SAS Business Intelligence Tool is an augmented analytics and business intelligence platform which stands as a stalwart, providing organizations with a robust array of features to unlock the power of their data. It is recognized for its sophistication and analytical capabilities with the drag and drop capabilities which facilitates a visual process for finding insights. Its Artificial Intelligence and Machine Learning based augmented analytics helps business users to gain insights and predictions from data. Thus, SAS BI Tool is proved to be a go-to solution for businesses seeking to harness the full potential of their information.

In this article, I’ll explain the key features that make SAS Business Intelligence Tool a standout in the world of Business Intelligence Tools.

1. Advanced Analytics Capabilities
SAS BI Tool distinguishes itself with advanced analytics capabilities. Users can leverage statistical analysis, predictive modeling, and machine learning algorithms to extract meaningful insights from complex datasets, facilitating informed decision-making.

2. Comprehensive Reporting and Dashboards
The platform excels in offering comprehensive reporting and dashboard capabilities. Users can create detailed reports and interactive dashboards that provide a visual representation of key performance indicators (KPIs) and trends, aiding in strategic analysis.

3. Data Integration and Transformation
SAS BI Tool ensures seamless data integration and transformation. Users can connect to diverse data sources, cleanse and transform data, and integrate information from disparate platforms, enabling a holistic view of organizational data.

4. Self-Service BI
Empowering users with self-service business intelligence, SAS BI Tool allows individuals to create their own reports and visualizations. This democratization of data ensures that insights are accessible to a broader audience, fostering a culture of data-driven decision-making.

5. Visual Data Exploration
SAS Business Intelligence Tool emphasizes visual data exploration, providing users with interactive tools to analyze and interpret data intuitively. From interactive charts to heat maps, users can choose from a variety of visualizations to convey insights effectively.

6. Real-Time Analytics
For organizations requiring real-time insights, SAS BI Tool supports real-time analytics. Users can monitor and analyze data as it streams in, facilitating prompt responses to evolving business scenarios.

7. Role-Based Access Controls
Security is paramount, and SAS BI Tool addresses this through robust role-based access controls. Organizations can define and manage user roles, ensuring that sensitive information is accessible only to authorized personnel, enhancing overall data security.

8. Predictive Modeling and Forecasting
SAS BI Tool incorporates predictive modeling and forecasting features. Organizations can anticipate future trends and outcomes based on historical data, enabling proactive decision-making and strategic planning.

9. Mobile Accessibility
Recognizing the importance of mobility, SAS BI Tool is optimized for mobile devices. Users can access reports, dashboards, and analytics on smartphones and tablets, ensuring that critical insights are available anytime, anywhere.

10. Scalability for Enterprise Solutions
Designed for scalability, SAS Business Intelligence Tool caters to the analytical needs of both small businesses and large enterprises. Its architecture allows for the processing of vast datasets while maintaining high performance, ensuring flexibility as organizational needs grow.

11. Collaboration Features
SAS Business Intelligence Tool fosters collaboration among team members through features that enable users to share insights, reports, and dashboards. The real-time collaboration tools enhance teamwork, ensuring that decision-makers are aligned in their understanding of the data.

12. Integration with SAS Ecosystem
For organizations using other SAS solutions, SAS BI Tool seamlessly integrates with the broader SAS ecosystem. This integration provides a unified analytics environment, allowing users to leverage the combined power of SAS solutions.

In conclusion, the SAS Business Intelligence Tool emerges as a comprehensive and powerful solution for organizations seeking to derive valuable insights from their data. With its emphasis on advanced analytics, comprehensive reporting, and scalability, SAS BI Tool continues to be a strategic choice for businesses aiming to make informed decisions and drive success through data intelligence.

In today’s advanced digital age, customer relationship management (CRM) has evolved much from traditional standalone desktop software to a technology-driven advanced web based software which has adopted many technologies in its making. Artificial Intelligence (AI) is one of them that is playing a pivotal role in this transformation, revolutionizing the way businesses manage and enhance their interactions with customers.

AI enabled CRM systems can help recognizing patterns, recommend best possible actions, predict outcomes and automate future sales processes. Now AI enabled CRM can understand and analyze data of customers more deeply. They can analyze market & sales data and can provide more insights to grow the future sales and help companies in making better decisions.

Some of the leading vendors that offer AI enabled CRM include Creatio, Pipedrive, HubSpot Sales Hub, Zendesk Sell, Freshsales, Quickbase, ClickUp, Salesforce Sales Cloud, Monday Sales CRM, and ActiveCampaign for Sales. These vendors offer low-code or no-code development option, predictive lead scoring, next action recommendations, call data entry & collection automation.

Let’s explore the significant impact of AI in CRM.

1. Data Analysis and Personalization

AI enables businesses to process vast amounts of data efficiently. This data, including customer demographics, purchase history, and online behavior, is analyzed to create detailed customer profiles. With this information, businesses can personalize their interactions, offering tailored product recommendations and content.

2. Predictive Analytics

AI empowers predictive analytics by forecasting customer behavior. By analyzing historical data, AI algorithms can identify trends and anticipate future actions. For instance, AI can predict which customers are most likely to churn, allowing businesses to take proactive steps to retain them.

3. Chatbots and Virtual Assistants

AI-driven chatbots and virtual assistants are available 24/7 to respond to customer inquiries. They provide quick and accurate responses, enhancing the customer experience. Chatbots can handle initial inquiries and respond based on preselected questions and answers, and can later add human responses based on the complexities of inquiries.

4. Enhanced Customer Support Through AI Enabled Customer Relationship Management

AI-powered CRM systems can route customer inquiries to the most suitable agent based on the nature of the query. This ensures that customers are connected with the right expert, improving query resolution times.

5. Improved Lead Scoring

AI-driven CRM can assign lead scores based on the likelihood of conversion. This ensures that sales teams prioritize leads with the highest potential, increasing efficiency.

6. Sentiment Analysis

AI can analyze customer feedback, reviews, and social media interactions to determine sentiment. This provides valuable insights into how customers perceive a brand or product, allowing businesses to make necessary adjustments.

7. Automation of Routine Tasks

AI can automate those tasks which are repetitive in nature, such as data entry and follow-up emails. This frees up employees to focus on more strategic and creative aspects of their roles.

8. Customer Retention

AI helps identify at-risk customers by detecting changes in their behavior. Businesses can then implement retention strategies to prevent churn.

9. Sales Forecasting

AI can provide accurate sales forecasts based on historical data and current trends. It is quite possible that this forecast may or may not work in future or businesses could not see the same amount of sale in future but this can help businesses setting realistic goals and allocate resources effectively.

10. Customer Journey Mapping

AI assists in mapping the customer journey by identifying touchpoints and interactions. This helps businesses understand the customer experience and optimize it.

11. Regulatory Compliance

AI-driven CRM systems can help ensure compliance with data protection and privacy regulations by monitoring and managing customer data in a structured manner.

12. Competitive Advantage

Businesses that embrace AI in their CRM strategies gain a competitive advantage. They can respond to customer needs more effectively, creating loyal customer base.

In conclusion, AI has significantly impacted customer relationship management. It enables businesses to deliver more personalized and efficient services, enhances customer support, automates routine tasks, and provides valuable insights through data analysis. Embracing AI in CRM is no longer an option but a necessity for businesses looking to stay competitive and meet the evolving expectations of their customers.

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NLP is one of the sought-after techniques to develop better and more effective communication skills. There are various techniques – such as anchoring and mirroring for establishing better communication with the prospects in your absence.

Natural Language is the medium of how humans communicate with each other through text, speech, images, signs, email, and SMS. But when humans try to communicate with machines, they do it in the same way that humans do, and algorithms process it in the backend. Find the translations’ accuracy to reconstruct the sentences in a better way. Find the accurate results and again convert them into human-understandable language.

What Is NLP and How Is It Making Communication Better?

Communication is the key to everything. It should be transparent from both the side to make it better and efficient. It builds trust, transfers knowledge and emotions, and makes things sell in the business. Thus, communication plays a crucial role everywhere and in our day to day life.

Businesses are already spending massive capital on making communication better and effective. Whereas neuro-linguistic programming, in short NLP, makes communication better. By adding lots of filters to give accurate information in the least time. NLP is the advanced level AI and ML programming and training from the existing algorithms on how to deal with human languages and convey them accurately.

Whether it may be voice search or text search, NLP takes the input, processes it through the algorithms, and answers the search queries that the search engine predicts the best and most relevant answers for the users. Most often, computers and smart devices fail to understand human languages. And there is NLP to solve this issue seamlessly with accurate results.

But the process is never simple the way it looks from outside. It has to go through a series of operations to get accurate results or effective communication. Let’s learn about them in detail.

1. Enhancing the Translation Accuracy

For Neural Machine Translation (NMT) models, supervised learning requires a large volume of sentences. However, the data is not available in general, but it requires a lot of research and uses monolingual data where there is no translation available. Most experts use back-translation, i.e., a semi-supervised learning technique for overcoming the above issue.

Translation accuracy involves three raw models: forward, backward, and fluency. The forward models look for how well the translations capture the original meaning. Whereas the backward scores look into how well the original sentences could get reconstructed. And fluency is all about fluency in a candidate’s translation and training in a self-supervised way by looking into the monolingual data. NLP takes the three scores, finds a balance, and produces improved translations.

2. Strengthening the Self-Supervised Models With Pre-training Methods

The pre-training is more about the self-training process, and it strengthens the whole algorithm process and model performance by incorporating the prediction and unlabeled data. It helps to find out the additional information that can get used in the training process.

Adopting NLP has many advantages. It not only makes the device smart, but the process itself gets smarter when your results are accurate to what users are looking for to know or have. And the users get amazed and feel wow to use it more often on their smart devices. And another advanced training is self-supervised learning. The prime purpose of this algorithm is not only to learn high-level features. But to become better and capable of handling a wide variety of tasks and datasets.

3. NLP Mirroring Technique is One of the Ultimate Solution

NLP mirroring technique is an alternative to NLP matching, and it tries to copy the word or sentences to build relationships. Imagine you standing facing the mirror, you raise one of your hands, and in the reflection, you can see the same happening there but with the opposite hand at the other end. This way, one is trying to build a relationship with another, and it is what we call mirroring.

NLP follows the same concept when you say some words, and its algorithms try to supervise them and reconstruct them depending upon the voice tone and sentence length, and structure considering the pitch, tempo, pace tone, and volume. To show the actual results to the users who are doing the type of search or voice search queries.

4. Different People Will React to The Same Differently

Every person has their way of saying, sometimes the influence is because of the mother’s tone that NLP does not understand and self-supervised it. To the same questions, they have multiple answers. They will not feel satisfied and react to the same differently. Using NLP, you can book a table for the party, or you can ask for photos to google about any location if you are planning for a vacation. Or you can ask your smart assistant to play your favorite songs or movies, and it will bring you to it.

Suppose if you want to auto tune or omit additional noise, then advanced lexical processing focuses on removing the miscellaneous noise in textual data so that you can build algorithms that will save you from embarrassment with auto-correction facilities.

There are two processes: stemming and lemmatization. And both are part of an advanced technique known as canonicalization. So, others can hear your right voice and pitch like the approaches the professional speak takes to maintain their flow.

Final Thoughts

There you have it all you need to learn about advanced Natural Processing Techniques for making communication better and more effective. When you finish this blog thoroughly, you will get to know what NLP is? How technologies make our communication better and easier when it is about communicating with smart devices.

When you go further deeper, you will find how to enhance the translation accuracy. Boosting up algorithms models and increasing their efficiencies. And how mirroring techniques reduce complexity and simplify the working process with accurate results. When you approach the end part, you will know why people react to the same things differently and how stemming & lemmatization can save you from further embracement.

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Data is being created all the time, from devices, applications, and users, because it’s digital and measurable, but what about offline? The data is waiting to be discovered by the action you perform offline and its information on the physical world. Devices that can capture, convert, and save the newly discovered data can help many businesses. It is an excellent way for any business or industry to measure its operation with this real-time data to make decisions further and target the right customers.

With the help of data collected through devices, they can forecast and anticipate the event proactively. They can solve the issue before they become more prominent and affect the whole business. No matter which field you are into, one can measure the real-time performance of their products in all the places that matter.

Wondering how IoT can help us collect that massive data? Here we are with a straightforward explanation on the same.

While connecting to the Internet, what all devices do you expect to be connected to? Mobile phones, tablets, computers, etc., right?

What if I say that any device could connect to the Internet and exchange data with each other (in the form of collecting and sharing), and this is the concept of the Internet of things.

With the price of processors and wireless equipment becoming cheaper and smartphone usage becoming commonplace, it has become possible to turn any device into an Internet of Things device. There is an opportunity for everything to be connected to the Internet, hence interconnected to any other device.

IoT can help improve human beings’ efficiency by eliminating the less critical time gaps in daily human schedules and making them more productive. Let us see an example for better understanding- Sandra has her important meeting at 09:00 A.M. Her alarm rings at 07:00 A.M, which further passes on the bathroom geyser’s information to automatically switch on for bathing.

The sensors and connectivity will pass on the info. The coffee machine starts performing its work by the time Sandra reaches the breakfast table, and after that, the car will begin to show the route with less traffic so Sandra can arrive on time. If by any chance due to traffic she got late from the time, an automatic message will be sent to the office intimating about the delay. So here, all the devices are smart and connected to the Internet, passing on information and improving time management. All the appliances are interconnected, making things seamless and smooth.

AI (Artificial Intelligence) and IoT (Internet of things) Relativity

Understanding the need and potential of IoT and AI, companies are investing in these sectors. The combination of these two super-powerful technologies has redefined the way businesses, economies, and industries function. Above we have discussed how IoT performs, but the question arises: how are they getting to know all this data, and how can they be more efficient?

The answer is while IoT deals with machine communication using the Internet, AI makes sure the appliances gather knowledge from their figures and experience. And this helps data scientists to predict the upcoming demand and business insights using time series analysis which eventually helps in better decision making and performing intelligent tasks.

IoT+ AI = AIoT (Artificial Intelligence of Thing)

When artificial intelligence is added to the Internet of things, it means that AI empowers IoT to create intelligent machines that cause imaginative understanding, correlating, decision making, support, and act smartly with significantly less or no humanoid involved.

Here are some examples in which the corporate world changes over with the introduction of IoT and AI.

1. Automatic Homes/ Smart Homes

In the concept of smart homes or automated homes, these are gadgets included- CCTV camera, AC, fridge, oven, water purifier, security systems armed with sensors in the home perform like intelligent devices and connected with IoT applications. Here, Artificial Intelligence acts as a powerhouse in collecting data, analysis, and decision-making systems to act spontaneously.

2. Automated Security

The security system should be solid and reliable; implementing an automatic locking system using IoT can escalate this risk. AI can collect the daily pattern of accessing doors or systems of the employees and understand the regular pattern of different individuals. AI can detect or alert us of any suspicious activity.

3. Smart Alarm

Like the fire alarm set in the buildings, AI can make the machines intelligent. So they can not only buzz the alarm at the time of the fire, but they can also call the relevant number, send a system alert, and start to sprinkle water.

4. Smart Parking

The sensor at the parking area can alert the potential parkers for the availability of parking, which AI empowers by using the map destination data and can show the available parking space by offering the parking area route. It is another essential time-saving aspect, which resolves through the alliance of AI and IoT.

Conclusion

The final word of this informative blog is, Both IoT and AI are superheroes and can make things, businesses, and lifestyles brighter, and their alliance can make a big difference. The combination of two can help in achieving more extraordinary digital transformation. IoT and AI are the two most deadly industries that require relevant skills and expertise. Businesses are aggressively investing the money to realize their potential.

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The future is now Machine Learning and Artificial Intelligence. Artificial Intelligence impacts the way we transact and interact more than ever before. From finance and shopping to social media and medicine, machine learning solutions power more aspects of daily life than you could imagine.

In the age of Artificial Intelligence (AI), machine learning (ML) is a popular topic that covers Natural Language Processing and computer vision. Through machine learning, inventors have achieved incredible breakthroughs in diverse fields.

From the facial recognition feature on your phone to hyper-realistic casino gaming on platforms like Mr Bett, what previously seemed science fiction is today a reality, thanks to the advancement in the machine learning technology.

With the help of machine learning, humans can live healthier, happier, and more productive lives. Experts predict that soon, computers will replace both manual and mental labor. In some industries, this is already happening.

Here are some ways algorithms have impacted everyday life-

What is Machine Learning or ML?

ML is a subset of AI in which a computer is programmed to have the ability to self-teach while continually improving its performance of a task. Basically, it involves data analysis and interpretation.

The information extracted is used to make predictions, test whether a prediction is correct, and help the machine learn how to make better decisions. If you wanted to design AI software, you should consider taking a machine learning course. Start with the basics and build your knowledge to become an expert if this is a career you consider pursuing.

Computer Vision and its Application Today

Machine learning has experienced exponential growth over the recent past, especially in the area of computer vision. As of 2016, the human error rate was only 3% in computer vision, which means computers can recognize and analyze pictorial data better than humans. This is incredible, because decades ago before we discovered how machine learning could help change lives, computers were just piles of machinery that could even fill a room. Now, people are walking with powerful ML software in their pockets.

One real-life application of machine learning is in Diabetic Retinopathy, a complication that affects the eye. During the extensive exam required to pinpoint the problem, a machine learning model built around computer vision completes the diagnosis.

The data collected from the diagnosis is then processed and interpreted. Experts can also use the model as a second opinion. The idea is that artificial intelligence models should replicate specialists’ work. So, the technology can be deployed in third-world countries in remote locations with a shortage of specialists.

1. Machine Learning in Ride Sharing Apps

To further demonstrate how ML works, it’s important to discuss more real-life examples. One such example is ride-sharing apps such as Uber and Lyft. You probably already use these apps, but do you know how they calculate the price of your ride in real-time? And how do they match you with cabs to minimize detours? The short answer to all these questions is machine learning.

In an interview, Uber Engineering Lead, Jeff Schneider, explained the company uses AI to predict demand. Also, the head of Machine Learning at Uber, Danny Lange, confirmed the company uses artificial intelligence to estimate meal delivery times, compute pickup points, and detect fraud.

2. Machine Learning for Finance – Banking

Imagine how many people use banking services every day. Or the number of people who have bank accounts. And how many credit cards are in circulation? The simple answer is many. So, how many hours would workers need to sift through all transactions completed in a day? It would be impossible to detect anomalies or even review all transactions if the bank did everything manually.

Using machine learning, banks track credit card transactions and identify fraudulent behavior in real-time. Banking servers work with ML systems with anomaly detection models that monitor transactions and verify the authenticity on the fly. Before a credit card purchase is processed, the system can verify if it’s the owner using the card.

3. Artificial Intelligence in Education

Teachers must perform many tasks: educate, analyze student behavior, mentor, counsel, and a lot more. No computer can do all those tasks, but some of these tasks could be automated through artificial intelligence and ML. There are computer programs that review individual study plans for each student.

The software can analyze data on a student’s attendance, learning disabilities, and academic history using algorithms. With this data, teachers can identify learning gaps for each student.

4. AI in the Gaming Industry – how machine learning is used in video games?

Gaming is one of the areas in which machine learning has shown great promise. In 1997, Deep Blue, a chess computer game by IBM, defeated Gary Kasparov. Just recently, in 2016, AlphaGo by Google beat the Go world champion, Lee Sedol. Considering Go is a more challenging game for computers to learn than chess, it was an incredible milestone in the development of machine learning. The algorithm analyzes the moves of players while also learning how to play and make better moves.

For improved game play that is almost realistic, casino platforms use artificial intelligence systems. This includes generating a list of recommendations based on the type of games a player enjoys while in the casino. Also, you can use AI to predict lottery numbers.

5. AI for Dangerous Jobs

Dangerous jobs like bomb disposal could also be handed over to technologies that run on artificial intelligence. Already, security teams are experimenting with drones, which require human control to dispose of dangerous material. In the future, through machine learning, it will be possible for robots to take charge of every process.

Tasks like welding are also outsourced to robots, so humans don’t have to worry about the intense heat, toxic fumes, and noise produced during welding. Advancements in machine learning have improved accuracy and flexibility, so the robots can work unsupervised.

6. Environmental Impact Protection

Computers can store millions of records. Using stored data and real-time trends, machine learning systems can identify weather trends and recommend solutions to previously untenable problems.

A good example is the Green Horizon Project by IBM, which uses ML to analyze environmental data from sensors to provide accurate weather pollution forecasts. The information helps city planners to simulate situations and design models to mitigate environmental impact. Innovations like self-adjusting thermostats are also entering the market.

7. Smart Homes and Home Security Systems

Machine learning has also been used to create improved home security systems. Technologies such as facial recognition build a catalog of frequent visitors to your home, so any uninvited guests are flagged, and a notification is sent to your mobile device instantly. People can also benefit from AI-powered smart homes that come with features like tracking, which show when you last walked your pet.

8. Personalized Digital Recommendations

Have you ever logged into your Netflix or Spotify account and found recommendations for the kind of content you love to consume? This was machine learning at work. ML algorithms collect data about the type of content you watch or listen to often. From this data, the algorithm creates a recommendation list that matches your preferences. So, you don’t need to search on different pages to find the content you love.

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Initially, cloud computing was just a tool to facilitate smooth computing experiences. With time, it has evolved to be a scalable service essential for a modern firm. It has turned out to be a buzz offering a lot of practical value. Cloud Computing has changed the way the business environment operates. The enterprises are striving hard to provide cheap, advanced and secure services to grow themselves in this competing world. Today, the cloud has come a long way and has been widely acknowledged by analysts and enterprises as the driving force to alter the IT landscape.

In the era of cloud computing buying servers, updating applications or operating systems or disposing of hardware becomes outdated. The world of internet is sure to suffer without a cloud. According to a report, industries run 79% of their workloads in the cloud. Companies leveraging the emerging trends in cloud computing are expected to grow at a faster rate. Here are a few emerging cloud technology trends to watch in-

1. Hybrid Cloud Solution

Hybrid cloud is likely to become the dominant business model in future since it enables a modernized infrastructure with two or more delivery models. Hybrid cloud environment connects a mix of public cloud, private cloud and on-premises infrastructure evolving to suit the unique needs of an organization.

Cloud bursting is the primary reason for its rising demand.  Hybrid cloud allows the enterprises to use unlimited resources to process the fluctuating workloads. To balance the organizations demand with supply, it offers close monitoring of the used resources.

The hybrid cloud solution is more popular among medical organizations to keep full control over sensitive data. It enables organizations to protect their most confidential data on their terms. In other words, Organizations themselves decide the data storage and conditions to secure the data.

2. Artificial Intelligence Platform in Cloud

Artificial Intelligence is expected to aid the world mainly in providing intelligent business functionalities in future. The amalgamation of both artificial intelligence and cloud computing is “The Intelligent Cloud”. It is capable of processing big data with greater efficiency and speed.

The two critical pillars of AI are data and compute. Artificial intelligence has automated businesses with robotic process automation. It automates human tasks in organizations and used for hiring employees for a job. It is increasingly adopted in industries to reduce human workloads.

Hardware optimization is the primary reason behind its rapid adoption. AI-optimized silicon chips can be inserted in any device to carry AI-oriented tasks.

3. Server-less Computing in Cloud

Server-less architecture is event-driven. In other words, for each request, a state is generated, and once the architecture serves the application, a state is destroyed.  Server less computing referred to as Functions as a Service (FaaS). Since the Server less functions are accessed as private APIs, you need to set an API Gateway.

The industries are leveraging Server less computing to support a wide variety of dynamically changing needs including IoT, mobile applications and web-based applications.

This event-based programming spins up to complete user request and spins down once the task accomplished. It runs typically for a few seconds or milliseconds at a time thus, reducing the cost.

4. Edge Computing in Cloud

Edge computing is shifting the computing functions close to the source – edge of the network. It dramatically reduces the complexity of interconnected systems making it much easier to gather and analyze data in real time.  It has a distributed and open-ended architecture that decentralizes the processing load.

Edge computing enables industrial equipment to make autonomous decisions without human intervention. It is the building block for smart factories equipped with temperature, motion and climate sensors capable of controlling light, cooling and other environmental factors. This will result in efficient use of power.

5. Backup and Disaster Recovery in Cloud

The increasing usage of data is driving the adoption of backup and disaster recovery software among organizations. The thought that hardware failure can result in companies compromising its confidential data leads to the rise of disaster recovery software in the market. Data backup can’t be considered as a “set and forget” solution. It requires regular maintenance and testing to ensure an appropriate facility and easily accessible to the company.

The customers enjoy a better business continuity plan since a cloud disaster recovery plan is automated and recovers the files in seconds.  The pay-per-consume feature makes it more popular among enterprises with less budget.

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6. Cloud Security

Cloud computing security is a set of policies, controls, technologies and procedures working together to protect cloud-based systems, data and infrastructure. The delivery of cloud security depends on individual cloud provider or cloud security options in place.

Cloud security is mainly responsible for securing the data and fighting against hackers. Cloud backup ensures your data is protected from external threats without a need to have an antivirus plan.

The security layer in cloud consists of network security, physical security, measuring endpoint security and communication encryption. The traffic moves to and from the cloud through public channels. The encryption like SSL is vital to maintaining the integrity of data. Carefully inspect the data movement and implement the possible checks.

Final Thoughts

Cloud computing growth continues to accelerate with the technological innovations and rising trends. It is capable of providing better security, storage and helps in better decision making. The trends in cloud computing are sure to automate the businesses thus reducing the machine complexity and labor.  The companies are leveraging these cloud-computing trends to stay ahead of the competition.

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Data science is one of the fastest growing fields of expertise at the moment. We know that data science involves machine learning, but what’s the difference between these two fields of expertise?

In short, machine learning involves complex algorithms that “learn” from data in order to predict future trends and system behaviors. On the other hand, data science is the process of tackling and making sense of large collections of data. This includes data cleansing, preparation and analysis, which is, in part, machine learning.

In this article, we will unpack these two concepts, aiming to bring an understanding of what each term means and how they relate to each other.

Data Science vs. Machine Learning

A. Data Science

Data science as a field is difficult to define since it draws from so many different fields of knowledge.

Most data scientists know machine learning and understand multiple analytical functions. This person usually has experience in SQL database coding and a strong knowledge of various coding languages, such as Python, SAS, R and Scala. Added to this, they are usually able to use unstructured data in order to extract useful information. Other fields that are sometimes included in data science are bioinformatics, information technology, simulation and quality control, computational finance, epidemiology, industrial engineering and number theory.

As you can see, data science covers a very wide spectrum of knowledge and skills. Depending on which side of this spectrum you are, you may or may not use programming and complicated mathematics, but you will definitely use large sets of data, usually in an unstructured format. Due to the broad nature of this field, it is hard to define and to find one person capable of doing everything involved needed for a successful data science project. Usually, data scientists would work as a team where each would focus on a specific subset of the field.

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Here, you would see titles such as “Machine Learning Engineer”, “Analyst” or “A/B Test Expert” indicating which area of work they focus on. Tools used by data scientists include, but are not limited to, data cleansing, preparation, predictive analytics, machine learning and sentiment analysis. These experts are tasked with making sense of large collections of data, extracting useful information from it and translating that into actionable goals. A data science team would understand how data relates to business and uses this to enable executives to make informed decisions based on solid science in order to propel their businesses forward.

Data science involves processing vast amounts of unstructured data in automated ways in order to extract logical, useful information from it and make prediction regarding future trends and system behaviors. Unstructured data comes from video, audio, social media, manual surveys, clinical trials and many other sources. This can be lumped together as human consumable data, which can be read and analyzed in tabular form, by humans. The amount of data is so vast, though, that this is entirely impractical, hence the need for automating and speeding up the process. Here, the data scientists will have to borrow techniques from related fields, as is done in most practical applications of science.

As time progresses, these predictions must be updated and the system re-calibrated using new data. Data scientists must also understand and decide which analytics tools to use for their specific purposes and applications, since this would affect the type of information that they would be able to extract from a specific set of data. Real world problems are tackled in data science. This field is incredibly complex due to the complex nature of the world we live in.

In data science, unsupervised clustering can be used. Here, an algorithm is used to find clusters or cluster structures without having been given a training set of data. These clusters must be labelled by a data scientist; thus, some human interaction is necessary.

Here, the major complexity of the system is due to the nature of the data (unstructured and vast). It is necessary to synchronize and schedule tasks in a logical manner in order to render the data useful and extract as much information from it as possible.

Simply put, data science is a vast field encompassing many disciplines, of which machine learning is one.

B. Machine Learning

Machine learning is a subset of data science. Arthur Samuel defines it as “a field of study that gives computers the ability to learn without being explicitly programmed”.

An expert in machine learning requires in-depth knowledge of computer fundamentals and must be excellent in data modelling and evaluation skills. Knowledge of probability and statistics is needed and in-depth programming skills and knowledge is essential.

In machine learning, large collections of data are mined in order to find patterns, learn from it and predict future behaviors of systems. It basically “teaches” a system how to behave under certain circumstances. A prime example of this is Facebook’s algorithm. Here, the algorithm observes various users on the social media platform in order to determine patterns of user behavior and interactions. This information is used in order to tailor the user’s news feed to articles that they are likely to enjoy. Amazon uses a similar principle to suggest products in their “you might also like” category. YouTube, Netflix and a myriad of other media platforms and online retailers work on the same principle to suggest your next view, article or purchasing suggestions.

In finance, machine learning is used to predict whether a prospective client applying for a loan is a good or bad prospect based in historical data. This takes the guess work and “gut feel” factors out of financial decision making.

Another example of sophisticated machine learning is the autocomplete or predictive text functionality on your smartphone or search engine. The software is programmed to collect data as you type in order to better predict what you are likely to type next in order to fill in the blanks faster and more accurately as time progresses. This has become so entrenched in our daily lives that few people stop to think about it.

Machine learning is a subset of artificial intelligence (AI). Here, a problem is defined in finite terms and the algorithm is programmed to know the “right” decision. Now, it trawls through the data at hand to learn which parameters are needed in order to get to that decision.

Basically, the computer is given the ability to learn new things and complete complex tasks without being explicitly programmed. When developing a machine learning algorithm, a training set of data would be used to “teach” the algorithm to perform a specific function. This would be fine tuned and can later be re-calibrated using a new set of data. On the long run, this would lead to a highly sophisticated algorithm that can accurately predict future trends and system behavior and can also make complex decisions in an unsupervised manner. This eliminates the need for regular human interference. Here, regression and naive Bayes or supervised clustering could be used.

Machine learning would not include unsupervised clustering, as is the case with the broader data science discipline. Data used in machine learning must be structured in a way that the specific algorithm would understand. Here, feature scaling, word embedding and adding polynomial features are some of the tools that can be used to render data useful and understandable for each specific application. In machine learning, the main complexity is in the algorithm itself. In some cases, an ensemble algorithm would be used, which is a combination of various machine learning algorithms. Here, the contribution from each algorithm would be weighted in order to obtain the desired results.

In short, machine learning is where practical statistics and highly sophisticated programming skills meet.

Overlap Between Machine Learning and Data Science

In machine learning, concepts that are used in data science career, such as regression and supervised clustering, are also used. In contrast to this, data science uses data that may or may not be originated in an actual machine or mechanical process. Both these fields use large collections of data in order to learn from it and arrive at logical actions in order to add financial benefit to an organization.

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Data science is a much broader term that machine learning. Machine learning focuses mainly on statistics and algorithms, while data science encompasses anything related to collecting, analyzing and processing data. Data science is multi-disciplinary. In a data science team context, each person would have a specific role to fulfill. Here, a machine learning expert would work to automate as many tasks as possible, breaking down code in order to simplify and reuse as many components as possible. Statisticians would ensure that the information teased out of data makes sense and is usable. Economic experts would optimize the system responses to ensure economic viability. Machine learning is crucial to data science and should be used in conjunction with other disciplines in order to complete the data science picture.

If you have a high level of knowledge on mathematics and statistics combined with hacking skills, you are able to program in the field of machine learning. Pair these skills with a large portion of substantive expertise, and you have a highly skilled data scientist.

In short, machine learning is one of many tools used by data scientists in order to extract useful information from large collections of data.

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Artificial Intelligence has huge potential economic benefits and equally significant societal impacts. Some estimate that the annual GDP (Gross Domestic Product) contribution from AI could be as high as $130bn a year, by 2030. Equivalently, some suggest that between 30% and 40% of all jobs could be, at least ‘Augmented’ by AI and, potentially, especially where those jobs are repetitive, replaced entirely by the technology.

There are a number of potentially important consideration as AI projects and solutions roll out. Bias in AI systems is just one. Bias is an ‘unfair’ output, produced as a result of the calculations performed by an AI system. There are many examples of ‘bias’ in AI, but one memorable exemplar has been provided by Joy Buolamwini, a researcher at MIT who turned racial bias in AI systems in to her PHD paper. For her example, defined bias, as ‘having practical differences in gender classification error rates between groups.’

Essentially what Buolamwini produced in her research, was clear evidence that AI systems performing Facial Recognition, got it wrong, systematically, for some ethnic groups.

Algorithmic bias comes from the most fundamental aspects of AI projects. AI systems are designed to meet the priorities (and therefore, sometimes the prejudices, even subconscious prejudices) of those who program them.

Imagine, for example, an AI system that was trained to assign jail time for people accused of crimes. The algorithm is ‘trained’ with the history of all crimes in a particular state and the corresponding sentence their perpetrators received.

In general, as a result of human bias, women receive half the jail time of men who committed the same crime. Put simply, there is systematic bias against men in the court system and the data this AI system is trained on reflects that balance. Any AI algorithm trained using that data would be equally bias against men and assign the same 50% jailtime to women whose circumstances it was asked to adjudicate.

The problem comes as we explore the ubiquity of AI solutions in our world. AI is already used, for example, by most of the fortune 500 companies, to filter applicants, before they are seen by a human. AI is being trailed at airports, in new schemes to avoid the needs for passports, using facial and ‘gait’ recognition technology to identify people without the need for paperwork.

As things stand, the economic benefits of AI could be off limits to some subsets of our society.

Chinese Leadership in Artificial Intelligence

China is winning the Cold War over the development of Artificial Intelligence (AI Leadership). Last year they produced more research papers than the US, invested more dollars in AI R&D than the US and trained more citizens on AI skills, than the US.

China’s more Authoritarian approach to government and economic growth, their a huge population, (which generates reams of data, from billions of smartphones, every hour) and a clear, top down strategy, in which they have a stated goal of being number 1 in the field, are just some of the reasons China has streaked ahead in AI leadership in technology terms, in recent years, and seems set to ultimately achieve their goal of dominating this world changing technology.

What could bias in Chinese AI look like?

It’s hard to tell exactly what systematic bias might look like, in Chinese AI Systems. Indeed, if you asked Chinese software engineers, psychologists and mathematicians who design those systems, they might well say that the system was not bias – because they are not aware of their own prejudices.

China, is, however, different in two notable ways to what we might describe as The Western World. It has a more Authoritarian government with a clearer direction and a preparedness to put the wishes of its citizens second to its own requirements.

Secondly, although not entirely differently, it, along with other Eastern cultures with histories of religion including Confusions and Buddhism, favor the interests of collectives and of groups, over the wishes of the individual.

A Chinese AI leadership designed to write books, for example, would, in all probability, not write a film of the sort which might have starred John Wayne as a bold, independent lone figure doing what he thought was right in the Wild West.

How could systematic bias affect our lives?

Artificial Intelligence is being rolled out in every industry and every component of business. AI systems now review job applicant’s social feeds before they come in for interview, some actually perform first line interviews with job hunters, examining applicant’s facial ‘micro movements’ and trying to associate the best performers in the organization to see if there’s a match. AI guesses where crimes are going to take place and the most relevant TV shows, music and podcasts to show you on your internet services. Bias, if it is shown, will affect lives materially.

At the very least, the situation calls for governance and standards of proof, that systems are not bias. They should instigate this, even as things stand, with such clear discriminatory bias already being evident in the examination of AI systems, so far.

If there is light at the end of the tunnel on the subject, it is that, this time, unusually, it is white males who are being discriminated against. It’s possible that this time, outrage at the effects of discrimination, since it is against a group which does not usually receive it, will receive a fairer and more immediate hearing. But of course, it is my own bias that makes me think that.

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Samsung Mobile India launched four smartphones in its Galaxy A and Galaxy J series which all features Samsung’s flagship ‘Infinity Display’ offering an almost bezel-less-like feel with 18:5:9 aspect ratio.

The all four new smartphones also run on Android Oreo operating system. While the all-new Samsung Galaxy A6+ and A6 feature a metal unibody design, the Galaxy J8 and J6 come with a polycarbonate body.

Samsung Galaxy A6+ and A6 specifications

Samsung Galaxy A6 and A6+ feature a similar spec-sheet with differences in processor, display size, battery and rear camera. The Galaxy A6+ is powered by a Snapdragon 450 processor while the A6 is powered by an Exynos 7870 SoC.

The smaller Galaxy A6 comes with a 5.6-inch HD+ Super AMOLED Infinity Display with 18:5:9 aspect ratio, the Galaxy A6+ has a 6-inch display with the same resolution.

The Galaxy A6 comes with a 16MP rear camera with f/1.7 aperture while the bigger A6+ has a dual-lens setup with a 16MP primary sensor with f/1.7 aperture and a 5MP secondary sensor with f/1.9 aperture.

There is a 16MP front camera on the A6 while the Galaxy A6+ has a 24MP selfie camera.

The smaller Galaxy A6 is powered by a 3,000mAh battery while the Galaxy A6+ has a 3,500mAh battery.

Both the smartphones have 4GB of RAM. The Galaxy A6 comes in 32GB and 64GB internal storage options. There is support for microSD cards of up to 256GB.

The main feature on both the phones in their Artificial Intelligence (AI)-powered camera module.

Samsung Galaxy J8 and J6 specifications

The Galaxy J8 is powered by a Snapdragon 450 processor while the smaller Galaxy J6 features an Exynos 7870 processor. While the smaller J6 comes with a 5.6-inch HD+ Super AMOLED Infinity Display with 18:5:9 aspect ratio, the Galaxy J8 has a 6-inch display with the same resolution.

The Galaxy J6 comes with a 13MP rear camera with f/1.9 aperture while the bigger J8 has a dual-lens setup with a 16MP primary sensor with f/1.7 aperture and a 5MP secondary sensor with f/1.9 aperture.

There is an 8MP front camera on the J6 while the Galaxy J8 has a 16MP selfie camera.

The smaller Galaxy J6 is be powered by a 3,000mAh battery while the Galaxy J8 has a 3,500mAh battery.

The Galaxy J8 has 4GB of RAM and 64GB of internal storage that can be expanded to up to 256GB. On the other hand, the Galaxy J6 comes in two variants, Galaxy J6 with 3GB of RAM and 32GB of internal storage and 4G RAM and 64GB storage version.

There is support for microSD card of up to 256GB on both.

Samsung Galaxy A6+, A6, J8, J6 prices and availability

1. Samsung Galaxy A6+ – priced at Rs 25,990
2. Smasung Galaxy A6 – comes in two storage variants (both with 4GB RAM) – 32GB storage at Rs 21,990 and 64GB storage costs Rs 22,990
3. Galaxy J8 – costs Rs 18,990
4. Samsung Galaxy J6 comes in two RAM and storage variant, Galaxy J6 with 3GB of RAM and 32GB of internal storage costs Rs 13,990 while the 4G RAM and 64GB storage version is priced at Rs 16,490

The Samsung Galaxy J6, A6 and A6+ will be available across retail stores and Samsung eshop website from May 22. The Galaxy J6 will also be available on Flipkart while the Galaxy A6 and A6+ will also be available on Amazon starting May 22. The Galaxy J8 will be available from July 2018. The devices will also be available on PayTM Mall. All the four devices will be available in Blue, Black and Gold color options.

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As we have already covered the features of new Android P, the next version of Google’s OS in our previous article and the released features are more or less the same.

Artificial intelligence and machine learning are the most important announcements made at Google I/O 2018 keynote presentation, and Google Assistant is ready to play an even more central role in Google’s ecosystem than it already has over the past few years. Apart from this, Google also unveiled newly updated version of Android P, which is available to developers (and anyone else who wants to install it on his or her Pixel phone).

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At the event, the search giant launched many features across products including Google Compose, which helps in writing emails faster, new Google News app with AI, Android P and more.

With the Android P Beta, Google aims to make Android smarter and easier to use than ever. Android P has machine learning at its core and will help the smartphone learn and adapt as per the usage.

Android P Updates

1. Google has partnered with Deep Mind, the world leader in artificial intelligence, to build Adaptive Battery for Android P.
2. The new App Actions feature – to help the user to get to the next tasks by quickly predicting what they might want to do next. Actions will show up in different places such as the Launcher, Smart Text Selection, the Play Store, the Google Search app and the Assistant. For instance, just as the user connects the headphone jack, the phone will perform an action to resume playing the favorite playlist
3. Slices – available only through Google Search. This feature will show results in slightly larger interactive snippets
4. Look and feel by introducing a new navigation system
5. New and improved Dashboard – shows how the user is spending time on the device including time spent in apps, number of times the phone has been unlocked and more
6. App Timer allows user to set time limits on apps
7. New Do Not Disturb mode that not only silences the phone calls and notifications, but also all the visual interruptions that pop up on your screen
8. ML Kit – a new set of cross-platform APIs available through Firebase which offers developers on-device APIs for text recognition, face detection, image labeling and more
9. A nutrition tracker –  can easily deploy our text recognition model to scan nutritional information and ML Kit’s custom model APIs to automatically classify over 200 different foods with your phone’s camera

The Android P Beta is available in Google Pixel devices, and will be available on a select few partner devices including

• Pixel 2 / Pixel 2 XL
• Pixel / Pixel XL
• Essential Phone PH1
• Sony Xperia XZ2
• OnePlus 6 (coming soon)
• Xiaomi Mi Mix 2S
• Nokia 7 Plus
• Oppo R15 Pro
• Vivo X21

How to install Android P Beta on your smartphones?

1. Go to android.com/beta
2. Sign up for Android P (users need to sign up with the same Google account  that they have signed in on their smartphone)
3. Accept Android Beta Program Terms of Service and share the feedback about Android P with the company from your device and by joining the Android Beta Program Google+ community
4. After you register, you’ll get a notification on your device about a system update being available. That’s Android P

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Chinese consumer electronics and mobile communication company, Oppo brand under OPPO Electronics Corp., has finally launched its flagship smartphone Oppo F7 in India today in an event held in Mumbai. OPPO’s major product lines include smartphones, Blu-ray players and other electronic devices.

This newly launched baby is the successor to the Oppo F5, the highlight of this device is its whopping 25MP front-facing camera with Artificial Intelligence features, which the company is claiming to be the first on a camera smartphone.

The only real competition for the handset in this aspect comes from Vivo which is offering a slightly low 24MP selfie-camera in its recently launched Vivo V9.

Price & offers on Oppo F7

Oppo F7 is priced at Rs 21,990, for the 4GB RAM variant and Rs 26,990, for the 6GB RAM variant. Both the variants come in Solar Red, Blue and Moonlight Silver color options. There is also a Black diamond-cut edition of the Oppo F7.

The device will be available via flash sale only on April 2 online exclusively on Flipkart and offline on its 777 stores. The first sale of the handset will begin from April 9.

Specifications of Oppo F7

• 6.23-inch full-HD+ display with 1080×2280 pixel resolution and 19:9 aspect ratio.
• It has an iPhone X-like notch on the top.
• Processor- An 64-bit octa-core MediaTek Helio P60 processor paired.
• Storage options – 4GB of RAM with 64GB ROM and 6GB of RAM and 128GB ROM.
• Dual sim support and a dedicated microSD card support, with triple slots.
• AI Beauty 2.0-powered 25-megapixel front-facing camera with Sony 576 Sensor HDR.
• Single rear camera lens of 16-megapixel with LED flash, comes with AI recognition for up to 16 scenes. The camera also boasts of the Portrait and Vivid mode.
• Color OS 5.0 based on Android 8.1.
• Facial unlock feature.
• Battery- 3400mAh (offers 15 hours of battery backup on a single charge).
• Connectivity- 4G, VoLTE, 3G, Wi-Fi, Bluetooth and GPS.
• Fingerprint sensor at back

AI enabled device focuses on better color reproduction and detailing. As for the Artificial Intelligence features, the camera is capable of scanning more than 296 facial spots and arm and neck recognition as well. It is capable of differentiating the gender, age, skin tone, and skin type of the person in front of the camera. The AI-learning of the Oppo F7 recognizes the habit of the user and remembers it and does that automatically for them for their subsequent pictures. Another feature of the camera is its Vivid Mode, which is said to keep the face of the person natural but enhances the background. The camera of the Oppo F7 also comes with AR-based sticker support as well as group selfie-beautification capability.

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Frontback, among top 17 Photo sharing apps, A Photo Sharing App akin to Instagram (albeit with a much smaller user base), is expanding into the social media realm. Frontback lets you capture a photo of you and what you see. Take a photo with the front camera, another with the back camera, and share them both in a single image. It’s you and your perspective.

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The app allows users to share two photos stitched together as one — one photo taken with the front-facing camera and the other with the rear-facing camera, hence the name — and added both public and private comments on Thursday. Now users can respond to images they see in their stream, sending back their own image or video complete with text if they want.

It’s one of the first major product changes for Frontback, which launched just over a year ago and gained early attention thanks to celebrity users like Twitter co-founder Jack Dorsey and actor Ashton Kutcher (an investor). The San Francisco-based startup, which took a $3 million seed round shortly after launch, has been relatively quiet since.

App Features

— Capture moments & events using the front and back camera
— Share photos instantly via all standard iPhone sharing features
— Explore many feeds including: recent posts, staff picks and posts your friends liked
— Follow friends, family or other community members
— Frontback Reactions: Interact with friends by commenting on their posts with text and/or photo

Frontback was recently acquired and is managed by a fresh new team with lots of new ideas to make ths fun photo social app more interesting!

One can download this app from Google Play store and App store for their respective devices.

Top 17 Photo Sharing Apps

1. Instagram
2. EyeEM
3. Flickr
4. PhotoBucket
5. Snappr
6. StreamZoo
7. Trover
8. TouchReTouch
9. Cluster
10. SnapSeed by Google (developed by Nik Software), SnapSeed Android, SnapSeed iOS
11. VSCO
12. Tumblr
13. AfterFocus
14. FacebookMoments
15. Google Photos
16. ImGur
17. Foap

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