Amazon had recently made an announcement of open sourcing one of its device software DeepRacer which means that the software DeepRacer is freely available for anyone to modify or redistribute according to their requirements or plans. The DeepRacer was first developed by AWS. Lets’ look at what exactly is DeepRacer which was announced by Amazon.
Amazon Web Services is a subsidiary company of Amazon Inc., The company comes under the web services and cloud computing industry. The company launched its web services in the year 2002 and cloud computing in the year 2006.
Some of the products and services of the company include computing, networking, storage, analytics, database, deployment, application services, machine learning, management and so on. The most popular is Amazon Elastic Compute Cloud (EC2).
DeepRacer is known as AWS DeepRacer as it was developed by Amazon Web Services (AWS). The company had introduced DeepRacer in the year 2018. The objective was to help the developers learn Machine Learning in an easier and a fun way.
AWS DeepRacer
AWS DeepRacer is a race car with an autonomous 1/18th scale. The car is designed to test the reinforcement learning models by conducting virtual races or physical races. The virtual races will be conducted in the AWS DeepRacer Console and the physical races will be conducted on a track at Amazon Web Services or customer events.
Objective of DeepRacer
The main objective or idea behind AWS DeepRacer is to teach the developers basics of machine learning. After the introduction of AWS DeepRacer in the year 2018, the company has conducted various physical races with DeepRacer known as AWS DeepRacer leagues and recently they have conducted a wide range of virtual races as well.
In order to encourage new people to get involved with the technology and to encourage the machine learning process, the company had reorganized the leagues last month. The announcement of open sourcing is also another step taken by the company to increase the number of participants.
It is a step to increase the interest of developers as it is a chance to make it their own and they will be able to customize it and add new layers to the cars which were not possible until now. This is a significant step taken towards encouraging the machine learning process.
After the launch of AWS DeepRacer in the year 2018, it has evolved significantly which has led the company to announce a new step which is open sourcing the software of these cars. The DeepRacer car is a mini computer that is running using an Ubuntu Linux and a Robot Operating System (ROS).
Both the software is open source elements. The open sourcing will enable the users to change the car’s default behaviour and the company believes that by open sourcing the device, there will be a lot of creative uses of DeepRacer coming forward by the software developers.
The open source for the AWS DeepRacer will enable the software developers to easily change the device code of the race car which is programmed only for the track into anything useful and creative.
Some of the changes you can make are you can deploy countermeasures into the car so that it won’t let other cars to overtake it or you can deploy your own counter algorithm which will make it run faster from a specific point to another.
The company wrote in its blog post about these examples and added saying that you just need to dream it and code it.
AWS DeepRacer is an autonomous 1/18th scale race car designed to test RL models by racing on a physical track.
What is AWS DeepRacer league?
The AWS DeepRacer League is the world’s first global autonomous racing league for developers.
Which geographic regions is AWS DeepRacer available in?
AWS DeepRacer simulator is available in the US East (N. Virginia) Region.
Conclusion
AWS DeepRacer is one of the most efficient ways to learn machine learning for developers. It is designed for developers of all skill levels and even the ones who have no experience in Machine Learning.
Gone the days of photo shopping where fake images only remained as a joke. The advancement in this technology with empowering tools such as AI has everyone worried. Recently, a lot of pornographic images and videos of Hollywood actresses and famous celebrities which are highly convincing, have been circling over the internet and millions of people have been sharing the same. These obnoxious images and videos were later found to be forged and were declared fake by forensic experts.
Social media, the bearer and broadcaster of fake news too is posing a threat since the news it upholds spreads like wildfire and leaves little to do when the news is a hoax or a rumor. Remember what happened during #pizzagate? Day by day its what we see vs what we believe.
The video where Barrack Obama was seen talking rather unusually and calling Donald Trump a “Dipshit” also went viral, only to learn that it was American actor Jordan Peele just playing around with Artificial Intelligence technology. What is this technology that is so make-believe that we often forget to comprehend if it’s actually authentic? Today we’re going to explore the AI that has taken the internet by the storm and is seen as a potential threat to the near future. Deepfakes.
Deepfakes are images or videos created using artificial intelligence where you can create real-looking forged images such as morphing popular faces onto pornographic sites. Some amateurish hobbyists started this on Reddit and this gave birth to the pioneering of so-called deepfake learning.
Deepfakes aren’t just fake images or videos. Some are made with so much precision that it is really daunting to distinguish between a real and a fake. Deepfakes can be created using voice modulations too. So if you have seen a video of Nick Cage appearing in ten different movies he wasn’t a part of, or a politician saying rather unruly things, you have come across a deepfake.
Origin of Deepfakes- GAN
A general adversarial network-GAN is a deep learning technique in AI. Usually, machine learning is a hand-down job where you instruct the computer with commands to complete certain tasks. GAN gives machine learning a new power where it can skip human intervention and self-learn, maybe even imagine and create images that never existed.
In a general adversarial network, two neural networks are pitted against each other. These networks are provided with several images and the two networks, using data from the images creates new plausible images which are so real-looking, you could hardly tell in the first go. They learn from the data provided from several images such as facial expressions, complexions, bone structure, etc.
The neural networks require high end systems with huge data space to create the anticipated outcomes.
These networks trace the movements of pedestrians walking around which has helped develop driver-less cars. They have also studied voice modulations which make Alexa and Siri more conversational.
Deepfakes are using these deep learning techniques and hence the name.
Synthesia is a London-based AI video generating platform where you can create corporate training videos for businesses around the world. The platform let’s you choose an avatar for the video and can be created in over 40 plus languages.
Synthesia Website
In grave times of the pandemic, it has made shoots riskier and Synthesia is slamming the corporate training costs by using deepfake technologies to create these videos.
Recently, it helped David Beckham appear in a public service announcement on malaria where he “spoke” in nine different languages.
Rosebud AI
Rosebud, founded by a team of AI experts, is aiming to disrupting media by creating glossy images used by e-commerce platforms or marketing. It has released around 25000 modelling images of people that don’t exist. It has also launched a service where you can put on clothes photographed on mannequins onto virtual real looking models.
Rosebud ai Website
This could be used by several cosmetic and apparel businesses who are constantly in requirement of models and who spend a fortune going through sets and shoots.
Modulate.ai
Modulate is using the deepfake technology in the gaming industry to analyze speech software that learns how to create Voice skins or filters of famous celebrities or characters. Old school method relies on text-to-speech tech whereas Modulate uses real time audio to synthesize the voice skins.
The voice skins have a real life effect which is radical for the gaming industry. Modulate.ai has raised $2 million through investors like Hyper plane venture Capital and 2Enable Partners.
Like Modulate, Respeecher demonstrates emotional nuance and prides itself in creating a technology of voice cloning. Respeecher aims at revolutionizing the film and animation industry. Since animation and film studios are prone to human error, Respeecher’s voice clones come in handy when actors who are dubbing, quit. Only a good sample of their voice and the technology does its wonders.
Respeecher Website
D-ID
In the wake of privacy concerns with facial recognition synched with sensitive information, D-ID de-identifies and removes identifiable features without losing a grip on important attributes such as age, gender or emotion. Its smart anonymity feature provides top notch security for biometrics databases.
Deepfake technology refers to audiovisual content generated by deep learning AI systems.
How do Deepfakes work?
Deepfakes rely on artificial neural networks, which are computer systems that recognize patterns in data.
What was the first Deepfake?
The first deepfake was coined on 2017 by a Reddit user of the same name. This user created a space on the online news and aggregation site, where they shared pornographic videos that used open source face-swapping technology.
Conclusion
The companies mentioned above are using deepfake tech to do more than just creating videos and images. They’re developing services around the most controversial phenomena and are being applauded for the same.
Lately, Deepfakes have become a common tool for harassment, revenge porn, busting political propagandas and are seldom used as a tool for harmless humor. There are several agencies relentlessly working on confirming deepfakes. But it has worried the entire AI tech industry since the technology is improving at lightning speed and are afraid that it might outrun the ones trying to find a difference or call out a fake.
MarketMuse is an AI-powered keyword planner and content marketing tool created by Jeff Coyle and Aki Balogh in 2013. It uses artificial intelligence and machine learning to provide topic suggestions, analyze content and develop briefs for brands and help you create quality content.
MaketMuse dashboard
It is a definite recommendation for SEO writers that work for medium to large agencies. MarketMuse will help you build strong content marketing strategies that align with your brand goals.
Hence, it is no wonder that MarketMuse has collected a variety of industry awards from the Content Marketing Institute, G2 Crowd, Red Herring, and US Search Awards.
Pages: Stores content on your website and provides insights to improve the content.
Topics: Stores the most common topics of your websites, including topics or areas which can be improved.
Plans: For content planning strategies.
Reports: This allows you to filter and sort data for your content.
Applications include:
Research Type in a topic and MarketMuse will give you a complete list of words to include in you content and also the number of times these words or phrases must be covered in an article.
Compete What content are you competing against? MarketMuse shows you all the top-ranking content so you can collect important keywords and highlights content gaps that you can bridge.
MarketMuse’s Compete dashboard
Questions This application helps when you are looking to rank voice searches and related topics. Questions provide a lot of queries users are searching for in relation to your keyword. These are similar to Google’s “people also ask” questions, but the list is far more dynamic. Not all questions are relevant so the sorting is up to you.
Connect If you are using MarketMuse for your websites, it will suggest internal links to help connect the content you are currently working on to the content you’ve already published.
Optimize One of MarketMuse’s best features, simply to drop your content in the dashboard and MarketMuse ranks it against your competitors, It creates a content score that lets you know how your content fares and also suggests additional topics for you to cover.
The first thing you’ll notice when you sign into MarketMuse is that it provides a lot of options in the dashboard, which can get a bit overwhelming.
However, the app is fairly simple to use. Follow these steps to create the best content you can publish-
Step 1: Before writing anything, head over to the Research application and type in the term you are planning to use as a topic. MarketMuse will return a list of about 50 terms and topics related to the keyword you provided.
Step 2: Next, head over to the Compete tab and type in your keyword again. This will give you a list of the top 20 articles that your content will be competing against – as well as keywords and topics your competitors aren’t targeting.
The boxes in red are topics that aren’t being targeted so you can include those to create a more in-depth piece.
Step 3: Use the Questions tab and look for popular topics and questions. You can add these questions and topics to the list of main keywords once you’ve sorted out the relevant terms. You can then use these keywords in your article for maximum impact.
Step 4: Once you’ve written the piece, you can use the Optimize application to see how your content does against your competition. If your content is below average then you can add more topics that you’ve been suggested and look for areas that you can make more useful.
MarketMuse – Pricing
MarketMuse provides four pricing plans:
Premium Plan – $1499 Pro Plan – $499 Plus Plan – $179 Optimize Plan – $79
Use credits for AI-powered Content Briefs and First Drafts.
A Content Brief is an outline that sets the direction of how an article covers a specific topic.
First Draft is the raw output of MarketMuse AI-generated content, built on the shoulders of a MarketMuse Content Brief. The brief is a competitive deep dive into a specific topic, revealing what’s required to fully optimize a page for that subject.
MarketMuse builds AI content addressing the subheadings and corresponding subtopics of the associated content brief. Analyzing vast amounts of content on the web for each subheading, it then matches it to our subtopics list. Its plagiarism detection algorithm ensures that the AI content construct is always unique.
Use credits to jump-start your content creation process, saving you hours and days of comprehensive research on any given topic.
A query occurs any time you enter a topic in an application (and optionally a URL) and press RUN. For example, if you search for competitive gaps for “what is content intelligence” In Compete, that counts as 1 query.
What are MarketMuse’s applications?
Optimize is a real-time editor that scores your content as you write, helping you to improve the depth and breadth of your article before you publish.
Research analyzes the distribution of related topics on your page to ensure the right depth and comprehensiveness. This is also a great way to find new content to create.
Compete shows the top-ranking pages on a topic and helps you visualize gaps in a competitive heatmap.
Questions help you ensure your content answers the important questions asked by your audience and avoid intent mismatch.
Connect provides internal and external linking recommendations.
Inventory and dashboards will be available to purchase within the platform (pricing information coming soon). These give you personalized insights, shows you Rockstar pages, where your content is too thin, gaps in titles, and more.
What’s the best way to get started with MarketMuse?
You can find their knowledge base with technical information and how to get started, educational blog posts and webinars, and a crash course to jump-start your strategy.
Conclusion
MarketMuse lets writers and marketers focus on creativity and business growth rather than wasting time trying out different keyword planner tools.
When it comes to getting the most out of content marketing, MarketMuse is truly worth it. If you have a fast-growing website or run an agency, MarketMuse is an ideal tool to add to your content marketing kit.
Swiggy was founded in the year 2014. It is currently India’s largest delivery and online food ordering platform. It is operating in 100 different cities in the country. Swiggy has been using AI to improve its orders and delivery. Let’s look at the steps taken by the company to use AI for the smooth delivery of its products.
Swiggy has used AI to grow its order value by over 200%. The company said that the real challenge they faced was real-time decisions or optimizing their products. They said that, when the customers are deciding what to order, the delivery executives keep moving around and restaurants get busy with their customers and orders. Finding a balance between these three was the real challenge faced by them.
Customer Sattisfaction
The company stated that first promise they would maintain is that they will deliver the products within the given time period. They have built an AI system, where the customers will be able to see what they would prefer, at what time, etc. in the company’s platform. It is designed using AI which makes it easier for the customers to choose their order.
The platform is built in a way where it would understand the language used to refer to the same item in different names. For example- In India chicken is addressed using different names in different languages and AI helps in making the platform understand that all these different names means the same dish.
Swiggy’s Delivery Executives
The location of the delivery executives is tracked by the company on a real-time basis. They get to know whether the delivery executive is available to fulfill the order or not using the AI system. This will help the company in confirming the order given by the customers.
The company also needs to understand, whether the executive is a new person or an experienced person which is done through an automation Process.
The delivery executives will have to mark arrived, when they reach a restaurant for picking up the order. The company uses live GPS tracking so that they don’t click arrived even before reaching the restaurant. This helps the company to know that they are physically present in the restaurant.
Valuation of Swiggy ($m)
Swiggy’s AI Solution for Restaurants
In the same way, restaurants will also face similar challenges. It would take different time periods to prepare different dishes. Some may take 20 minutes while others may take up to 30 minutes to prepare. The company has built systems to take into account how much time would it require to prepare the dishes ordered by the customers.
Another area where the company concentrates is on food quality. Sometimes the customer would receive a different dish from the one they would have ordered. The company has built a box that has a camera in it, which will take pictures of the food to ensure that it is the right food. The company informed that it is a computer vision model based on deep learning.
Time and Space
The other prime factor the company takes into consideration is the time and space input. The uniqueness of on-demand services is that the customers need would change according to the time and location. The platform understands, whether it’s morning or afternoon and shows the restaurants and dishes accordingly.
AI Strategy
Swiggy has hired an AI team from across the globe. They have hired individuals who hold multiple PHDs from international universities, and people who work as senior scientists in places like GE research and IBM.
They have built a separate team dedicated only to applied research. This team is different from the data science team. This team will concentrate on solving the problems of the next 100 customers, while there is a team that looks at immediate problems.
The company receives terabytes of data weekly and around 40 billion messages per day. Swiggy has worked on putting this data to develop and build Machine Learning models. The company has said that they have also partnered to get third-party data.
Swiggy is also working on Artificial Intelligence on computer vision where for example a picture of a menu is automatically taken and converted into a file.
They are looking forward to building long-term capability by partnering with other research institutions and industries which are external. Swiggy has also partnered with various universities and institutions where they would fund the research programme and innovative ideas.
This is a step where the university students and Swiggy scientists would be able to work together in developing innovative models. They have also partnered with Amazon Web Services and a lot of startups.
FAQ
Who is the Founder of Swiggy?
Swiggy was founded by Nandan Reddy, Sriharsha Majety, and Rahul Jaimini.
What is the valuation of Swiggy?
Swiggy has a total valuation of $1 billion.
How many orders a day does Swiggy have?
Swiggy has a high number of orders with 1.4 million orders a day.
Conclusion
Swiggy has also made efforts to introduce Artificial Intelligence across the organization. This would help everyone build models around it. They have launched a training programme for their teams, analysts and even business leaders which is “AI for All”.
The vision of the company is to make AI part of their culture which has led them to build the capability and knowledge in the organization said Swiggy’s technological head.
Cybersecurity is at the tipping point entering 2021. Advances in AI and ML are accelerating its technological progress. Technology which can take us forward and empower us, can also show the flip side that is Cyber Crime. By creating cybersecurity systems that encourage diversity and value equality, we can help ensure that technology, innovation, and the future, will be better. We’ve listed technology used in cyber security in this article.
These evolving Cyber security technologies list can help protect your organization.
The future of cybersecurity is in high-speed quantum encryption as Cyber war-fare gets graver day-by-day.
If we look at some trends, then it’s observed that cybercrime is costing organizations on average $3.9 million of data breaches every year around the world. Global scalability of Cybercrime is becoming easier for attackers, as criminals find new business models. It is one of the reasons that 55% of the organizations work together with external partners to reduce security risks.
Technology and security go hand in hand. There’s always have been a war between data defenders and data thieves, so you have to take a stand and be infosec warriors. Here is list of top 5 emerging security technologies that may be of great help.
Top 5 Emerging Technologies That Are The Future Of Cybersecurity
Hardware Authentication
Hardware Authentication is the future of cybersecurity. This approach is used as user authentication that relies on a device like smartphones, laptops, or any hardware systems held by an authorized user. This could be in the form of a basic password or fingerprints to grant access to the device.
The dearth of usernames and passwords are well known, so, a more secure form of authentication is needed. Hardware authentication is an important feature for the Internet of Things (IoT), where a network wants to ensure that the thing trying to gain access to it is something that should have access to it.
One limitation of hardware authentication devices is that they can be lost or stolen, which can create login issues for users.
AI is compared as technology that appears to emulate human performance typically by learning, including conclusions, analyzing complex content, engaging in natural dialog with people, enhancing human cognitive performance and, the major one is replacing people on execution of non-routine tasks.
AI technologies can be used to protect data against increasingly sophisticated and malicious malware, ransomware, and social engineering attacks. AI is not conscious yet, but there is likely a future in AI cognitive autonomy in predicting and mitigating cyber-attacks.
Rediscover the technology behind cybersecurity
If we look otherwise AI and ML go hand in hand in every respect. It basically gets a computer to act without much programming. It combines with AI and is one of the rapid automation of predictive analytics.
ML can provide the fastest way to identify new cyber-attacks, draw statistical inferences, and push that information to endpoint security platforms. Threat intelligence is one of the special cases where AI and ML can be an important functionality for cybersecurity. AI and ML could help with identity management by cross-checking the veracity of data across multiple fragmented databases. Hence, AI and ML are definitely the future of the cybersecurity.
% of AI Cybersecurity for the folloeing areas in organisation
Automated and Adaptive Networks
Automated networks can change the future of cybersecurity. Automation allows for scanning and monitoring of connected networks that can report on deviations and anomalies in real-time. The automatic updating of Defence framework layers i.e. network, endpoint, firewalls, payload and anti-virus; and diagnostic and forensics analysis for cybersecurity. AI and ML can be one of the major components and support applications of these networks.
Cyber Security Competence Survey in different areas
Blockchain Cybersecurity
It is one of the potentially strong cybersecurity technologies that’s rising stupendously. The blockchain technology works on the basis of identification between two transaction parties so this type of security works on the basis of peer-to-peer fundamentals. It offers authentication and resolving a single point of attack simultaneously.
With the help of blockchain technology, a security system used in a company can leverage a distributed public key infrastructure for authenticating devices and users. The use of Blockchain technology with AI can set-up a robust authenticated verification system to keep potential cyber threats at bay. It’s the future of cybersecurity.
Zero Trust model is a response to a breakdown in traditional security models. The zero-trust security model is based on the principle of maintaining strict access controls and not trusting anyone by default, even those already inside the network perimeter. The goal is to use authentication throughout the course to re-ensure security—but in a thoughtful and limited manner, to avoid unduly burdening the user. The key is to know when a re-authentication has actually become necessary due to some malicious or simply anomalous event taking place. This is how the combination of identity technology with application and API protection technology comes into play.
In spite of these technologies and many more upcoming smart cybersecurity techniques, organizations do face a lot of phishing scams. So, have you ever wondered how these Malware actually look like or what are the challenges?
Challenges That Organizations Face With Cybersecurity
Continuous Security Incidences And Breaches
Ineffective Responses And Security Protocols
Too Few Responders are Available for 24/7 Support
Employees are Afraid of Security Threats at Work
Many Organizations are still Unprepared For Security Threats
Phishing Scammers Target Senior Decision-Makers
Increased Weekly Volume of Suspicious Emails
Lack of Training about Security Threats and Scams
To Strengthen Cybersecurity
The upsurge in technology and digital connectivity and more and more cyber-threats has promulgated the need for smart cybersecurity. Smart Cybersecurity is an intellect reflex to manage risk by lessening security gaps that often occurred by reliance on manual processes that are impacted by a continual cybersecurity skills shortage and the administrative burdens of data security management.
A myriad of upcoming technologies can help us enhance cybersecurity and guide the increasingly malicious and disruptive cyber threat landscape.
Frequently Asked Questions – FAQs
Does cybersecurity have a future?
Cybersecurity is at the tipping point entering 2020. Advances in AI and ML are accelerating its technological progress. Technology which can take us forward and empower us, can also show the flip side that is Cyber Crime. By creating cybersecurity systems that encourage diversity and value equality now, we can help to ensure that technology, innovation, and the future, will be better.
Will cybersecurity die?
Cybersecurity will never die. Cybersecurity is at the tipping point entering 2020. Advances in AI and ML are accelerating its technological progress. Technology which can take us forward and empower us, can also show the flip side that is Cyber Crime. By creating cybersecurity systems that encourage diversity and value equality now, we can help to ensure that technology, innovation, and the future, will be better.
Is cybersecurity a good field?
Yes, definitely cybersecurity is one of the growing things in India. Cybersecurity is at the tipping point entering 2020. Advances in AI and ML are accelerating its technological progress.
Why AI is the future of cybersecurity?
AI is compared as a technology that appears to emulate human performance typically by learning, including conclusions, analyzing complex content, engaging in natural dialogs with people, enhancing human cognitive performance and, the major one is replacing people on execution of non-routine tasks. AI technologies can be used to protect data against increasingly sophisticated and malicious malware, ransomware, and social engineering attacks. AI is not conscious yet, but there is likely a future in AI cognitive autonomy in predicting and mitigating cyber-attacks.
Will AI take over cyber security?
No, AI technologies can be used to protect data against increasingly sophisticated and malicious malware, ransomware, and social engineering attacks but cannot fully overtake cybersecurity. AI is not conscious yet, but there is likely a future in AI cognitive autonomy in predicting and mitigating cyber-attacks.
There are so many programming languages these days that deciding which one to start with is a daunting task. However, if your goal is to master the language and earn good money with your skills, then you are at the right place.
Even though there are a lot of programming languages out there, not all of them are useful or pay good money. So it is important to know which languages are on-demand in order to bag the highest paying jobs.
These programming languages are in demand and will remain so at least for a few decades.
Here is the list of languages that are the most relevant in 2021…
One of the highest paying programming languages in the world is Go. You can easily earn well above $100k per year if you learn this language as it is highly efficient and in demand.
Go programming language
Go was developed by Google in the year 2009 which makes it a modern, open-source programming language.
What’s more, you don’t need to spend a single dime to start learning to Go. Many tech giants like Google use Go as it offers some of the best programming features like parallel processing, multi-threaded tasks, fast start-up times, and computer memory usage efficiency.
Another powerful language on the list, Scala also provides you with high-paying job opportunities. Scala has one of the best support for object-oriented and functional programming. It provides more than $75k yearly salaries to developers.
As it is a language that constantly evolves with time, it is likely that it won’t be going anywhere for the next few decades. It is a multi-paradigm programming language, which means that you can easily switch to Scala without major issues. It offers static types that help programmers to avoid bugs in advanced development and complex programming.
It also has great support for JavaScript and JVM runtime environments that help you build high-performance systems. Scala plays a crucial role in big data and machine learning and offers a huge library to perform plenty of operations.
Ruby on Rails
Ruby on Rails is another globally popular programming language that is mainly used to develop web applications. Ruby works on the backend of many popular web applications around the world.
Ruby programming language
Ruby is easy to learn and pays good money especially if you’re a complete beginner. It is relatively easy to create compelling programs as it takes less development time than other programming languages making it a good starting point for developers.
Some key features of Ruby are:
Everything is an object
Provides modules for class hierarchy
Code development
Dynamic typing
Code quality
Objective – C
Objective – C is an improved and modified version of the C language by Apple inc. Since Apple is a trillion dollar organization, you might have an inkling as to how much you would be able to earn as a developer for iOS and Mac OS X. Compared to Google or Windows, iOS has more web apps than either of these companies, making Objective-C one of the highest paying languages for devs to learn.
Objective-C programming language
Even though it is widely sued to develop apps for the Apple ecosystem, it has huge competition from Swift language developers. Suffice to say, as long as Apple exists Objective – C will be high in demand.
Like Objective C was developed by Apple, C# was developed by Microsoft as the improved version of C programming. One of the highest paying languages it is used to develop apps for .NET frameworks.
C# programming language
These days, C# is widely used to create native apps for Microsoft platforms. It is also a general-purpose and object-oriented programming language that is used to create sophisticated algorithms and apps with ease.
You can develop web apps for desktops, web applications, and services. In addition, you can also develop games for PCs and consoles, and a lot more.
Python
The popularity of Python keeps increasing every year. It provides developers with great compatibility and integration features making it a powerful, simple, and user-friendly language.
With all these qualities, Python developers are undoubtedly one of the highest-paid developers there are. Python devs significantly earn more than C, C++, and Java developers while also giving modern languages fair competition.
Python programming language
Python’s major uses are data analysis, machine learning, mathematical calculations, web development, data science, task automation, and much more. It is one of the core programming languages used in Google, Reddit, YouTube, Dropbox, and Netflix.
And finally,
Kotlin
It is also a good-paying programming language for developers. Developed by JetBrains it has become the official programming language for Android app development.
Kotlin programming language
Compared to Java, Kotlin requires less code for app development and helps developers avoid common programming mistakes. Kotlin has been used by many companies like Square, and Google which are some of the best web application producing companies.
These languages will remain the highest paying languages for at least the next few decades. Since technology is growing at a rapid pace, there is always a lack of gifted programmers who can cope with the latest trends and technologies.
In other words, if you want to start meaning good money then you should start your career in programming or coding by learning any of the languages given above. Keep yourself updated with the latest programming practices to become the highest-paid programmer of developers.
For online businesses, it has become necessary to solve the queries of their customers to ensure consumer loyalty along with their brand establishment. Nearly 80% of businesses use a chatbot in one form or the other. But earlier what used to happen was that a man always sat on the answering machines waiting for the call and solve the queries of their customers. So, it was a cumbersome process. What chatbot has replaced now is a man looking for help from machines to remove the constraints of human limitations.
Since the time is the customer service industry which has been revolutionized, and the innovation responsible for this is the chatbot. Chatbots are considered the future of customer service and management.
Emirates increased their engagement rates by 87% after deploying chatbots.
What Are Chatbots?
Chatbots are those computer programs that can have real conversations with you with the help of chat interface, which allows real-time conversations with users. Chatbots are generally used with all of the popular messaging apps. The fun part is when you have conversations with them, they all seem real. For instance, Mark from XYZ company will help you out with FAQs and will direct you to the real seller and then he’ll call you back. Also, there’s no timing issue; if you open a website at 12 midnight, the bot is still ready to have a chat with you.
What is a Chatbot?
Chatbots can understand written and spoken text, and interpret its meaning. The bot can then look up relevant information and deliver it to the user. Most modern smartphone apps rely on chatbots to function. Chatbots have become an important part of business growth and the need for these AI-powered bots is only going to increase in the future.
Technologies Used In Chatbots
The cost and power of a chatbot depend on the technology it uses. There are two categories of chatbots –
1. Command Based Chatbots
As the name says, these types of chatbots rely on a databank of replies and heuristics. The bots reply by selecting an answer that matches the context of a query. Command based chatbots cannot create new texts. These chatbots can answer a set of limited questions so there is a need for manual help at the back-end for atypical questions asked. To answer a question, the chatbot has to understand it first. Command based chatbots use template search or dynamic search for understanding and answering questions.
One main limitation is that the command-based chatbot cannot perform functions outside its code, so what it lacks is the second technology.
Command-based and AI/ML-based chatbots
2. AI Based/ Machine Learning Chatbots
What the command-based chatbot can’t do, the AI/ ML-based chatbot can do it. It can answer all of the ambiguous questions of the user, meaning; you do not have to be specific when asking questions to these chatbots and no databank of replies. The chatbots create replies from scratch using natural language processing. These chatbots become smarter with time, learning from past questions and answers.
The star point, here could be: Both categories have their advantages and disadvantages. Command based chatbots can give more reliable and grammatically correct answers but fail to respond to questions outside their knowledge base. On the other hand, artificial/ machine learning chatbots need a vast amount of learning data and can struggle to form longer sentences.
If you are still wondering why you should get a chatbot for your business then we are going to give you seven different reasons why your business needs a chatbotright now.
Top 7 Reasons ‘Why Your Business Needs A Chatbot?’
1. You Want Your Business To Be Open
Keeping the business around the clock is difficult, especially for small and medium-sized businesses. This means when a customer reaches out to your business, they won’t get a response from you till business hours resume. What chatbot gives you is its incredible feature of time-boundness. There’s no timing issue; if you open a website at 12 of midnight, the bot is still ready to have a chat with you.
A chatbot can ensure that your business remains open all the time in one form or another. In fact, it can do more than just answer your customer’s questions. It can make reservations, ask about the menu, and give a plethora of different details about your business.
no timing issue; if you open a website at 12 of midnight, the bot is still ready to have a chat with you.
2. You Can Build Trust By Answering Questions
You know the drill when a small business gets on start then there’s not only one single established business. This means that the competition is always fierce and you need all the help you can get to stand out in the crowd.
When a customer has a query regarding your bran then it reaches out to you and then what they expect is an instant reply. This gives them the sense of, that the brand can be trusted and is always going to be with you in hard times. However, providing instant replies to all the questions can be outright impossible, especially for human agents. Chatbots, on the other hand, provide you with the ability to answer queries round the clock.
By adding frequently asked questions to the chatbot, your customers can search for the right answers anytime. This will build trust among your current and potential customers.
Chatbots are an automated solution that lets you handle your business handle easily with customer’s queries. Earlier what happened was, when the queries needed the real guidance then you have to book a meeting, so handling only a number of customers because a human agent can only attend two or three customers at the same time. This was the biggest limitation which businesses need not brag it until now when chatbots came into power.
However, if you use chatbots in addition to your human agents then your business can easily scale its operations without having to worry about the increasing number of customer chats and calls. Simply put, if you are looking forward to scaling your business then look into chatbots because chatbots helped scale India’s largest sports game by 30 times.
4. Right Products and Services to Customers
There will be times when your customers will need help figuring out the right product or service that fits their budget and requirements. This mostly happens when your customers are looking for something expensive to purchase or when they don’t have enough exposure to a particular product or domain.
Chatbots come in real handy when the customers aren’t sure what they are getting and whether it’s worth spending a big amount. Your Chatbot knows when your customers need help with their purchase decisions. The bots not only give an explanation regarding a particular product but also give recommendations based on prior queries from the user. Thinking practically, one could say that your chatbot loves your customers more than you.
5. Target Millennials
Millennials are not impulsive buyers. They like to inquire and compare products before they buy. They love chat channels more than any other form of customer contact services. So, if your product lines are aimed at Millennials, introducing chatbots in your customer service will be a prudent investment.
According to a survey, millennials prefer solving their issues on their own rather than reaching out to someone or a customer service agent. This exact point is why chatbots are needed and give this service to them can help your brand gain reviews.
6. Better User Interaction
Chatbots are capable of having conversations with customers like a human agent would. A funny instance that happened with me was, that I was reviewing some online coaching class, then a chat box popped up and from there Aksh helped me throughout the process. Here’s the fun part when I said it was nice talking to you and a sweet welcome came from there; then I had to ask if he was real. Oh No! He was a chatbot and I got fooled.
Coming back, they can keep your customers engaged in a specific platform by maintaining a conversation. When a web application is opened, the user is greeted by the chatbot in a non-intrusive manner. The user can then ask the chatbot specific questions regarding a product or a service. Most of the time, the user is provided with answers from the database, but in a structured manner that mimics human interaction. With each question, the bot also provides a list of options making it easy for the user to input the necessary query.
Most businesses come up with an app for their products, which customers have to download from play stores. Imagining the whole of this gives me like a lazy look, because whatever it is; it’s a cumbersome process. Looking from business point-of-view, apps are expensive and require a lot of time for development. And also, building an app and expecting them to download an extra app on their device for the query is going to get you only a handful of customers which is bad notice.
Here’s where you can implement chatbots on messengers, which is where most people spend their time. They are a better alternative to having an app. The chatbots are designed in a way that engages the customers who come in and makes them try the company’s products and services.
Things To Keep In Mind While Developing A Chatbot For Your Business
Keep personalization in mind. Your welcome screen should tell the user about the features of your bot. A user will feel more at home with your service if the bot is using his name for greeting and interaction.
Take care of the design of your chatbot because the user is going to trust a well-designed chatbot.
Use proper text-formatting, high-quality links, and high-resolution pictures in your bot replies.
Ask users for all the information you require, upfront, and take care of the conversation as well. Keep them connected to their mails.
Linking your chatbot with an NLP library can help in minimizing grammatical errors.
Readymade Chatbot Platforms You Can Use
Top 10 mot innovative chatbots in the world | Global Tech Council
In today’s competitive industry, customers are always looking for customized solutions. You need to satisfy your customer’s requirements and do it in a way that makes you stand out from the crowd. Chatbot scale up your operations. It does not suffer from the limitations of a human agent. Where live agents can handle only 2 to 3 conversations at a time, chatbots can operate without an upper limit. By employing chatbot solutions to complement your human task force, your business can get the boost it requires entering new markets.
The future will be heavily directed by Artificial Intelligence (AI) technologies which prominently include chatbots. There’s a positive response to many chatbot benefits such as an increase in process efficiencies, decreased workload, and improved customer service, but it’s important that a measured approach is taken to implement these technologies.
The digital transformation of the world has created an abundance of knowledge online to teach us, when we don’t have the opportunity to learn from a mentor or guide ourselves. With the help of online courses, we can apply some structured guidance to how we approach our interpersonal interactions, entrepreneurial pursuits, and even the way we think. These hyper-focused, organized lesson plans can help us hit refresh and streamline our paths to success.
Amidst the global outbreak of a lethal pandemic, many of us are surviving the lockdown at our home with some extra time on our agendas at our comfort level. To support those forced to join the office exodus, many online platforms, universities and distance learning organizations are offering free access to their courses.
Maybe this the perfect time, so that an entrepreneur can boost up their career at their own comfort level. There is a list of some of the top7 online courses for entrepreneurs, which can take your mind off things and help you gain a skill or two. This can boost your career at the same time for free, or a small fee. Because when there is a need to master a wide range of skills, then it’s wrong to waste a single second of your time.
Harvard Business School-Online Lessons
Harvard Business School- Online Lessons
Harvard Business School is consistently ranked as the top business school in the world. Since the beginning of the outbreak, when there was this prediction of staying home for a long time, they came up with free interactive business lessons that you can complete online. These lessons are now open for everyone who wants to advance their careers with core business concepts, from leaders to students.
The course offered was: Entrepreneurship essentials and leadership principles, but there is plenty for everyone. This is a great way to stay engaged and sharpen your business skills by learning from the best. Keep an eye out for the next deadline.
Blockchain For Business: The New Industrial Revolution
This course is over five and a half hours, which breaks down how blockchain technologyworks and today’s conception of the potential impact it has on society. If you’re confused about what blockchain is, then this course is made up of you. By the end of the course, you’ll have a firmer sense of what ‘decentralization’ is, and how blockchain is shaping the future of transaction or exchange.
Blockchain for Business: the New Industrial Revolution 4.0
With its clarity in terms, it provides various examples of how large corporations including UBS, Allianz, IBM, Kodak and a range of industries from finance to retail to logistics are using blockchain technology to improve security and efficiency, as well as what developments are on the horizon and challenges the tech will face in scaling.
The Complete Product Management Course
The Complete Product Management Course
Do you ever wonder how organizations like Apple, NASA, and Ticketmaster design their products? This course is designed to give entrepreneurs a deeper understanding of what it takes to bring a product from “idea to launch”, and how to develop strategies and concepts for products, coordinate their development, analyze and validate ideas and help them grow.
It features case studies from NASA, Google, Zappos, and many more. This course also teaches you the fundamentals of Agile Development and industry-standard tools like Pivotal Tracker, Axure, and Popplet.
Artificial Intelligence: Stanford University
Artificial Intelligence Stanford University
This online learning program is based on a course offered at Stanford University, taught by Sebastian Thrun, co-founder of Udacity, CEO of the Kitty Hawk Corporation and founder of Google’s self-driving car project, and Peter Norvig, director of research at Google. There is a prerequisite for the course, however: a background in probability theory and linear algebra.
For a crash course on AI and its applications, head to Udacity for Intro to Artificial Intelligence. In the 4- month course, students can start from the basics of machine learning as well as how statistics, logic, and planning come into play with AI. Then from there, they are going to learn how AI comes into play, per se, move onto the applications lesson segment of the course. They further provide you with insights into image processing, natural language, and processing robotics.
Udacity is an online platform devoted to digital marketing and since Content Marketing is part of it, then Content Marketing Essentials is one of its components. The creator of the course is Hubspot, a marketing software company. They have developed the syllabus to help students plan content marketing strategy and measure its reach and effectiveness.
Basically, the program starts with the segment involving the planned content to best which further communicates a marketing message. Further, the course heads on with content creation through storytelling, curation, and other techniques, followed by distribution, promotion, and tracking.
This crash course also provides tips for designing the user experience (UX) of a website, where actually the content resides, to resonate with readers. In the end, it provides real-world examples of careers in content marketing and gives prospective hires and hiring managers alike a sense of what makes a successful job candidate in the field.
The Essential Guide to Entrepreneurship: Guy Kawasaki
The Essential Guide To Entrepreneurship: Guy Kawasaki
An Apple-alumni and Canva evangelist Guy Kawasaki is sharing his experience as an investor and entrepreneur in this class. It states the each and every step process of starting a business, covering launching, pitching, fundraising, building a team, using social media, and evangelizing in over 56 lectures and 4 hours of content. Described as highly informative and entertaining, Guy uses real-world examples to show you the dos and don’ts of being an entrepreneur. You will definitely be inspired to get your startup off the ground.
Now, this is a sort of important inter-personal skill that every entrepreneur should have. So, whether you are leading a meeting, negotiating with partners, or making a media appearance, it’s important that you master this skill. Body Language for Entrepreneurs teaches “nonverbal communication to increase your income and influence as a business owner” in scenarios like networking, public peaking and delivering an elevator pitch, in order to nail it. Because, it’s not what you say it, but how you say it.
The fact the course states that “93 percent of our communication is nonverbal,” and it’s visible in your online presence, during your interactions with customers, as you connect with colleagues and in just about every other professional scenario involving another human being.
This course reaches not only for entrepreneurs but students on the flip side can apply the same strategies to get a better read on prospective hires, potential clients, and others.
Other Online Courses For Entrepreneurs
Startup India L & D Programme by UpGrad
Inbound Marketing
Inbound Sales
Starting-up Post COVID
Startup School by Y Combinator
Understanding Design Thinking
Introduction to Managerial Accounting
Managerial Economics
Supply Chain Analytics
Human Computer Interaction
Introduction to Data Analytics
Financial Statement Analysis and Report
Security Analysis & Portfolio Management
Enhancing Soft Skills & Personality
Consumer Behaviour
Economics of PR
Economics/ Management/ Entrepreneurship
Web Development with HTML and CSS
Android Programming
Amazon Web Services
Entrepreneurial Idea
Knowingly or unknowingly, we are all learning new things each day. As an entrepreneur, it is important to keep learning and to quit thinking that you stop learning at a certain age. Picking up new skills is a lifelong, ongoing process, and it becomes even more necessary when you are the helm of things with a whole workforce looking up to you for motivation. However, in this age of fast-changing technologies, it wouldn’t suffice to be a jack of all trades.
You will need to have a basic understanding of a wide range of topics. This doesn’t, however, mean that you need to pay humongous tuition fees and attend classes. You can learn new things and pick up newer skills from the comfort of your home through online courses.
Whether you choose to read books, take free online courses, or join a local entrepreneur’s group, the most important thing is that you never stop learning.
Machine learning has gifted humanity the power to run tasks in an automated manner. It allows us to improve things that we already do by studying a continuous stream of data related to that same task. In this article we have short-listed some Machine Learning Books.
‘Machine learning is a core, transformative way by which we’re rethinking everything we’re doing. We’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We’re in the early days, but you’ll see us in a systematic way think about how we can apply machine learning to all these areas.’ – Sundar Pichai, CEO, Google
Machine learning has a wide range of applications that belongs to different fields,i.e. From space research to digital marketing. Machine learning is a complex field but that doesn’t mean that it can’t be learned.
Here Are Some Must-Read Machine Learning Books.
1. The Hundred-page Machine Learning Book
This book by Andriy Burkov explains the various topics of machine learning topics in a mere 100 pages. Written in an easy-to-follow manner, the machine learning book is endorsed by reputed thought leaders to the likes of the Director of Research at Google, Peter Norvig, and Sujeet Varakhedi, Head of Engineering at eBay. Post a thorough reading of the book, the reader will be able to build and appreciate complex Artificial intelligence systems, clear and ML-based interviews, and even start their own ml-based business.
2. Programming Collective Intelligence
This book is counted among the best books to begin understanding machine learning, the Programming Collective Intelligence by Toby Segaran was written in the year 2007. The book makes use of Python as the mode of delivering the knowledge to its readers.
3. Machine Learning For Hackers
The Machine Learning for Hackers by Drew Conway and John Myles White, is meant for the experienced programmer interested in crunching data. The word hacker here refers to adroit mathematicians. Rather than delving deeper into the mathematical theory of machine learning, the book explains numerous real-life examples to make learning ml simpler and faster.
Some Must-Read Machine Learning Books
4. Machine Learning
Written by Tom M. Mitchell, this book is a fitting book for getting started with machine learning. It offers an overarching view of machine learning theorems with summaries of the respective algorithms. The Machine Learning book is full of examples and case studies to help the reader in learning and grasping ml algorithms easily.
5. The Elements Of Statistical Learning
This book focuses on mathematical derivations to define the underlying logic of an ml algorithm. Written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning is a must-have book. It is recommended to have a basic understanding of linear algebra before choosing this book.
Instead of imparting knowledge about the various advanced concepts pertaining to machine learning, this book by Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin prepares its readers to understand the complex machine learning concepts thoroughly.
7. Pattern Recognition And Machine Learning
This book was written by Christopher M. Bishop, and serves as a great reference for understanding and applying statistical techniques in machine learning and pattern recognition. A basic understanding of linear algebra and multivariate calculus are prerequisites for going through the machine learning book.
8. Natural Language Processing With Python
Written by Steven Bird, Ewan Klein, and Edward Loper, Natural language processing is the backbone of machine learning systems. It uses the Python programming language to guide the reader into using NLTK, the popular suite of Python libraries and programs for symbolic and statistical natural language processing for English and NLP in general.
Bayesian Reasoning and Machine Learning is a must-read for anyone interested in entering the field of machine learning. Written by David Barber, there is no scarcity of well-explained examples and exercises in the Bayesian Reasoning and Machine Learning book.
Books are the 2nd Most popular medium of learning
10. Understanding Machine Learning
Written by Shai Shalev-Shwartz and Shai Ben-David, The Understanding Machine Learning book offers a structured introduction to machine learning. The book focuses on the fundamental theories and algorithmic of machine learning, and mathematical derivations.
11. Hands-On Machine Learning With Scikit-Learn & TensorFlow
By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow- the author Aurelien Geron helps the reader gain an inherent understanding of the concepts and tools to build an intelligent system. The reader will learn a range of techniques, i.e. from simple linear regression and progressing to deep neural networks.
12. Building Machine Learning Powered Applications
The author Emmanuel Ameisen demonstrates the practical ML concepts with the help of various elements,i.e, code snippets, illustrations, screenshots, and interviews with industry leaders. The reader will learn the necessary skills required to design, build, and deploy applications powered by machine learning (ML) with the help of this book.
13. Grokking Deep Learning
Grokking Deep Learning teaches one to build deep learning neural networks from the basic level. In his engaging was, author Andrew Trask shows one the science under the hood, so to grok for oneself every detail of training neural networks.
14. Deep Learning With Python
Deep Learning with Python introduces the field of deep learning with the help of Python language. Written by Keras creator and Google AI researcher François Chollet, this book builds an understanding through explanations and practical examples.
15. Deep Learning
This book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is an introduction to a vast of topics of deep learning, covering mathematical and conceptual background, the deep learning techniques used in industry, and also research perspectives.
Conclusion
Machine learning is a trending career option these days. The future looks all bright and shiny for it. So, it is high time to jump into the scene and make a profitable, professional career out of it.
This article sums up the 15 best machine learning books that you can go through to advance in machine learning the way you want it. I hope you find it informative and helpful.
Avantika Bhardwaj
