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I saw a post from one of my friends. She’s a content writer.

She saw some sentences written by GPT-3 and said that she’s scared.

I told her that it’s not like that and sentences generated by GPT need to be structured otherwise they don’t make that much sense.

Although I convinced her but deep inside, I was concerned.

Some days ago, a person posted articles generated by GPT on Reddit and no one was able to detect that he’s using GPT to write those articles.

Except for one person, who figured it out but he was heavily downvoted for saying that and was accused of being disrespectful towards the author.

Again I was concerned.

Let’s see why?

Is AI something that we should be afraid of?

If you say, yes, I’d disagree.

If you say no, I’d again disagree.

Important Concerns

But still, there are some important concerns about what our future would look like.

Age of Disinformation

We live in an age of disinformation.

Rumors, fake news, and a lot of bad things are already there. We have witnessed mass events of unrest due to these and we are continuing to do.

Regimes around the world use fake facts and figures to emphasize and support their point of view and use it to suppress the opposing voices everywhere.

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And this all is happening with not-so-sophisticated technologies and tools.

What if the same protagonists start using the AI to fuel their propaganda?

Who’ll be responsible for this?

Mass Surveillance

Again, we are seeing mass surveillance programs by countries around the world. Be it their own citizens or not, they are using AI to even collect the most private details of a person.

Some countries are even said to be working on programs that are able to predict the ‘crime’ or ‘behavior’ of a person. It’s not that simple as it’s preached.

Every country has got a separate definition of ‘crime’ and in most cases, those definitions are used to persecute citizens who are protesting for their rights.

What if we live in a world where you can’t even think of protesting for your own rights?

Sounds scary, right?

Deep Fakes

If you have been following AI news for a while, you might have seen and heard a lot about deep fakes. They have been around for some years and are one of the most concerning things AI has made possible.

Deepfakes are actually synthetic videos and images in which an existing image or video has been used and the likeness of the person in the video replaced by something else.

Although faking is not a new thing in the tech world, but deep fakes leverage the power of sophisticated AI and Machine learning algorithms to mimic the behavior and actions of a person.

Just think how scary it could be for the world if two countries who are in a cold war with each other get someone from their leadership’s deep fake generated with some fiery words against the other party?

Loss of jobs

The most concerning thing that is also stressed a lot by AI leaders is the excessive automation that’ll be the ultimate result of once AI starts disrupting every industry.

Loss of the jobs for workers who are in roles that involve repetitive work is the imminent result of this revolution. Thousands in the workforce will be jobless in no time and we don’t have any immediate solution for that.

You see due to the present pandemic situation and overall economic condition of the world, we are already witnessing the highest unemployment rates throughout the world.

What if AI adds more to this? Are we prepared for it?

Hacking

Since AI allows scripts and programs to predict different things, it is already being used to avoid cyber attacks and is helping companies and organizations solve their security-related problems.

On the other hand, since AI gives the hacking programs and scripts the power to self-improve and adapt to different behaviors, it’s predicted that in the coming years, hacking backed by AI would be one of the major concerns.

Hackers can write scripts that monitor websites and then those scripts can trigger themselves using some AI algorithms and attack websites using different techniques like phishing, SQL injection, and others.

Do you think hacking using AI would be a major problem in the future?

Conclusion

AI and the 4th Industrial Revolution is the biggest opportunity in human history and it’s said to disrupt every walk of human life. It’ll help the world to solve most of the challenging problems related to vice, poverty, and disease harnessing the power of AI. We will be able to have a better and comfortable lifestyle.

Everywhere around us, there’d be self-driving cars, IoT devices, drones, and exciting things. But at the same time, it’ll be one of the biggest challenges for us to face the above challenges and many more which are unknown to us today.

Now, do you think should we be afraid of AI or still embrace it?

Would we be really able to solve the problems around us as we are expecting?

Would we be able to finally have a better world for everyone that’s based on the principles of quality, justice, and pluralism?

These and hundreds of such questions remain unanswered.

No one knows the answer. Neither you, me, and AI leaders.

Are we at the mercy of AI now?

Think about it!

Don’t forget to give us your ? !

Why We Should Be Afraid of AI? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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This exciting new industry is growing by more than 42% annually and will reach a market size of $733 billion by 2027. In the future, all services and products by the time they reach the consumer have at least once encountered artificial intelligence. The growth and viability of the industry are obvious to everyone. If something is so evident, why not exploit it today and plant the seeds of our future wealth? Here are 4 ways we can make more money in the 2020s using AI.

The industry potential

If we mention the year 2020, surely everyone will immediately associate it with a pandemic. A mass of Internet memes was made about how 2020 was not the most pleasant year for humanity. Of course, this negative mood did not develop for no reason. In addition to human loss and endangering our mental health, another notable category is the economic downturn. Entire industries shut down overnight.

Artificial intelligence is fully intertwined with digital life and services. That is why this market has not suffered a hard downturn due to the months of the pandemic. According to IDC, a highly prestigious global market research firm with decades of history, revenues from the AI ​​market will be around $156.6 billion in 2020, an increase of more than 12% compared to 2019. The study notes that while growth was still in double digits, it was slower than many had expected.

Artificial intelligence revenues are all projected to multiply over time. Emphasizing this fact is very important when trying to assess future business potential. There is no jump-like revenue growth or success stories in markets that are already in an anxious age. In this sense, let us also consider the market as the body of an organic organism. An old horse is less competitive than a young one. In this situation, let’s put the horse’s competitiveness in parallel with business opportunities. In a young market, it is always easier to make good decisions.

1. Who are the biggest companies in the AI ​​industry and how can we benefit from their success?

Artificial intelligence goes hand in hand with large amounts of data. It follows that which company has more data is also ahead of AI.

Amazon, Google, Microsoft are all in fierce competition with each other for developers, who will use their cloud service to develop their newer models and algorithms.

Let’s see for ourselves the process as the industry grows. More users arrive, more services are born, and more products. To create these, at some point, you need to take advantage of an existing AI industry pillar service provided by a giant. Think about computing power, processing storage, chips. Many small companies will depend on the services of the big ones, thanks to which they will surely grow together with the industry in this field.

These giants are all publicly traded companies, we can buy their shares on the stock market. Thereafter, we can reap the rewards from dividend or stock price increases. Investing in giants is a possible way to increase our wealth. Indirectly with artificial intelligence.

The increase in profits in the industry may lead us to conclude that the shares of large players will also increase over time. By investing in and out of these, we can embrace and sit on the train of growth.

2. Startup

Not only giant companies exist in the market and surely they will not be the only ones to solve problems. At the corporate level, with thousands of employees, decision-making and direction of development can no longer be flexible. It is for this reason that there are many problems outside the periphery of large companies that need to be solved. But this is not a problem, on the contrary, an excellent opportunity. Small startups specialize in solving these problems. It’s worth mentioning here that most of these startups don’t look like “pure AI startups” but operate with artificial intelligence applied within some other industry.

These other industries could be for example healthcare, finance, insurance, and so on. Massachusetts startup PathAI uses machine learning to be able to produce more accurate cancer diagnosis.

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With startups, we can also profit as an investor, but of course, we have to be capital-strong for this. Another possible way is to set up a startup ourselves to solve a similarly burning problem. To do this, we need to find a workable solution to a well-recognized problem and team up with professionals who know the right technology. The management can then decide on the fate of our startup company’s revenues.

Another option is to exit our small company. We sell our stake to a larger company or a capital-intensive entrepreneur.

3. AI trading bot

Another growth opportunity is the development of trading algorithms. This point also looks back a bit on the previous one. Data volumes are generated as a result of cryptocurrency price movements. Many are trying to develop algorithms that successfully predict market movements for this data. We can go this route on our own and try to develop such a robot ourselves. Or we can use a bot that someone has already developed. The multi-award-winning French fintech startup b-cube.ai itself specializes in the development of these AI crypto bots. By subscribing to one of them, we can integrate the bot into our own existing account at the exchange itself. With this, the robot will only get permission to buy and sell, eliminating the risk of any Ponzi scheme. Using the help of historical market movements and conclusions filtered from the sentiment of the participants, the AI engine forms predictive signals, with the aim of consistent returns in the long run. Although a 100% success rate is not possible, if the signal finds a target more than the breakeven point, we achieve nice results.

4. Contract work

Perhaps the easiest way is for us to have (or acquire) enough expertise ourselves and work for a company that wants to harness our knowledge. We see a breathtaking number if we go a little after the annual salary of a machine learning engineer. According to a study by Indeed, the average annual salary was $146,085 in 2018, an increase of more than 344% compared to 2015. This, of course, varies from country to country, this number will not be as high everywhere as in the United States, but we can agree that we will not have useless knowledge by getting expertise in ML.

Today, we don’t even have to work locally. The digital age has brought, and the pandemic has even made the mainstream consider remote or freelance work. All you need is a laptop and you can carry it from anywhere.

However, it is true for the ways of making money mentioned above that they are exclusive to some degree. For each version, we have to have something that unfortunately the majority of the population does not have. Be it capital, expertise, programming knowledge. On the other hand, if we have one or even more of these, artificial intelligence can hold for us opportunities like the Internet has meant to the brave dreamers of the time a few decades ago.

Don’t miss it!

Disclaimer

The information provided on this website does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the article’s content as such. The author, website or the company associated with them does not recommend that any cryptocurrency should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.

Don’t forget to give us your ? !

Ways to use AI to generate wealth in the next decade was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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How to evaluate the Machine Learning models? — Part 2

This is second part of the metric series where, we will discuss about correlations — continuous to continuous and dichotomous (categorical) to continuous. When it comes to feature selection in machine learning as well as in dimensionality reduction correlation plays a very crucial role — decides which feature will have high impact on the model and in several cases it is treated as the metric for model evaluation.

Over of correlation matrices if given below, few of them we will be discuss in this article. In first part of this metric series, we have discussed eight statistical metrics, in this part we are going to discuss Correlation Metrics used to evaluate the model as well as feature .

Correlation

Correlation is defined as the relationship between two variable generally IV and DV, it ranges between [-1 ,1], where as -1 means highly negative correlation i.e. increase in D.V will tends to decrease in I.V or vice versa, 0 means no correlation i.e. no relationship exits between I.V and D.V and 1 means highly positive correlation i.e. increase in D.V will tends to increase in I.V or vice versa.

While implementation we use to set threshold for both side, so that we can incorporate the IV which are highly correlated in term of absolute value of magnitude and then we process further to build the model. Below are the correlation metrices:

1. Pearson Correlation

Pearson correlation also known as the Pearson’s, the Pearson product- moment correlation coefficient between two variable which is denoted by “r”. It measures linear association between two variable. It is parametric correlation. It ranges from [-1,1] where,

0= no relation,

1= positive relation,

-1 = Negative relation

Pearson Correlation is defined as the covariance of two variable normalized by the product of the standard deviation of the variables.

Assumptions of Pearson Correlation:

1. I.V and DV are normally distributed i.e. mean =0 and variance=1 or bell shape curve.
2. Both IV and DV are Continuous and as well Linearly related.
3. No outliers are present in the IV and as well as DV.
4. IV and DV are Homoscedasticity (having the same scatter from the fitted line)

Steps of Pearson Correlation:

1. Find the of covariance of X and Y.

2. Find the standard deviation of X and Y.

3. Multiply the covariance of X and Y and divide by multiplication of the standard deviation of X and Y.

Below is the question solved by the Pearson Correlation.

Jupyter Notebook: link

2. Spearman Correlation

Spearman Correlation is a non-parametric test which is used to measure the association between the two variable. It is equal to the Pearson Correlation and it calculated as same as the Pearson Correlation but instead of the value we use rank of the variable. It ranges from [-1,1] where,

0= no relation,

1= positive relation,

-1 = Negative relation

Assumption:

1. IV and DV are linear and as well as monotonous.
2. IV and DV are Continuous and as well as Ordinal.
3. IV and DV are Normally Distributed.

Steps of Spearman Correlation:

1. Rank the variable X and Y
2. Calculate the difference of the rank i.e. d=R(x)-R(y)
3. Raise d to power of 2 and perform summation.
4. Calculate n*(n-1), where n is no of the variable.
5. Substitute the respective value in the below formulae.
6. (optional) you can use the rank of the variable in pearson’s formulae and calculate the Spearman Correlation.

Jupyter Notebook: link

3. Kendall’s Rank Correlation

It is non-parametric measure of the relationship between the columns of the ranked data. It is preferred over Spearman correlation because of the low GES (Gross Error Sensivity) and as well as the Asymptotic Variance. It ranges from [-1,1] where,

0= no relation,

1= positive relation,

-1 = Negative relation

Assumption:

1. IV and DV are linear and as well as monotonous.
2. IV and DV are Continuous and as well as Ordinal.
3. IV and DV are Normally Distributed.

A quirk test can produce the negative coefficient hence making the range between[-1,1] but when we are using ranked data the negative value does no0t mean much. Several version of tau correlation exists such as tau A, Tau B and Tau C. Tau a used with squared table (no. of row==no. of columns), and Tau C for rectangular columns.

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Kendal tau= (C-D)/(C+D) where,

C is concordant and D is Discordant.

Briefly, Concordant means rank are ordered in same order irrespective of the rank(X increases Y also increases)and Discordant (X increases Y also decreases )means rank are ordered in opposite way irrespective of rank.

Steps of Kendall’s Rank Correlation:

Steps to find Concordant:

1. Take any column, in below image, i have taken Interviewer 2.
2. For first value in the selected feature i.e. Interviewer 2, count no of value in the respective column starting from next row which is greater than the selected value in row . Suppose you are calculating concordant for ith row, so you will take values from (i+1)th row from the Interview column.

Steps to find Discordant:

1. Take any column, in below image, i have taken Interviewer 2.
2. For first value in the selected feature i.e. Interviewer 2, count no of value in the respective column starting from next row which is smaller than the selected value in row . Suppose you are calculating concordant for ith row, so you will take values from (i+1)th row from the Interview column.

Jupyter Notebook: link

4. Distance Correlation

Distance correlation is used to test the relation between the IV and DV which are Linear or Nonlinear, in contrast with the Pearson which is applicable to only Linear related data. Statistical test of dependence can be performed with a permutation test. It ranges from 0,1 where,

0= no relation,

1= positive relation,

-1 = Negative relation

Steps of Distance Correlation:

1. Calculate distance metric of X and Y .
2. Calculate doubly center for X and Y each element.
3. Multiply the doubly center of X and Y for each term take the summation and finally divide by n raised to 2.

Jupyter Notebook: link

5. Biweight MidCorrelation

This type of correlation is median base instead of traditionally mean base, also know as bicorr – measures similarity between samples. It is less affected by outlier because it is independent of mean. It ranges from 0,1 where,

0= no relation,

1= perfect relation.

1= negative perfect relation.

Assumptions of Biweight MidCorrelation:

1. I.V and DV are normally distributed i.e. mean =0 and variance=1 or bell shape curve.
2. Both IV and DV are Continuous or ordinal.

Steps of Distance Correlation:

Follow the instruction/steps mentioned in the below image:

Jupyter Notebook: link

6. Gamma Correlation

It is measurement of rank correlation equivalent to the Kendal rank correlation. It is robust to outliers. It’s goal is to predict where new value will rank. It is applicable to data which are tied with their respective rank. It ranges from 0,1 where,

0= no relation,

1= perfect relation.

1= negative perfect relation.

Jupyter Notebook: link

7. Point Biserial Correlation

This type of of the correlation is used to calculate the relationship between dichotomous(binary) variable and continuous variable works well then IV and DV are linearly dependent. IT is same as the Pearson correlation the only difference is that it works with dichotomous and continuous variable. It ranges from 0,1 where,

0= no relation,

1= perfect relation.

1= negative perfect relation.

Assumptions of Point Biserial Correlation:

1. I.V and DV are normally distributed i.e. mean =0 and variance=1 or bell shape curve.
2. Anyone of them IV or DV should Continuous or ordinal and another should be dichotomous.

Formulae and Steps of Point Biserial Correlation:

Follow the instruction/steps mentioned in the below image:

Jupyter Notebook: link

8. Biserial correlation

Biserial correlation is almost the same as point biserial correlation, but one of the variables is dichotomous ordinal data and has an underlying continuity. It ranges from 0,1 where,

0= no relation,

1= perfect relation.

1= negative perfect relation.

Assumptions of Biserial correlation :

1. I.V and DV are normally distributed i.e. mean =0 and variance=1 or bell shape curve.
2. Anyone of them IV or DV should ordinal which has underlying continuity and another should be dichotomous.

Steps of Biserial correlation:

Follow the instruction/steps mentioned in the below image:

Relationship between Biserial correlation and Point Biserial Correlation.

Jupyter Notebook: link

9. Partial Correlation

Partial correlation we measure the strength of correlation between the variable by controlling one or more variable. Like if dataset has 5 IV then we calculate correlation with 1 IV and DV and then we calculate correlation between 2 IV and DV and so on. Therefore some discrepancies is there when we compare t and p-value from other correlation method.

Jupyter Notebook: link

Special Thanks:

As we say “Car is useless if it doesn’t have a good engine” similarly student is useless without proper guidance and motivation. I will like to thank my Guru as well as my Idol “Dr. P. Supraja”- guided me throughout the journey, from bottom of my heart. As a Guru, she has lighted the best available path for me, motivated me whenever I encountered failure or roadblock- without her support and motivation this was an impossible task for me.

Contact me:

If you have any query feel free to contact me on any of the below-mentioned options:

Website: www.rstiwari.com

Google Form: https://forms.gle/mhDYQKQJKtAKP78V7

Notebook for Reference:

Jovian Notebook: https://jovian.ai/tiwari12-rst/correlation

Jovian Notebook: https://jovian.ml/tiwari12-rst/metrices-part1

References:

Correlation Types: https://easystats.github.io/correlation/articles/types.html

Environment Set-up: https://medium.com/@tiwari11.rst

Jovian: https://jovian.ml/docs/user-guide/install.html

Youtube : https://www.youtube.com/channel/UCFG5x-VHtutn3zQzWBkXyFQ

Don’t forget to give us your ? !

How to evaluate the Machine Learning models? — Part 2 was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Latest Enterprise software development technologies

Top 7 Enterprise Software Development Technologies to Adopt in 2021

Here is the list of best software development technologies that can help in making better business plans,Internet of Things, Machine Learning, Artificial Intelligence, Bigdata. etc are currently the most growing software technologies in 2021. Read on to know how they can do wonders for your business.

Globalization has impelled start-ups, small and medium enterprises (SMEs), and other businesses to stretch arms and think beyond the old and traditional methodology and practices for software application development.

That being said, enterprise software has its own special set of priorities. The architecture must be scalable but cost-effective, secure but easy to use, and most of all it must deliver the kind of high-quality user experience that gets results.

Trying to balance commercial pressures with technical realities can be a challenge even for experienced developers.

What gives those experienced developers the edge is knowing the best tools for the job?

Each technology has different advantages and limitations; Using those whose strengths play into those business priorities leads to superior software.

This list of software development technologies would definitely help enterprises in making better plans and gaining a leg-up in the tough competition. Let’s quickly check out the most promising 2021’s software development technologies for your business:-

1) Big Data

The increasing employment in Big Data clearly indicates its success graph. As per Statista, this software development technology will reach approx. $103 billion by the year 2027.

As Big data comprises data-gathering strategies & wide range of analytics, it can be used to transform large amounts of raw and unstructured data into valuable information with ease.

The data can be taken from social media, emails, web/mobile applications, corporate sources, video/audio records etc. and used by different industry verticals for their business process.

This software development technology consists of various processing tools to provide benefits like improved customer experience, predictive analytics, accurate credit scoring, risk management, and efficient customer insights.

Apps Idea with Big data software development technology

Grading Systems, Career Prediction, Cyber Security, weather forecasting, Congestion management, Risk mitigation, Business clarity, Customer statistics alteration, Money laundering, Gender and Age Detection

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Big Data Application in top industry verticals

1. Energy & Utilities (Smart meter)

2. Transportation( Revenue management apps, Route planning, Traffic control apps)

3. Retail & trading (POS scanners, RFID, Customer loyalty cards)

4. Insurance(Customer behaviour insights, Claims management)

5. Education industry(Learning apps, Data security apps)

6. Healthcare industry(Real-time patient monitoring apps, Sentiment analysis apps)

7. Banking Apps(Financial activity tracker, NLP)

2) Blockchain

From the last two year stats, it can be seen that Blockchain has been greatly adopted in different industry verticals. This software development technology claims decentralized digital data record to provide secure data transactions without the involvement of third-party tools.

Almost every industry including agriculture, finance/banking, healthcare, logistics, manufacturing, trading & crowdfunding are leveraging benefits from this.

Blockchain has introduced smart contracts, Ethereum, Solidity platforms that can be written into code and automatically monitors its completion without involving intermediaries.

It is predicted by Statista, 55% of healthcare apps will get deployed with Blockchain integration for commercial use by 2025. Moreover, the blockchain market is expected to climb to over 23.3 billion U.S. dollars by 2023. The food & agriculture industry will leverage the maximum benefits from Blockchain.

Blockchain app ideas for business in 2021

Distributed Cloud Storage, Secure Payments, Proof-of-Provenance, Loyalty Programs, Smart Contracts, Digital Identity of passports, Voting security

Industry Apps With Blockchain

1. Real Estate Apps (Ubiquity App)
2. Supply-Chain Management apps(Food distributors, pharmaceutical enterprises apps)
3. Ride-Sharing Apps (Uber, Ola)
4. Apps for Government (Voting systems)
5. Medical Records (Guardtime)
6. Funds Transfer Apps (Abra, Bitwage, Coinpip)

3) AI & ML

Artificial intelligence and machine learning technologies are the most appealing one these days that should not come as a great surprise now. The wheel of innovation is already rolling on the path of success carrying ready-made platforms & libraries for data analysis.

Theano, Keras, AWS ML, TensorFlow are the big examples that software developers are using to develop AI & ML applications.

As per the recent survey by O’Reilly Media, 51% of European companies and 49% of American companies are heavily adopting Machine learning technology for their upcoming projects.

For massive businesses, the rise of AI as a service(AIaas) has brought machine learning to enable powerful features such as voice commands, chatbots, automatic search engine suggestions, and facial recognition.

No doubt, the combination of both software development technologies(AI & ML) will be greatly leveraged by enterprises in the coming months of 2021.

Top Ideas catering your AI & ML development needs

Sentiment Analysis, Image Recognition, Speech Recognition, Author Identification, Language Identification, Robot Control, Video Surveillance

Use Cases(AI/ML software development technologies)

1. AI in the Healthcare Industry (Redivus Health, BillyScreen)
2. Streamlined Manufacturing with AI (Codegreen Energy )
3. Market Prediction Apps(FRED Economic Data, NetDania Forex)
4. eCommerce apps (Alexa, eBay)
5. Cybersecurity apps(LogRhythm, CrowdStrike )
6. Chatbots (Recruitment chatbot, Erica)
7. Education apps(Goodreads, Udemy)

4) Internet of Things

Internet of Things is one of the best software development technologies today which has connected the Internet to every next thing with each other. It allows exchanging data over the connected devices and enables them to be connected until the process gets over.

According to Gartner report, IoT devices will reach approx. 30 billion by 2021.

IoT can provide your business with IoT of benefits including less operational cost, enhance business growth, collect large amounts of data, inventory management without loss. Healthcare, construction, and telecommunication are the major sectors that will leverage the highest benefits in the future as per Statista.

App Ideas for IOT

Motor Speed Control, Weather Reporting System, Soil Moisture Detection, Remote Control Car, IoT Based Smart Mirror, Automatic Drawing Machine, Health Monitoring System, Surveillance Camera, Facial Recognition Door Unlock

Uses cases for IOT

1. Retail and Supply Chain Apps (Amazon Go)
2. Logistics solutions (thethings.iO, fleet management app)
3. Agriculture IOT apps(Blossom)
4. Automotive solutions (Cobra Code)
5. Social applications (Pawscout Pet Finder)
6. Healthcare and Fitness Apps (HERO)
7. Home Automation solutions (Nest)

5) Mixed Reality

Mixed reality defines the combination of Augmented Reality & Virtual Reality which creates immersive environments with digital as well as physical objects interacting with each other in real-time. As per Juniper Research, the mobile market value of mixed reality will cost approx. $43 billion by 2024.

This combination will transform many industries including education systems, media & entertainment, healthcare industry, retail, military training & remote working area.

Various big brands like Deloitte, Ford & Volvo are using mixed reality to provide best user experience to their customers and to achieve excellence.

App ideas for mixed reality software development technologies:-

Theft Protection, Real-time Information Overlay, Training and Employee Education, Engineering and Design Modeling, create 3D paintings, amusing photo effects, gamify everything, navigate through new locations, try new clothes using app, Book Illustrations, Smart Home Design, Autocorrect Handwriting, Reading Pet Emotions

Important use-cases of Mixed Reality technology:

1. Supply chain management(Toshiba, BAE Systems)
2. Education sector (HoloLens devices)
3. Automobile design (Ford, Renault Truck, Volvo and Audi)
4. Media & entertainment (Arizona Sunshine, Form)
5. Marketing & B2B sales

Two more on top software development technologies…

6) PWA

Progressive web applications are another trend you should be aware of. A PWA is a combination of a website and mobile application functionality that acts as a native solution. Offering users an immersive full-screen experience, it can be installed directly from the site and live on the smartphone’s home screen. So you don’t have to open the app store.

Improved performance, attractive user experience (UX), faster load time, and less data usage are the main advantages of PWA. Many world-famous companies and projects like Forbes, Twitter, AliExpress, Pinterest, Lancôme, etc. have already integrated PWA solutions.

Due to PWA integration, the organizations can achieve outstanding results such as 104% increase in conversions for new users, 82% increase in conversion rate for iOS users, 2 times more page views per session, 74% of increase in time per session.

Major business benefits of using PWA:-

1. Enhanced user experience
2. Improved performance
3. Less data usage
4. Faster loading time

Examples: Forbes, Twitter Lite, Flipkart, AliExpress, MakeMyTrip, Pinterest

7) RPA

Robotic process automation (RPA) is another software development technology that will change the business scenario in the future by reducing costs and increasing efficiencies.

It is the use of software to automate business processes such as application interpretation, transaction processing, data handling, and even email response. As per the Gartner report, the total spending on this technology will reach $2.4 billion in the year 2022.

Using this technology, the time as well as manual efforts for repetitive tasks can be saved. In this way, the human workforce can shift their focus on other higher-level activities.

Here are some best examples of Robotic Process Automation in practice:-

1. Claims administration in insurance & healthcare sector
2. Scheduling systems for healthcare appointments
3. Fetch data from multiple sites to provide the best deal
4. Expense management
5. Processing of credit card applications
6. Better help desk
7. Help in the onboarding process
8. Automated Data entry work
9. Supports call center operations

Conclusion:

If you still have questions about whether your current business software solution meets modern cybersecurity standards, or if you think it lacks some technologies that can give you a competitive edge, I can help you out!

In this endeavor, opting for an authentic and reliable partner is highly important as it involves your data security as well. Being an IT consultant I would suggest you to hire expert enterprise app development company. You must try a few options, get the quote from them as per your requirements, and then compare and finalise them.

Don’t forget to give us your ? !

Top 7 Enterprise Software Development Technologies to Adopt in 2020 was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/top-7-enterprise-software-development-technologies-to-adopt-in-2020-bf6b2bfa5650?source=rss—-5e5bef33608a—4

Flit towards AVs — how fleet management technology can help?

The autonomous vehicles’ race has been hot for a while. However, there is one field, less heard of, whose companies collect data that brings tremendous value and paves the way for Autonomous Vehicles — the fleet management technology industry.

I will focus on three key components already covered within fleet management, and explain how they contribute to the future of AVs.

1 — Maintenance

For Autonomous vehicles, remote maintenance tools are crucial to enhance performance and safety on the road when there’s no driver to watch over the car.

• Refueling/Recharging (In the AV case — battery usage). For AVs, it’s essential to know how to plan the upcoming trips appropriately. The system knows, for example, that from Monday to Friday, you commute to the workplace, the same as human fleet managers know how to load balance their fleet based on different days of the week. Therefore, it can schedule battery charging according to actual and planned usage. Moreover, the system is equipped with GPS analytics and can identify map locations in which charging would occur at the right timing. As in current fleet management, this is all about maximizing the car’s utility to the owner, not necessarily maximizing road time.

• Route tracking: Any vehicle needs to plan which route to take based on various factors — some, as mentioned before, are related to maintenance, while others are related to road conditions. Fleet management tools can find vehicles and route history on a map with real-time GPS tracking. Point-by-point location information includes the date, time, altitude, speed, and any moments of risky driving behavior. This data matters because it helps us to forecast the road conditions and predict the next steps. For example, if most vehicles break instantly at a specific location, we can understand that there might be a hazard on that spot. This information can shed light on road structure/conditions and help optimize our fleet — either managing driving behavior in this specific road or trying to avoid this road overall.
• Kilometers remaining until scheduled service Whether it’s a personal AV or part of a fleet such as Uber, this aspect is important. The AV is a new kind of more complex vehicle that consumes much power to process and run algorithms. We should know how to maintain safety, and vehicle function at least as the way do now — which poses a challenge, having no driver who ‘feels’ the car is ‘a bit off.’ We can use the data collected from EV car fleets today to predict when is the next time the vehicle should get to the shop and ensure this does not have urgent business implications.

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2 — ADAS/Behavior

ADAS (Advanced Driver Assistance System) smart solutions take us one step closer to fully autonomous driving, not only by advancing safety but also by capturing data that could be used for training. Many fleets are equipped with camera vision (even 360 degrees), allowing to capture footage of the road ahead and the vehicle’s cabin to detect risk wherever it occurs.

For this feature, I think it’s pretty obvious to have both a camera in front of the vehicle, one behind it, and one within the cabin itself to monitor driver behavior.

These cameras’ data is perfect for AV as it captures precisely human behavior and reaction to road conditions as we humans see it.

The computer vision data can be used to “teach” AV models how we humans drive. Moreover, I think this type of data is needed for AVs to imitate the exact human behavior: people don’t have lidars or thermal cameras, just our eyes, and we get pretty good results — there’s much to learn from both how we drive, and how we REACT to road conditions even before we made a driving decision.

3 — AV fleet management

When you manage a fleet of AVs, you have much more flexibility as human limitations do not apply. However, you also lose the independent judgment of human drivers. Thus you have to issue very precise instructions.

Today’s tools allow you to get instant access to live video from the road, manage risky driving, and review recordings to investigate incidents at any time. By doing so, this feature allows AV fleet management to scale their capabilities and monitor the vehicles to perform better, and eventually make an optimized decision for the fleet.

For example, we can optimize scheduling for real-time driving requests like Uber or food shipment or fleet management for trucks. We already have all statistics: their ride, their schedule, their performance etc — now, we can actively manage the AV fleet using this data to optimize the entire fleet’s results.

Conclusion –

Data has become the next currency and is valued more than ever (ask Google). Moreover, data is a big part of what we need to accelerate AV R&D. Fleet management technology exists today and produces a ton of data — a perfect combo.

Thank you for reading!

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Flit towards AVs — how fleet management technology can help? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/flit-towards-avs-how-fleet-management-technology-can-help-930c7ca4f480?source=rss—-5e5bef33608a—4