patterns in data are analyzed and identified. In machine learning, each instance in a data set is characterized by a set of attributes. Here, the computer or the machine is trained to perform automated tasks with minimal human intervention. 

To train a model in a machine learning process, a classifier is used. The classifier makes use of characteristics of an object to identify the class it belongs to. For instance, if an object is a car, the classifier is trained to identify its class by feeding it with input data and by assigning a label to the data. This is called Supervised Learning. 

To train a machine with an algorithm, the following are the standard steps involved:

• Data collection  
• Training the Classifier
• Analyze Predictions 

While gathering data, it is critical to choose the right set of data. This is because it is the data that decides the success or failure of the algorithm. This data that is chosen to train the algorithm is called feature. This training data is then used to classify the object type. The next step involves choosing an algorithm for training the model. Once the model is trained, it is used to predict the class it belongs to. 

For instance, when an image of a car is given to a human, he can identify it belongs to the class vehicle. But a machine requires to be trained via an algorithm to predict that it is a car through its previous knowledge. 

Various machine learning algorithms include Decision trees, Random forest, Gaussian mixture model, Naive Bayes, Linear regression, Logistic regression, and so on. 

Machine Learning- Deciphering the most Disruptive Innovation : INFOGRAPHIC

• Deep Learning: Process Involved

Deep learning can be defined as a subcategory of machine learning. Inspired by ANN (Artificial Neural Networks), deep learning is all about various ways in which machine learning can be executed. Deep learning is performed through a neural network, which is an architecture having its layers, one stacked on top of the other.

A neural network has an input layer that can be pixels of an image or even data of a particular time series. The next layer comprises of a hidden layer that is commonly known as weights and learns while the neural network is trained. The final layer or the third layer is that predicts the result based on the input fed into the network. 

The neural network thus makes use of a mathematical algorithm to predict the weights of the neurons. Additionally, it provides an output close to the most accurate value. 

Automate Feature Extraction is a way in which process performed to find a relevant set of features. It is performed by combining an existing set of features using algorithms such as PCA, T-SNE, etc. For instance, to extract features manually from an image while processing it, the practitioner requires to identify features on the image such as nose, lips, eyes, etc. These extracted features are fed into the classification model. 

The process of feature extraction is performed automatically by the Feature Extraction process in Deep Learning by identifying matches. 

Related Reading: AI and ML are revolutionizing software development. Here’s how!

Key Differences Between Machine Learning And Deep Learning Algorithms

Though both Machine Learning and Deep Learning are statistical modeling techniques under Artificial Intelligence, each has its own set of real-life use cases to depict how one is different from the other. Let us walk through the major differences between the modeling techniques.

1. Data Dependencies

Machine learning algorithms are employed mostly when it comes to small data sets. Even though both machine learning and deep learning can handle massive amounts of data sets, deep learning employs a deep neural network on the data as they are ‘data-hungry’. The more data there is, the more will be the number of layers, that is the network depth. This increases the computation as well and thus employs deep learning for better performance when the data set sizes are huge.

2. Interpretability

Interpretability in Machine Learning refers to the degree to which a human can understand and relate to the reason and rationale behind a specific model’s output. The major objective of Interpretability in machine learning is to provide accountability to model predictions. 

Certain algorithms under machine learning are easily interpretable, such as the Logistic and Decision Tree algorithms. On the other hand, Naive Bayes, SVM, XGBoost algorithms are difficult to interpret. 

Interpretability for deep learning algorithms can be referred to as difficult to nearly impossible. If it is possible to reason about similar instances, such as in the case of Decision Trees, the algorithm is interpretable. For instance, the k-Nearest Neighbors is a machine learning algorithm that has high interpretability.

3. Feature Extraction

When it comes to extracting meaningful features from raw data, deep learning algorithms are the most suitable method. Deep learning does not depend on binary patterns or a histogram of gradients, etc., but it extracts hierarchically in a layer-wise manner. 

Machine learning algorithms, on the other hand, depend on handcrafted features as inputs to extract features. 

4. Training And Inference/ Execution Time

Machine learning algorithms can train very fast as compared to deep learning algorithms. It takes a few minutes to a couple of hours to train. On the other hand, deep learning algorithms deploy neural networks and consumes a lot of inference time as it passes through a multitude of layers. 

5. Industry-Readiness

Machine learning algorithms can be decoded easily. Deep learning algorithms, on the other hand, are a black box. Machine learning algorithms such as linear regression and decision trees are made use of in banks and other financial organizations for predicting stocks etc. 

Deep learning algorithms are not fully reliable when it comes to deploying them in industries. 

Both machine learning and deep learning algorithms are used by businesses to generate more revenue. To know more about how your business can benefit from artificially intelligent systems and which algorithms can be leveraged for a positive business outcome, call our strategists right away!