Artificial Intelligence and Data Science are two closely connected disciplines that frequently collaborate to produce intelligent systems and extract useful insights from data. Despite having different approaches and goals, they are very complimentary and frequently have applications that overlap. You can find below points relevant in understanding how Artificial Intelligence and Data Science interact.

1. Data Acquisition and Preprocessing

Data Science’s primary purposes are data acquisition and data preparation, as well as data cleansing. Operations such as data extraction, data cleansing, data transformation, and data integration are among the activities performed during data warehousing. Various types of information and data which are valuable for AI training depend on the quality to ensure adequate preparation for creating models. In this respect, data science plays the key role of providing the base input data for further application of AI.

2. Machine Learning Algorithms

Data science is a process of making data analysis and processing through different machine learning algorithms for business intelligence. Machine learning algorithms used in AI systems are a set of techniques that allows the AI system to learn from the data and make decisions or predictions without user instructions. This means that careful model selection, evaluation, and tuning could benefit from the skills of data scientists to ensure optimal performance of AI applications.

3. Feature Engineering

The process of choosing, modifying, and producing pertinent features from unprocessed data in order to enhance machine learning model performance is known as feature engineering. Finding significant characteristics that capture the underlying trends in the data and improve the prediction capacity of AI systems is the responsibility of data scientists. To create reliable and accurate AI models, feature engineering must be done well.

4. Model Training and Evaluation

For AI systems to discover patterns and relationships in the data, they must undergo substantial training on labeled datasets. The training or Machine Learning can be categorized as Supervised Learning, Unsupervised Learning, and Reinforcement Learning based on data mathematics fundamental approaches. Data scientists design experiments based on theories and partition data into training and testing sets and evaluate the performance of the model by how high the F1-score, accuracy, precision, and recall rates of the model are.

5. Data-driven Decision Making

The goal of Artificial Intelligence and Data Science is to provide decision-makers with useful insights from data. Some applied their algorithms and machine learning models in predicting the outcomes in an organizations’ decision-making processes. Based on data-driven insights, data science offers the statistical methods and analytical framework needed to evaluate risks, identify opportunities, and interpret model forecasts.

6. Continuous Learning and Improvement

Iterative learning and constant feedback are key components of AI systems’ ability to adjust to shifting circumstances and gradually enhance performance. AI models are retrained by incorporating fresh data and evaluating their performance using data science approaches including A/B testing, online learning, and model monitoring. In dynamic situations, this iterative process guarantees that AI systems stay applicable and efficient.

Final Words

In conclusion, Artificial Intelligence and Data Science are complimentary fields that collaborate to use data to create intelligent systems and make data-driven choices. AI uses data to create prediction models, automate processes, and drive innovation in a variety of fields. Data science supplies the fundamental skills and methods for gathering, preparing, and evaluating data. It is essential to highlight the cooperation of artificial intelligence and data science that enable the users to extract the full potential of their data and gain insight that facilitate innovations and company development. There has many of the job openings for the artificial intelligence, machine learning and data science professional not only in IT sector but in all sectors, for creating a futuristic and sustainable product. AI, ML and DS have great potential for their applications in several sectors and their future looks bright.

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Artificial intelligence (AI) is one of the technological fountains that is prevailing and gaining huger importance in every sectors of our life in the present days. Since it has almost become integrated into every sphere of our lives whether it is our doctors, financial institutions or retailers, online, on manufacturing, film and tv recommendations and even opera singers, our personal assistants on our phones and especially in transport, customer care and schools. What follows in this article will be a look at its impact to the general populace and how it continues to alter the ways through which we go about our day to day activities as well as how we interact with other people and the world in general.

1. Enhancing Convenience and Efficiency

Now, convenience and efficiency benefits are two areas Artificial intelligence has had profound impact on daily existent. Virtual assistants and Smart speakers operating under artificial intelligence include Alexa, Siri, and Google Assistant that today let consumers manage smart devices without physically touching them, procure information without having to flip through a newspaper, book, or search the web, or get reminded of an event without the assistance of a human or a paper calendar. Some of them, for instance, in agenda management, sending messages, alarms and buzzer alerts, even controlling home appliances.

2. Personalized Recommendations and Experiences

AI-driven recommendation systems are increasingly central to our web experience as individuals and acting as a selector of everything that we find engaging or useful on the web. Recommendation systems employ AI algorithms via recommendation engines where platforms like Netflix, Amazon or Spotify analyze the users’ patterns and draw conclusions based on the users’ activity and likelihood of their interest in certain movies, products, or music pieces. Doing so fosters a targeted interaction with clients by following a tailored approach, which in turn improves client satisfaction and, by extension, the levels of satisfaction users have with the service.

3. Improving Healthcare and Well-being

By determining new customized approaches to focus on clients, care for patients and diagnose ailments, Artificial intelligence is revolutionizing the health sector. Machine learning techniques can help the doctors and healthcare practitioners to make better diagnosis and effective treatment plan since data can be analyzed and patterns identified within the data, results forecasted. Further, the performance of the monitoring process can be done in real-time through wearables and health monitoring applications powered by AI, empowering the people regarding the steps they can take to improve the quality of their lives.

4. Transforming Transportation and Mobility

This includes self-driving cars, ride-hailing companies, and GPS navigation – a part of mobility and transport, driven by artificial intelligence. Autonomous cars are capable of revolutionizing how transportation is done through utilization of machine learning algorithms such as eradicating carbon emissions, frequent traffic jams, and even alleviating the frequent incidence of accidents. It applies in ride-sharing services such as Uber and Lyft, where the efficiency of pricing algorithms, path determination, and choice of drivers and passengers enhance the provision of punctual and fairly priced means of transportation for the daily commuters.

5. Enhancing Communication and Accessibility

The use of AI technologies and models as a communication aide and assistive devices continues to make the world more accessible and inclusive to the disabled. Some of the more predominant tools include the ability to speak to a device and have it understand; speech recognition enables people with speech disabilities to be able to speak; voice operated assistance and technology tools such as the text-to-speech are enabling for mobility or vision impaired people. Besides, enhanced translation through the aid of advanced technology, particularly artificial intelligence, improves international relations because it fosters the exchange of ideas across countries.

Areas where Artificial Intelligence Included

In this piece, we describe AI and its growth as a transformative force in society as it continues to spread and become followed across various fields and spheres. Here is a list of the main areas that employ AI.

1. Healthcare

Such items as better diagnosis, more personalized approaches to the treatments, and forecasting have all brought changes to the health-care industry with the use of AI. AI technologies that can detect abnormalities in medical images, including MRIs and XRs, to allow medical professionals to make decisions. Also, the intelligent and more advanced conversational agents, such as chatbots and virtual assistants, help patients avail support services and medical data.

2. Finance

In the financial and banking industries, artificial intelligence (AI) is employed to undertake activities such as algorithmic trading, credit scoring, fraud discovery, and client servicing. To identify fraud schemes and risks, AI systems analyze extensive data from the financial world and help financial institutions to navigate the situation. They also enhance customer relations through the use of AI where chatbots and virtual assistants assist the clients through providing personalized information and support.

3. Retail

In addition, the retail industry has also been altered through positive transitions due to artificial intelligence, through customer relationship management, marketing strategies, and centralized inventory management. The AI algorithms can assist retailers in being able to analyze, from the customer’s side, data and buying pattern and this can aid them in being able to offer product recommendation that is suitable for customers, improve the demand forecasts as well as being able to offer the right price for products. AI conversational self-service systems such as chatbots and virtual assistants can also support consumers in their purchasing decisions and questions. Presently, e-commerce companies like Amazon and Flipkart are integrating AI to their mobile apps where it tracks the consumer behavior of the products and depending on that it makes recommendations of products that may be personalized or individual.

4. Manufacturing

Due to the various ways it helps in process by enabling quality control, process improvement and maintenance the AI is inspiring innovation in the industrial applications. Autonomous systems through machine learning process analyze data collected from machines and equipment to predict issues and fault in them, so as to reduce downtime and increase efficiency. AI systems also improve the efficiency of production, minimize resource usage and the defect rate and boost the quality of final products through real-time tracking and evaluation.

5. Transportation

AI is about taking complete control over vehicles, traffic signals, and various transportation planning and management solutions. The current route planning is aimed at achieving maximal effectiveness, minimizing traffic congestion, and increasing safety; for achieving the set goals, AI algorithms analyze traffic flow, meteorological conditions, and road constructions. For instance, an automobile can utilize artificial intelligence to employ computer vision and machine learning algorithms to perceive highways and also determine actions that the automobile should execute in response to the perceived environment within real-time.

6. Education

In the similar way, the education sector is also changing and being facilitated by artificial intelligence through bettering of the student’s outcomes, minimizing of the administrative burdens and even offering of a more personalized learning. Intelligent and highly individualized learning systems using artificial intelligence apply specific lessons and corresponding assignments on the basis of the pace of the students’ turnover, thereby adapting to individual learning styles. Further, using such performance data from students, AI algorithms identify areas of strengths and weakness for further improvement and then provide relevant counseling and support.

7. Customer Service

Customer service is one of the primary domains benefiting from AI as it enhances the interactivity with the clients and can process numerous typical questions directly and promptly. It is important to note that through a combination of Chatbots and virtual assistants, powered by artificial intelligence, proposals of solutions for clients’ inquiries and problems, as well as providing support through different available platforms including social networks, mobile applications, and websites, available throughout your daily lives. Its called Natural Language Processing where these AI systems can understand and respond to consumers questions in real English.

8. Cybersecurity

Since the AI application is capable of identifying and mitigating a cyberattack within a blink of an eye, it is now considered a reliable solution in cybersecurity. Those that are run by artificial intelligence (AI) study the network data, look for anomalies, and to identify any security threats that have manifested themselves in the network before they go to the next level. Furthermore, AI systems are configurable and learn new threats that may exist and enhance cyber security measures and secure confidential data.

9. Energy

AI is also making significant impact in the energy industry especially in determining how energies are produced, distributed, and consumed to enhance sustainability. The applications implemented in the reference through the AI compute sensor and smart meter information to predict energy usage, optimize the grid usage and manage renewable energy resources efficiently. AI solutions also help facilitating energy infrastructure to operate under the conditions of the so-called ‘predictive maintenance’ –which means that the costs of maintenance are reduced significantly, and the time spent on maintenance is minimized as well.

10. Agriculture

AI is now becoming a significant player in farming as it looks into more sophisticated and effective facets of farming such as crop surveillance and higher yields. From drones and sensors powered by Artificial Intelligence, data on crop status, fertility of the soil, watering levels is collected by farmers to decide on planting, watering levels to use in combating pests and diseases. The following data is also processed through the AI algorithms and to increase the agricultural yields, to reduce the utilization of resources and to increase the production of the farms.

11. Legal Services

AI is bringing changes to the legal field through enabling the tasks like legal research understanding documents, and case management system. Contracts, cases and legal documents can contain huge amount of data that may be easier to be parsed through an AI tool to provide new outlooks for the legal fraternity including the lawyers. Among other tools, AI-based chatbots and virtual assistants also provide self-service options when it comes to frequently performed legal operations and assist clients in legal matters.

12. Entertainment

Thus, AI also enhances the entertainment industry as a whole through timely suggestions and transformative content curation that adapts according to the audience’s preferences. They analyze the user’s watch history and his/her preferred movies, TV shows, and music selection to provide suggestions. Virtual reality (VR) and augmented reality (AR) two Artificial Intelligence tech developments that are changing how consumers engage and or consume digital content.

13. Supply Chain Management

Supply chain is thereby being enhanced by real-time tracking, forecasting, and inventory management with AI. AI systems help predict disruptions in supply chains, select efficient means of transportation and minimize stock expenses due to studying data from the past and current market and customer needs. AI-powered systems also ensure effective preemptive risk management and supply chain management choices.

14. Human Resources

It is penetrating the field by automating typical practices such as talent acquisition, hiring and engaging of employees; consequently revolutionizing ways that Human Resource practices are done. There is now equipment that can review CVs, select candidates, and possibly predict staff turnover rates. Moreover, the chatbots and virtual assistants with artificial intelligence help the HR employees in dealing with various employee related-products such as queries, training modules and even performance appraisals thus resulting in better efficiency of the HR across all industries including IT or others.

15. Marketing and Advertising

Real-time data, messaging and, campaign techniques of self-directed advertising and marketing are major disruptive technologies that AI brought into the advertising and marketing industry. Potential customers, on the basis of consumer data, interaction on the social media platform, and other online activities, are searched, and advertisements are tailored, by the AI algorithms, for greatest outreach and effectiveness of the marketing or advertising campaign.

16. Smart Cities

Technological innovations such as artificial intelligence are helping to build smart cities through a process of cutting out unnecessary bureaucracy in public services, investing resources in raising the general quality of people’s lives, and synchronizing the technological landscape in cities. Real-time traffic control, management of energy consumption, and even identification of natural hazards are achieved through the help of AI operational systems ensuring sustainable functioning of cities. Similarly and in the same context, the use of some other AI applications such as smart grid and IoT sensors can enhance the management of resources and minimized waste.

17. Telecommunications

Through optimal resource provisioning, and efficient and precise network fault prediction, artificial intelligence is enhancing telecom service delivery too. To diagnose and analyze issues and to decide where the traffic should go in order to make the most efficient use of the available link, artificial intelligence systems analyze data received from the network in real-time. AI-integrated service and support tools also suggest services and further customer help and support, which means the consumer experience is improved.

18. Insurance Underwriting

AI is currently applied in insurance industry for the purposes of evaluating and adjusting the pricing formulas and element of underwriting. Insurers use AI analytics to analyze various inputs, which include policyholders’ information, past claims, and other external conditions to evaluate insurance applications and set the level of cover and premiums to be charged. Discuss how solutions that integrate advanced artificial intelligence increase the profits for insurance organizations, decreasing insurance fraud due to faster risk evaluation.

19. Environmental Monitoring

Environmental reportage is facilitated through the intervention of AI through evaluation of the change in weather patterns, the ecological systems and air quality through enumeration of the sensor information, aerial images and weather models. AI can be used for forecasting natural disasters, monitoring animal populations, and detecting logging and cutting.

AI remains to be effectively utilized for prognosis of natural disasters, monitoring of animal species, as well as detection of felling and cutting. It will be very useful for combating climate change mitigation, disaster preparedness, and other conservation activities and thereby assisting the people.

20. Language Identification and Translation

AI technology is developing the identification and translation of languages, real-time translation of languages, language translation and interpretation both online and offline basis, and cross-lingual communication. In the recent past, there existed many automatic language identifiers and all of them are now integrating AI. The useful translation services powered by AI use machine learning algorithms to analyze text, speech, and context to give accurate translations in numerous languages. One of the main advantages of language translation inventions brought by the AI technology is the improvement of general communication between individuals across cultural barriers to gain information.

These are just a few of the many domains where techniques of artificial intelligence are being employed to enhance creativity, efficiency, and the overall default standards of consumer satisfaction. With the advancement of AI technology, its use cases are expected to expand into even more domains and industries, thus providing fresh opportunities for its adoption and evolution.

Conclusion

To sum up, it is proved necessary to mention the ways how artificial intelligence influences people’s lives at present and use it as the theoretical basis for further discussion. Some of these include enhancing effectiveness and ease of operations, over acting as a communication platform, a mechanism of enhancing health delivery, a transport channel and as means of enhancing accessibility. Due to the continuous advancement in the application of AI technologies in the society, these advances should not be taken as harms to humanity but instead people should embrace them to make their lives easier. It is high time to harness it for AI’s potential is not only revolutionary, but also carries within it seeds of equity, efficiency and sustainability if utilized in a responsible and proactively ethical manner.

Image credit- Canva

SAS Business Intelligence Tool is an integrated analytics and business intelligence platform that is a true source of the data of organizations that gives them the necessary features to extract the power of their data. It is acknowledged for its sophistication and analytical skills with the drag and drop features which make it possible to deal with a visual process when you are looking for insights. Its AI and Machine Learning assisted analytics of business users is the key for them to acquire insights and predictions from the data. Hence, SAS BI Tool is shown to be a top choice for businesses that need to take advantage of the complete potential of their information.

In this article, I’ll lay down the key features that make SAS Business Intelligence Tool the most outstanding among the Business Intelligence Tools in the world.

1. Advanced Analytics Capabilities

The SAS BI Tool is characterized by the sophisticated analytics that it has. The user can use statistical analysis, predictive modeling, and machine learning algorithms to get the meaningful information from the complex datasets, this way, the user can make a decision that is based on the data.

2. Comprehensive Reporting and Dashboards

In-depth Reporting and Dashboards are the integration of various data in a way that they give meaningful and valuable insights which become the decision-making tool for the business. The platform stands out in providing the reports and dash boards of great details. Users can compile the drawn reports and interactive dashboards that show the key performance indicators (KPIs) and trends, hence, they are giving a visual interpretation that can be used in the strategic analysis.

3. Data Integration and Transformation

The technique of data integration and transformation can be used to combine different kinds of information and exchange them between different platforms. The SAS BI tool is capable of the smooth data integration and transformation. The users can connect to the many data sources, cleanse and transform the data, and combine the information from different platforms, which will give them a comprehensive view of the company’s data.

4. Self-Service BI

The SAS BI Tool empowers users by enabling them to create their own reports and visualizations, therefore, they are the ones who can control the business intelligence. This openness of the data leads to the democratization of the information, which means that people can get the insights from a wider circle, thus creating a culture of decision-making using the data.

5. Visual Data Exploration

SAS Business Intelligence Tool is mainly used for visual data exploration, and through this, users are provided with the interactive tools to understand and interpret the data in an intuitive way. One can mix several types of charts or visualizations like interactive charts, heat maps to name a few in order to be creative and show the information in the most in a better way.

6. Real-Time Analytics

For the organizations that need the real-time insights, SAS BI Tool is the perfect one that can be used for the real-time analytics. Users can watch and process the data as it comes in, thus the responses to the changing business trends are quick executed.

7. Role-Based Access Controls

Security is an important issue and the SAS BI Tool solves. The tool uses role-based access control. Through the implementation of user roles, organizations can set and control the access to the sensitive information. With the role based access, only authorized user can access the information or resources. This feature increases the level of data security.

8. Predictive Modeling and Forecasting

SAS BI Tool has the predictive modeling and forecasting features in it. Organizations can foresee the future events and outcomes based on the past data and thereby, they are able to take the proactive decisions and plan for the future.

9. Mobile Accessibility

Now, the large-scale mobility has been acknowledged, and hence SAS BI Tool is structured for mobile devices. Smartphone and tablet users can handle reports, dashboards, and analytics making the essential information available anytime and anywhere.

10. Scalability for Enterprise Solutions

The SAS Business Intelligence Tool is created for scalability which is a plus to both small businesses and large companies because they all have analytical needs. Its architecture enables the handling of enormous data sets while at the same time maintaining the high performance, thereby, it is possible to be flexible as the needs of the organization grow.

11. Collaboration Features

Business Intelligence Tool by SAS is the instrument which creates collaboration among the team members through the features which are available for sharing insights, reports, and dashboards. The real-time collaboration tools are the main reason for the teamwork and the decision-makers are now under one roof, so they are in the same platform and they can easily see the data together, therefore, they are in the same understanding.

12. Integration with SAS Ecosystem

To the companies that are utilizing other SAS solutions, the SAS BI Tool will be combined with the whole SAS ecosystem. This integration gives a whole new analytics environment which is a type of unified environment, so that users can take advantage of the combined power of SAS solutions.

In summary, the SAS Business Intelligence Tool is a complete and strong solution for the enterprises that are looking to extract the valuable information from their data. The SAS BI Tool is the perfect choice for businesses that want to rely on data intelligence in order to make informed decisions. It is based on the principles of advanced analytics, extensive reporting and scalability.

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

The AI can direct customers to the most suitable agent based on the nature of their concerns by using the power of AI-powered CRM systems. The former ensures your customers link up to the relevant expert, which in turn reduces the time for query solving.

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 technology makes easier the tasks that entail repetition, such as data entry and subsequent reply emails. Now the employees are freed to use their mind for the analytical and creative elements of their roles.

8. Customer Retention

The AI machinery detects at-risk customers either by identifying changes in behavior pointed by the AI or notifying any abnormalities. Companies will be able to develop retention techniques to reduce clients’ churn.

9. Sales Forecasting

AI is capable of the provision of the realistic prediction of sales numbers based on the past records and the current trends. It is easily understood how forecasting works now, but in the future, forecasting may not be as successful or businesses may not realize the same amount of sale in future, but forecasting typically helps businesses to set realistic goals and allocate resources efficiently.

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- based CRM systems may be used to supervise that the data protection and privacy laws are being followed by using them to check and manage the customer’s data effectively.

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.

Finally, it can be said that Artificial Intelligence has changed the CRM (customer relationship management) sector in a number of ways. Personalized and efficient services are delivered to people, enhanced customer support is done & routine operational activities are automated on top of that data analysis is considered as highly valuable source through which insights can be gained from. Integrating AI in CRM is just not an advanced step but an essential tool for businesses wishing to lead their industry and fulfill the demanding expectations of their clients.

Image credit- Pixabay (pixabay[dot]com/illustrations/crm-customer-relationship-7142692/)

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 powers 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?

Machine learning 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.

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.

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

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.

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.

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 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.

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.

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

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.

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.

>>> Top 10 Most Popular Android Photography Apps You should Use

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