Category: Artificial Intelligence

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AI with real-time data

Data is the lifeblood of Artificial Intelligence. AI/ML models develop the capability to perform tasks based on the quality and quantity of training data consumed. As emerging technologies, Artificial Intelligence and Machine Learning can ingest large amounts of relevant Big Data and provide incredible accuracy and efficiency with their solutions. For this piece, we’ll be exploring how AI is harnessing the power of real-time data, data that is providing the accuracy, speed, and opportunity to scale that companies and world governments are looking for.

WHAT IS REAL-TIME DATA ANALYSIS?

Real-time data is any information received immediately after discovery. Such data could be static or dynamic in nature, and it can be used for navigation and tracking purposes. It can also be used for analysis purposes, and research, after a large sum of such real-time data has been collected.

Real-time data analysis involves analyzing the data immediately on reception. Users draw quick conclusions with the data and publish their findings, or use obtained insights to improve processes.

A popular example of real-time data that is being consumed daily is the coronavirus infection spread/cases across the globe. South Korea flattened its curve by introducing an app that could monitor citizens’ whereabouts and warn them about nearby infection hotspots. China has used active surveillance techniques for increased testing. Computer vision systems have helped the Chinese government identify unhygienic practices specific to this pandemic (such as crowded zones and citizens avoiding facemasks) in real-time and assess the risk such practices bring.

Coming back, customer relationship management (CRM), fraud detection, and credit scoring, among other popular use cases, take advantage of real-time data.

AI AND REAL-TIME DATA

With real-time data available in the petabytes, it shows that there is no shortage of such data’s availability. AI/ML models require volumes of training data to achieve the highest accuracy possible. Real-time data allows these models to learn from the latest information, thus increasing the model’s relevancy with time.

From agriculture to banking, here are some interesting industries wherein AI and real-time data are aiding innovation:

AGRICULTURE

50% of the world’s population depends on agriculture for their survival. For farmers to succeed in this space, they need to monitor their crops and ensure that only the highest quality of produce is being delivered. With the help of AI, farmers now have a higher guarantee of growing uninfected and market-ready fruits and vegetables.

AI and real-time data in agriculture

Farmers can monitor the moisture level of soil using soil and water level sensors. Such sensors can alert farmers about low water levels, and action can be taken accordingly. Microsoft’s agriculture project (FarmBeats) has been able to use AI, Edge, and IoT to reduce water intake by 35% while increasing farm productivity by 45%.

Computer vision systems can detect insects and pests that are threatening to ruin crops. This allows farmers to sprinkle pesticides only on the affected crops, thus saving resources. AI today can also help farmers with weather prediction. Having such information can help farmers to mitigate potential damage to their crops.

HEALTHCARE

The human body has multiple measurable parameters that provide insights. Real-time patient data is a boon for surgeons looking to monitor parameters such as blood loss and oxygen levels. Using such information, medical professionals can determine the necessity for high-level treatments and take a specific action.

AI and real-time data in healthcare

Also, real-time data collected from multiple patients can understand trends and identifying health patterns. Using such insights, doctors can detect health issues at the earliest stages and recommend the appropriate treatment. For example, doctors are able to review mammograms 30 times faster with AI, with accuracy levels of 99%. This allows hospitals to save on resources by eliminating unnecessary biopsies.

MILITARY AND DEFENCE

Nations can use computer vision for real-time surveillance of sensitive locations, borders, ports, etc. We can train computer vision systems with the relevant training data (that depicts illegal activities and national threats) to understand when to alert authorities. By monitoring such locations, governments will know when to call for military assistance. This will ensure the military will be used only when absolutely necessary thus ensuring reduced national security risks. Attacking moving targets using a combination of computer vision and advanced artillery.

AI and real-time data in defence

Such technology allows militaries to flip the cost curve. For example, if a $1m missile takes down an F-35 jet, it becomes an expensive loss for the air force that lost the jet. But what if the same air force sends a low-cost unmanned vehicle (such as the Octatron Skyseer, which costs $25k-$35k)? If it gets destroyed by a similar missile, the army that launched that missile bears the loss, because it’s more expensive for them to use their missile. Hence, the cost curve gets flipped.

The United States, the world’s most powerful military force, introduced Project Skyborg for developing drones and UAVs (Unmanned Flying Vehicles). China is also working towards weaponizing their AI capabilities with military robotics and drone technology. Recently at the UN, the US, Russia, South Korea, and Israel expressed their desire to explore AI-inspired weaponry, also known as “killer robots” to the opponents of such technologies.

BANKING

In a fast-paced industry such as banking, real-time data aggregation happens in volumes every second. There are multiple parameters and customer touchpoints that banks can use to their advantage. Real-time banking data will allow Machine Learning algorithms to understand what each customer is like and accordingly create a personalized experience, thus improving customer engagement. For example, if a customer is reviewing his/her personal investments regularly, banks could offer investment advice.

AI and real-time data in banking

Fraud detection is another important application of AI in banking. When a bank’s ML model notices activity that has often ended up in fraud, they can alert authorities to take action. Using AI-backed fraud detection solutions, banks have reported an increase in detecting real fraud by 50%. Such systems help banks build trust with their new customers and retain loyal ones.

WEATHER FORECASTING

I mentioned this earlier under AI’s influence on real-time data in agriculture. Meteorologists construct and operate several models and data sources to track the activity of clouds (shapes, movements, density, etc.). With numerous real-time data points obtained from weather analysis, it’s humanly impossible to cover all aspects of weather effectively. Artificial Intelligence becomes the only viable solution for handling such large data volumes, due to its ability to consume Big Data with ease.

AI and real-time data in weather forecasting

While monitoring cloud sizes and other weather parameters with AI, weather forecasters have the luxury of reporting more accurate information. For example, such AI systems can detect comma-shaped clouds from satellite imagery. Comma-shaped clouds are a strong visual indicator of severe weather events. Also, satellite imagery of the earth’s ozone layer with AI-backed insights can allow lawmakers to create policies surrounding climate change and climate protection strategies.

CONCLUSION

Real-time data is voluminous, and with each passing year, we are collecting almost double the amount of the previous year. AI/ML models are the only systems out there that can handle such data, and developers are discovering numerous applications for implementation. Real-time data offers solutions for quick decision making and AI models are here to make that process faster.

This article was originally published on TechPluto.

AI Trends: AI and Medicine

April 21, 2020 | Artificial Intelligence | No Comments

AI and Medicine

Artificial Intelligence is here to improve our lives, by not just making things more efficient, but also increasing our lifespan. Companies across industries are experiencing the advantages that come with AI innovation, especially the healthcare space. 

Throughout human history, we’ve been able to understand the parameters that determine health better, and we’ve developed accompanying technology. With vaccines in the late 1700s, anesthesia and medical imaging in the 1800s, to organ transplant and immunology in the 1900s, healthcare innovation has been on an upward slope. In the 21st century, AI in medicine is showing us how that slope can rise further. Here are some popular trends in AI and medicine:

Virtual Doctors

Chatbots have experienced a rise in popularity across industry operations, and healthcare has an important use case for them. In a time such as the coronavirus outbreak, there aren’t enough medical professionals available to attend to all patients. In the USA, the ratio of physicians to the country’s population is around 277 to 100,000. The World Health Organization recommends a 1:1000 ratio, but 44% of its member nations don’t meet this criterion.

Conversational AI in medicine

Here’s where medical chatbots can reduce that gap. Chatbots offer 24/7 accessibility and instant responses. For patients facing unusual symptoms, they can address their health issues from the comfort of their homes with a chatbot. Conversational AI in medicine can be trained with conversational datasets that represent diagnoses for various diseases. Accordingly, the chatbot can recommend direct solutions for simpler cases or schedule a doctor’s appointment for the more serious ones. 

Treating Patients With Alzheimer

Many of our populations’ senior citizens end up suffering from Alzheimer’s disease. It’s the first step towards severe cognitive decline and it can have a toll on the affected individual and his/her loved ones. While it has always been hard to predict the chances of cognitive decline due to Alzheimer’s, today, AI can reliably perform the same prediction. 

Using a combination of biometric data and cognitive tests, AI models can determine a patient’s risk levels. Also, AI models are learning how to convert brain signals into machine-readable text. Such technology can also improve Alzheimer’s research and therapy.

Vaccine Identification

The coronavirus pandemic has created the wild chase for a vaccine, for its the solution to ending the world’s present lockdown. Organizations across industries are exploring ways in which they can contribute. This has led to exciting players entering the vaccine game. Tech giants such as Microsoft and Google are developing AI solutions for vaccine development. 

AI in vaccine development

When it comes to viral infections, their vaccines are produced by combining the original virus with another virus that can weaken the former. This combination will ensure that the virus to be tackled cannot reproduce or multiply well, thus being rendered ineffective. 

But, vaccine development is easier said than done. Pathogens have highly complex methods for dodging mainstream medication. Pathologists have to go through multiple protein structures and medicinal ingredients to zero in on the one that will cripple the virus or bacteria at hand.

Here’s where AI comes in. Artificial Intelligence models can read through thousands of research papers within the same time it takes a researcher to go through about 10. Also, an AI model can recommend vaccine ingredients that exhibit higher chances of success. Training such a model involves making it understand how protein folding and virus-to-virus interaction takes place. With the appropriate training datasets, AI models will be ready to help medical researchers and pathologists locate the right vaccine. Since clinical trials always are the rate-limiting step, AI models provide the advantage of saving time in other areas of vaccine development.

Managing Medical Records

Data is everywhere, and medical records have plenty of that. These records contain patients’ medical history, diseases, surgeries, etc. They also include treatment methodologies used for each scenario. So now, we’ve got a database of patients’ health issues with its accompanying solution. Such data can be converted into machine-readable text for AI models to consume. Once these models receive a list of a patient’s symptoms, using the data consumed, they can perform simple diagnoses and suggest treatment methods and medication. This can help doctors save time and be on top of their game even when presented with rare cases.

Wearables

Popular wearables such as Fitbit Flex, Samsung Gear 2, and Apple Watch have revolutionized the way we communicate with our mobile devices. It has also shown us how it can aid our fitness regimes, with counting our steps and measuring our heart rates. But, wearable tech has potential beyond the above-mentioned activities. Advanced wearables can today detect quivering or irregular heartbeats, symptoms that are a sign of possible blood clots, stroke, heart failure, and other complications concerning the heart. Such information can be sent directly to a medical professional for immediate attention.

Cancer Treatment

AI in cancer therapy has allowed for numerous solutions to tackling this fatal disease. Computer vision systems can be trained to detect cancerous cells and understand what these cells look like at different stages. Adopting computer vision into cancer diagnosis results brings higher accuracy into the picture. This helping doctors and medical professionals provide better treatment options and extend more lives.

Surgery

Surgical procedures require high-level precision, and it takes a skilled hand to perform them successfully. AI-inspired surgical equipment could help guide a surgeon performing a complex procedure such as open-heart surgery, cesarean section, cataract, etc. 

AI in surgery

Using a combination of visual datasets and movement-based training, surgical AI equipment can locate parts of the body that need attention, sometimes better than a surgeon can. When developed successfully, such technology can revolutionize surgical procedures by increasing the number of successes and increasing patients’ trust.

AI-inspired genomic medicine

The National Human Genome Research Institute describes genomic medicine as a medical discipline that involves using genomic information about an individual for clinical care. Already popular in oncology and infectious diseases, this emerging field of medicine has arrived during the same era as Artificial Intelligence. By consuming large volumes of datasets containing genomic information, AI models can learn how to detect patterns and provide first draft health reports. Doctors can review such reports and use them for their medical practice.

Conclusion

While the avenues for AI implementation in healthcare are practically endless, AI’s ability to provide accurate results becomes the defining factor. Healthcare involves life or death situations, and any room for error could result in undesirable consequences.

Human error is inevitable and it’s something even the world’s best medical professionals can be victims of. Here’s where AI can relieve them of some pressure. AI models in medicine can learn from well-built training data sets, identify trends, and provide recommendations for medical practitioners.

Especially during this coronavirus outbreak, we need to provide doctors with the best technology available for them to perform their tasks with higher precision. AI trends provide for good news at this point, and hopefully, it’s forward sloping curve flattens out the coronavirus’.

AI and COVID 19

As we experience the coronavirus pandemic, we have an influential role to play for mitigating the virus’s spread. At an individual level, social distancing goes a long way in blocking potential routes for infection spread. Added to that, if you wash your hands regularly and wear a mask whenever you’re in public, you’re doing your part to keep the coronavirus at bay!

COVID 19 - the coronavirus

Businesses also have a vital role to play here, and they’re exploring avenues to help fight this battle. Essential services are operating around the clock to ensure their inventories are full. Governments have also roped in private firms to help with medical resources. For example, the Trump Administration issued an order under the Defense Production Act to make General Motors manufacture surgical masks at scale. 

Along with these measures, companies that have a working knowledge of Artificial Intelligence are identifying coronavirus-relevant use cases and developing intelligent models. As an emerging technology, industries are slowly adopting AI. With the hopes of mitigating the spread or finding a cure to COVID-19, here’s how AI can be used for tackling it:

Vaccine development

Historically, vaccine development has been an effective strategy for tackling contagious diseases. This is why pathologists are researching numerous relevant antigens and immunogens for tackling COVID 19. The AI strategy for vaccine identification is two-fold: 

  • Suggesting vaccine components by understanding viral protein structures
  • Scouring through medical research papers at a pace faster than medical professionals

Pathologists are researching nucleic acid vaccines (one of the three types of vaccines, others being whole-pathogen vaccines and subunit vaccines) as these are the ones relevant to crippling the coronavirus. AI helps pathologists understand a variety of molecule structures, and their ability to fold the coronavirus (the action that will render the virus ineffective).

COVID 19 vaccine development

Korea-based Deargen created a deep-learning model that can employ simplified chemical sequences that can predict a molecule’s chances of disabling the coronavirus. Google’s DeepMind is using Artificial Intelligence models to study properties of the novel virus, which includes understanding the protein structures associated with SARS-CoV-2. 

Chatbots for diagnoses 

Chatbots help companies save time and resources. As the coronavirus spreads, the world has understood that hospitals are overloaded. And, most countries don’t have the right medical infrastructure to handle numerous patients.

AI in diagnosis using chatbots

Patients can use medical chatbots to perform quick diagnoses. after which it can predict the likelihood of a patient being infected. This ensures that only patients who have a high probability of testing positive end up visiting a hospital. Medical chatbots reduce the load on medical professionals and hospital resources, thus saving lives truly at risk.

Monitoring infection spread

With location data, we can identify infection hotspots across geographies. This can be used to predict the odds of infection spread based on earlier instances. Users report whether they’re positive or negative on an app. And based on that user data, the app can determine which parts of a neighborhood need to be avoided. It can also notify users who have visited hotspots and request them to get tested. Such data will be immensely valuable to governments imposing lockdowns, and it will provide them with the data needed for creating a path to normalcy.

COVID 19 infection spread

South Korea used an app that can locate infected citizens and their local travel history. During a mandatory two-week self-quarantine, the app helped the South Korean government trace infection spread by identifying cases wherein people were at a two meters distance from each other. Using this app helped South Korea flatten the curve, thus exhibiting how this battle is one that can be won. 

Israel has created a survey reading AI that takes advantage of user-reported data for tackling the COVID 19 spread. Using this, populations can be warned about risky locations, and users at a higher risk of contamination can avoid infection. 

Conclusion

The world is now on red alert and everyone is battling this pandemic together. Social distancing has proven to be the single most effective tool we have at our disposal. People have been asked (in many cases forced by law) to wear masks and maintain physical distance while in public. For the ones already infected, hospitals are doing their best to ensure the right resources such as hospital beds and ventilators are available.

While all this is good, we still have the problem of a shortage of medical professionals, nurses, and hospital resources. Also, we don’t fully understand this virus and a vaccine isn’t exactly close to being ready for usage. Here’s where we’ve got Artificial Intelligence at our disposal. 

AI can speed up human processes and allow us to quickly reach our process’ finish line. By using AI for vaccine creation, pathologists can locate and develop a suitable vaccine faster for tackling COVID 19. Chatbots can reduce the load on doctors and ensure multiple patients are tested. And, a combination of location and user data can help the public and governments understand what the situation surrounding infection spread looks like.

Coupled with human intelligence, AI has the potential to get us out of this pandemic sooner rather than later. The answer lies in the efficiency of AI implementation.

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.

Financial Services

Artificial Intelligence and innovative services and products are spreading like fire. The companies and individuals who are a fan of technological developments follow the developments. AI provides multiple services and people unaware of new technology even use it extensively.

The modern approach towards the finance industry is the result of multiple technological interventions.

Current Market Size of the Finance Industry:

Presently the expansion phase of the finance sector in India is calling for innovation. The foreign portfolio investors have reached $899.12 million on November 22, 2018. Total Asset Under Management (AUM) in the Mutual Fund industry was on peak, at $340.48 in April 2018 till February 2019. IPOs (Initial Public Offers) raised in the period from April to June 2018 have increased to $1.2 billion.

Investments and Developments provide a new horizon to the upcoming future.

Financial Services and AI

Future of Financial Services:

Leading financial services firms are achieving a higher market share with the AI initiatives they enroll. The finance sector is enthusiastic,about 70% of firms are part of the ML and 60% use NLP.  The future of this sector varies in terms of revenues independent of the technology.

Artificial Intelligence brings dependability in the service sector and the finance industry is a prime service provider. The trust built over the last few years is changing the budgeting and strategy for involving AI. It provides an advantage to meet customer expectations and to gain a competitive advantage over others.

The scope of investments by 45% of frontrunner financial services firms are nearing to $5M. Risk takers are likely to win, as they are pro technological changes.

AI adoption increases the ability to solve the operational problems of a repetitive nature, or simple tasks like primary conversations with the basic level of Artificial Intelligence technology. Advanced level of AI brings in understanding power, perception and decision power.

Mobile wallet transactions in India expected to touch $492.6billion by 2022.

The Association of Mutual Funds in India (AMFI) is targeting nearly five-fold growth in assets under management (AUM) to Rs 95 lakh crore (US$ 1.47 trillion) and three times growth in investor accounts to 130 million by 2025.

Artificial Intelligence helps in credit decisions, risk management, fraud prevention, trading, personalized banking, process automation and enhancement of customer experience. AI is making the dream come true for the people who had weird ideas that machines can do wonders.

Humans are optimistic about AI technology, with expectations that it will bring transactional security, improved digital assistants, a high level of transparency in handling accounts, introducing process automation and foremost significant is the thorough checks of transactions and processes.

Types of Financial Services:

  1. Banking Services
  2. Investment Services
  3. Insurance
  4. Foreign Exchange
  5. Accounting
  6. Brokerage
  7. Mortgage
  8. Wealth Management

Software and mobile applications are improving accessibility to financial services and Artificial Intelligence is easing the process. Availing services was never so easy. Automation with AI, ML and NLP is a boon for service recipients.

Scope for AI-based automation:

1. Commercial banking Services: These financial services help businesses to raise money from market sources like bonds and equity. The primary activities of commercial banks if powered with AI can bring discipline to internal banking processes. Investment banking and retail banking are already exploring AI.

2. Venture Capital: It is a service that provides outside investors to companies with the potential of high growth. There is a surety of business when these investors bring in money for the business. AI can help in calculating risks and returns for the investors.

3. Angel Investment: An informal investor (angel investor) typically shares the resources and funds their investment capital. There are groups and networks of angel investors. AI can improve networking for connecting the right investment seekers and investors based on preferences.

4. Conglomerates: A financial services company is functioning in multiple sections that provide services such as life insurance, asset management, retail management, and investment banking can draw advantages with AI-based support apps.

5. Financial Market Utilities: Stock exchanges, clearinghouses and interbank networks and such organizations provide specialized services that require precision. AI-powered trading and banking are in high demand.

AI can assist in simplifying the service and improving its quality.

1. Smart Sales: The AI-based Chatbots are better in solving basic queries and responding using FAQs. With no or minimum human intervention, a virtual salesperson can take the customer through the stages of sales right from inquiry until closure.

2. Compliance: An enormous amount of financial data that is generated in banking and other financial services sector creates challenges for the service providers. Ai can identify the malpractices, manipulation and any loopholes found in personalized and classified services.

3. Evaluate Risks: Artificial Intelligence can consider the concerns and treat the user requests accordingly. Each financial transaction, loan or investment is accompanied by various risks that affect the business and thus the help of technology improves decision making.

4. Trading: Financial markets are prone to fluctuations yet many algorithms that try predicting the trends, using the old data. It can independently suggest, buy, sell or hold the stocks and notify us for the transactions or alert based on fed instructions.

5. Predictive Analytics: The spending habits, purchase frequency, other choices, investment portfolio, and transactional data lets AI guide for improving financial decisions and shares investment ideas.

6. Data Enrichment: Transaction data is simplified enough for the customers to understand and take control over their spending habits, budgeting, managing the credit score.

7. Smart Loans: The banks and financial institutes consider the credit score of the customer to approve the loans. Their banking history, income, tax payments, current financial situation, and past loan records are maintained by AI. It can easily bridge the gaps between creditworthy loan seekers and lenders.

8. Personalized Wealth Management: This service is for customers that have either huge bank balances or active investors in both the cases they are the favorite sales targets. The AI-based advisors provide the best advice to the customers based on the customer data available.

AI Performed Banking Activities:

1. Issue checkbooks

2. Credit cards

3. Interacting with customers for balances

4. Loan information and procedures

5. Online transactions

6. Electronic fund transfer  

7. Pending documents

8. Send dispatch information

9. Make bill payments

10. Schedule payments

11. Utility bills

12. Repayment of loans

13. Assist in tax planning

14. Aid in foreign exchange

15. Foreign exchange processing and remittance

16. Send info on upcoming investment options in debt and equity

17. Calculate and inform about brokerage for transactions

18. Guidance for wealth management

19. Help buy an insurance policy, send quotes and renewals

20. Book new FDRs and renewal of FDRs

21. Ease to operate the accounts

Innovations that have changed this industry with traditional mindset functions are:

  1. Cleo: An AI-powered data-driven messenger helps manage their finances. It allows the users to link bank accounts and send money to their contacts of FB messenger. You can set a limit for savings and Cleo can keep that spare amount aside. Checks if you should spend money and is it affordable. It can warn users when they do not follow the financial limits and overspend.
  2. ZestFinance: This ML automated platform is an underwriting solution that assesses borrowers with no credit information. AI-powered platform can be implemented by the companies to automate lending and reduce losses occurring due to inaccurate data. Zest Finance can predict the risk and improve the business.
  3. Scienaptic Systems: It provides an underwriting platform that gives banks and credit institutions better transparency about the customers. It successfully holds 10 crores of customers. Scienaptic Systems uses myriad unstructured and structured data, transforms the data and learns from interactions to offer contextual underwriting intelligence. It could save $151 million of loss for a major credit card company.
  4. Eva Money:  This AI-based mobile app is available on iOS and Android. It is voice and chat enabled and replies to all your queries relating to personal finances. Link the Eva Money app to your bank accounts and it provides a picture of your current financial holdings. It can even recommend increasing savings, improving credit score and other financial decisions.
  5. Trim: It analyzes your expenses and assists in saving money. It can even cancel the unused facilities or high-cost subscriptions, get you better options on investment and insurance requirements and even negotiate bills for you. VentureBeat reported Trim to save $6.3 million of 50,000 users.
  6. DataRobot:  It provides machine learning-based software for data scientists, business analysts, software engineers, and IT professionals. DataRobot helps to build accurate predictive models that can enhance decision making for financial services. It deals with issues like fake credit card transactions, digital wealth management, direct management, and lending.
  7. WinZip: AI-powered finance app delivers automated financial services like investments, savings, and payments. The conversational AI ‘Misa’ is the most powerful financial chatbot, MintZip takes the support of Misa to consider the behavioral sciences and financial sciences for continuous training on financial aspects. It assists users in financial planning.
  8. Kesho: This software provides machine intelligence and data analytics to leaders in the finance industry. Kesho also used cloud computing and NLP, this speeds up the response to the questions from users. Kesho could predict the pound rate drop as mentioned in Forbes article.
  9.  AlphaSense: This AI-powered search engine serves the banks, investment firms, and Fortune 500 companies. Natural language processing analyzes keyword search within research, news, and transcripts to discover the trends of financial markets. AlphaSense is providing great value to financial professionals, organizations, companies, traders, and brokers with the latest information on private and public companies. AI analyzes large and complex data and uses algorithms for quantitative trading that can automate trade and make them profitable.
  10. Kavout: It uses machine learning and quantitative analysis to process massive data that is unstructured. Identifies financial market patterns for price and SEC filings in real-time. Higher Kai Score shows outperformance of stock, it is an AI-powered stock ranker. Kavout selected stocks to have a higher annual growth rate.
  11. Kasisto: A conversational AI platform Kai improves customer experiences and reduces the volume of customers approaching call centers.  Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. Chatbots recommend daily financial decisions based on calculations. Kaisisto can be integrated with mobile apps to provide real-time support to customers.
  12. Shape Security: Top banks in the US use Shape Security restrains frauds of credit application, credential stuffing, cracking gift cards and other frauds by investigating and identifying fake users. The ML models are trained to identify between real customers and bots. Its Blackfish network uses AI-enabled bots to detect logins from different IPs, machines, and phones and alerts the customers and companies for the breach.
  13. Able AI: A virtual financial assistant that integrates with Google Home, Amazon Alexa, SMS, Facebook, web, and mobile to make banking more convenient. Able AI provides services like customer support, personal financial management, and conversational banking. The app also helps in budgeting, tracking expenses and working on savings.
Innovations in financial Services Industry

AI-based financial services mobile app development is in full swing. Customers are focusing on the things that make a difference in their lives instead of looking for processes and trying to understand the terminology.

Implications:

The evolution of financial services with the advancement of AI technology allows us to manage business risks, improves forecasts, assists trading, provides cybersecurity, detects fraud, betters personal banking and brightens user experience.

The technology of today is the future of industries tomorrow. Hammer the iron when it is hot applies to the adoption of advanced technology in every sector and finance is no exception. Financial services are awaiting a brighter future where humans are relieved of the pressure to perform better. Let AI guarantee the uninterrupted services for your valuable customers.

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.

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.

Role of Big Data and AI in Financial Trading

Considering the recent development of AI / ML, it is worth exploring the role of Big Data and AI in revolutionizing financial trading. Internet accessibility, mobile smartphones, social media platforms increase the information exchange. Financial trading is complicated, requires complex calculations that use formulas and other factors that affect are market influencers. Thus the trading for a common man is challenging.

In 2018, the global trade finance market was valued at $ 59,500 million. It is expected to touch the mark of $ 71,000 million by the end of the year 2024.

In 2016, the International Data Corporation (IDC) had predicted that sales of solutions based on big data analytics would reach $187 billion by the year 2019.

What is Big Data & Artificial Intelligence?

Big data is voluminous data in either raw or structured form collected from various sources by the organizations. This data is important for businesses but the processing is complex. It requires technology-based solutions to clean, format, manage data and make it usable. It helps in improving operations and make decisions faster than before due to the insights available.

Artificial Intelligence is the human intelligence programmed in machines. Machine learning, Deep learning, Natural language processing of AI enables recommendations, forecasts, reporting, and business analytics. AI builds intelligence from initial learning and continuous learning.

Big data has an input of raw data and AI pulls input from Big Data. The Big data is the initialization of data processing and AI is the output that can help you to make better business decisions.

Define Relationship between Big Data and AI:

  1. Data Dependent: Both Big data and Artificial Intelligence need data that can benefit organizations
  2. Accurate Predictions: Insights are precise with AI to support Big Data, which is just a collection of data. Manually it is impossible to find sense out eg. Big Data but AI can speed the process to highlight actionable.
  3. Trading performance: Big Data has a detailed track record of each trade, broker, trading company and stock. AI empowers us to utilize this gathered information to draw promising results.

What is Financial Trading?

Financial trading is buying and selling of stocks, bonds, commodities, currencies, derivatives, and securities. The price of a financial instrument is determined by demand and supply. Factors that affect financial trading are market conditions, economic conditions, and market influencers. The process of trading is shortlisting financial instruments, buying or selling via broking houses or online trading platforms.

Benefits of Big Data and AI in Financial Trading:

We no more rely on human intuition, knowledge and data-based decision-making gained importance with the development of technology.

  1. Quantitative analysis and trading
  2. Trends and patterns in trading
  3. Trading opportunity analysis
  4. Minimize risks
  5. Increases accuracy
  6. Better trading decisions
  7. Market sentiments analysis
  8. Financial market analysis

Revolution in Financial Trading by AI and Big Data:

Each step of financial trading cycle is crucial and the technology can increase the profitability or at least the probability of success. Changes in the financial market are faster than a blink of an eye and at times stagnant. This dynamic or sluggish behavior of the market can tempt traders to take actions out of impatience. This is where advanced technologies play a vital role.

How big data and AI has revolutionized financial trading?

The massive data stored is formatted to benefit data analysis and analytics. AI discloses valuable insights from the data pertaining to the industry.

Intelligent algorithms designed using Big Data and Artificial Intelligence can help us accomplish our financial trading goals.

Distinct information about the trading patterns, market trends, market reviews, and potential trades is possible due to Big Data. AI can predict using this data stored for trading patterns, market trends, etc.

The growth of Big Data leads to better AI solutions. It can encompass more data to learn from and analyze. A combination of AI and Big Data will be in demand as people have started tasting the fruits of this technology. Their interdependencies provide interesting results. AI brings reasoning power, automates learning and allows scheduling tasks relating to financial trading.

Measurable Trading Growth: Financial trading with AI technology-based algorithms will foresee quantitative trading. Growth in the number of traders and trading activities is the result of data-driven intelligent trading systems. Quality data, proper processing and connecting it with applications facilitate users in prompt decision-making. Programs and AI tools have left aside the manual trading strategies that once prevailed. Accurate outcomes are one of the major reasons for using Big data and AI in financial trading.

Offerings: Various applications that AI introduced to the field of financial trading are systems that recommend stocks, an investment able period, and signals buying and selling. Predict price movements, annual returns, link current world affairs and its impact on the markets. It can even help in portfolio management. It can predict new investment models and introduce profitable algorithms.

Reliance: Customers can rely on the mechanisms developed to meet the financial goals of long term and short term. Secured transacting and faster dealings increments the transactions to prevent frauds and meets the requirements of financial market compliances. Surveillance of trading platforms by the stock exchange includes the micro-level check on the technological tools that can disrupt the process.

Bots advisory services: The chatbots assist users in making financial decisions keeping customer preferences in mind. Suggestions and solutions presented by them are free of bias and does not manipulate humans. The time, energy and costs involved are lesser compared to the human agents that provide service.

Risk Mitigation: Human errors and manual processing issues are diminishing with the new technology financial trading implemented. Big data and AI improved the trading process right from reviewing stocks, placing an order, execution of the order, and delivery. We can schedule notifications, information, and confirmations using AI. Fraud detected is analyzed by the exchanges and take corrective measures or levy penalties on the fraudulent parties.

Sentiment Analysis: Evaluating market sentiments requires opinion mining from sources like social media posts, blogs, articles, etc. This huge data processing uses advanced data mining tools to produce a summary of performance on stocks and commodities and influencing market trends.

Transaction Data: Enrichment of transactional data can help customers monitor the stocks, current prices, futuristic price, and trade better. This data shapes up as historic data after a while and the accuracy of this matter in creating efficient algorithms for financial trading.

Market Predictions: There are no complete predictive solutions in financial trading. The tools that AI provides can convincingly improve the trading abilities, reduce the chances of loss-making, and track the market movements. If, in case 100% accuracy is achievable in predicting the markets the trades will never accomplish. The situation of no profit and no loss-cannot be ideal for any business. A market prediction in this industry is its volatility and stability probabilities. Precautionary actions based on predictions or safe trading as a practice can help traders and investors.

The future of financial trading with Artificial Intelligence:

Secured trading is a result of the numerous calculations that AI performs in negligible time. Absolutely eradicating the past methods is possible when current solutions are effective. AI performs operational transactions, enables high- frequency trades, highlights unprofitable transactions, and most important is it keeps learning to improve.

  1. Automated Trading
  2. Fundamental Analysis
  3. Triggers

The drawback is that we just cannot predict future prices based on historic data; hence at least partial automation is possible. AI can assist in creating a trading account and completing the account opening procedure, send a welcome kit, and introduce the user to trading with training videos.

The trading strategy created and modified with the help of technology scans data and market patterns. It helps predict intraday price movements and recommends trading actions. Queries are resolved and responded accurately based on historic data AI inspects. Intelligent search platforms and tools generate valuable insights based on market behavior to improving trading.

The finance sector is full of opportunities for investors and companies. If we implement Big Data and Artificial Intelligence technology in several fields, the difference in results is noticeable. Execute large trading orders in single or multiple groups using AI. Scheduled trading can save time and efforts of human beings. The trade operations are AI automated, they can control activities that are of repetitive nature for each trade that takes place. Manage the calculations, processing of receivables and payables, account balance, stock holdings.

AI can help finance sector and financial trading activities to provide customer service 24×7. It can process settlements, resolve basic level issues, and share the latest updates to the customers. Investing decisions if AI-supported can benefit the user and it can act as the main investment qualifier for the preferences set by them. Observe the stock performance risks and set targets for the risk capacities we hold or price to profit levels.

Conclusion:

Big data and Artificial Intelligence are almost inseparable, especially with their unique abilities that help businesses. Like knowledge is available everywhere the advantages of Big Data and AI are widespread. The established facts that the finance industry uses this technology extensively is enough to draw advantages and having a competitive edge over others. Humans along with machine help can lead a better financial life.

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.