Cyber criminals, also known as hackers, use computer systems to access business and personal information for malicious purposes. There is no doubt that criminals are the most creative people in the world and the development of Artificial Intelligence has only made them stronger. Since the tutorials and tools for its development is widely available in the public domain, AIs use for attacking purpose is hugely unrestrained. Machine learning poisoning is one of the ways for criminals to circumvent the effectiveness of AI. Today we live in a world of chatbots. Most people do not realize how much personal information AI-driven bots may know about them, which makes them easy prey for experienced cybercriminals. Moreover criminals could harness machine learning technology to sift through huge quantities of stolen records of individuals to create more targeted phishing emails. However the biggest fear remains in the fact that the fully unstoppable AI creature will seize the world one day. Thus AI security abuse must be prevented and humans being much better than machines must understand its cause and effect. Read more at: https://resources.infosecinstitute.com/criminals-can-exploit-ai/#gref

Gender pay gap in the workplace is one of the most relevant issues which can be solved by data driven decision making. On taking a closer look, it has been found that gaps in pay structure arise from unconscious biases and strategies that benefit one gender more than the other.  Research shows that there is no connection between the fairness of a raise and the effect of that raise on the gender pay gap, making it a complex issue. This can be solved with Data analysis and visualization. Rather than raising the salary of every female employee by the same percentage, building algorithms using companies’ data is more effective to eliminate the gap. This approach also brings the manifestations of unconscious bias in the pay structure to the forefront. Thus, data driven solutions can test salary decisions before making them thereby closing the gap.

Read more at: https://insidebigdata.com/2019/06/26/addressing-demographic-pay-gaps-with-data-driven-solutions/

With the advancement of data science, Chatbots are on the rise. Chatbots optimize customer service as they prod intelligent conversations between a human and an automaton. Chatbots, driven by machine learning, constantly collect new data from their interactions with customers to deliver improved experiences. It has been estimated that in 2020 over 85% of all customer service interactions will deploy Chatbots. They can act as virtual advisors and look into customer issues, can decipher typos made by interlocutor, provide speedy responses with 24/7 assistance to customers, deliver help in the banking industry, etc. Chatbots being the employees that can work without taking any rest, are useful in the insurance industry, Facebook Messenger and health service as well. Developments of Machine Learning algorithms and technology behind Big Data are the pillars of Chatbots.

Read more at: https://www.smartdatacollective.com/big-data-leads-to-impressive-array-of-chatbots-in-customer-service/

   With the idea of improving Gmail security, Google has adopted new big data security standards. Nonetheless, users should incorporate big data to secure their Gmail login. So far, Gmail is not the most secure email servers in the market as it wants our emails and details which act as data. Big data enhances Gmail security by prioritizing advancements in cybersecurity and malware protection and making two step verification more reliable. As Gmail users, we should upgrade our browser every time we are notified about it and use a sophisticated password which should be a unique combination of characters, letters and numbers. Hence it’s a blessing for us that Big data has started looking into Gmail security concerns.

Read more at: https://www.smartdatacollective.com/4-brilliant-ways-to-use-big-data-to-boost-gmail-security/

Having entered the era of Data Analytics, lately it has been realized that even the shipping industry is driven by Big Data. Companies track KPIs (Key Performance Indicators) to measure its performance and spot areas that need improvement. KPIs can be applied to shipping logistics as well and this monitoring can be done easily by Big Data. Shipping Damage is a vital metric which Big Data tracks during transit as it’s important to identify and curb damages of shipments. Big Data helps monitor Shipping Time and identifies the causes of delayed deliveries. It also ensures that customers’ expectations are met by tracking Inventory Accumulation. Owing to its versatile nature, Big Data can also monitor Shipping Costs. Thus given the profound impact that Big Data has on shipping, it’s important that shipping companies incorporate data analytics to save themselves from higher costs and other damages.   

Read more at: https://www.smartdatacollective.com/data-analytics-optimizes-shipping-through-kpi-tracking/

Predictive Analytics make predictions about the future events based on current data. These days retailers use it to determine the future needs of the customers. The biggest challenge faced by retailers today is customer retention. Predictive Analytics is bringing about a huge change in the retail experience altogether. Eminent retailers like Amazon is using predictive analytics to make customer recommendations based on purchasing history. Furthermore, development in Artificial Intelligence and machine learning is boosting its use. These developments in turn are helping the retailers gain a competitive edge. However tech giants like Google, Microsoft tend to keep the cutting-edge innovations for them. The new and innovative predictive analytics must level the playing field for small medium industries. Read more at: https://channels.theinnovationenterprise.com/articles/retail-reaping-rewards-predictive-analysis

With benefits like tracking the response of Instagram users to different Instagram stories, delivering better content, deeper understanding of the ROI of various time slots, etc. Instagram marketers can use Big Data to attract more followers and drive more sales. The feature of Instagram stories help increase these numbers backed by big data. Appealing content for Instagram stories can be created by first determining the goal of the story. Next, a great concept story needs to be spun to convey the benefits of the brand’s products to Instagram users. After building the concept, the story outline needs to be created which should then be sketched visually. Application of these steps and proper incorporation of Big Data should keep Instagram marketers from buying followers without causing any hindrance in the achievement of their goals.

Read more at: https://www.smartdatacollective.com/big-data-for-instagram-using-data-to-perfect-instagram-storyboard/

Since the emergence of Green Revolution in the 1960s, Indian policies have focused on ensuring national food security. However the small and the marginal farmers suffered a lot as the Green Revolution policies failed to increase their income. This has resulted in an increase in farm indebtedness and distress leading to farmer suicides all over the country. Over the last few decades Indian agriculture has seen massive technological developments and opportunities. The recent policies focus on increasing farmer incomes through digital innovations to give the farmers greater access to technology, risk management, finance and markets. Digital innovations enable the farmers to have credits at lesser risk and cost. Digital extension services provide real-time advice to farmers .Among the innumerable obstacles faced by Indian farmers, some include distance from the market, dependence on moneylenders for access to capital, huge cost of transportation, etc. Digital solutions are seen as a way to overcome these obstacles by providing farmers transparency and unmediated market access. Thus digital innovations are greatly helpful in improving farmers’ livelihoods and Indian Agriculture as a whole by making it more market-oriented. Read more at: https://www.thehindubusinessline.com/opinion/the-digital-route-to-transforming-farm-sector/article26006290.ece

Machine learning applies artificial intelligence to automatically learn and improve from experience without being explicitly programmed. Machine learning applications are generally applied to those areas which involve processing lots of a data; a field where humans aren’t well-equipped. Machine learning applies discovered insights in ways that can optimize the customer experience. Chatbots provide effective solutions by stimulating an interaction with a customer service representative or resolving simple inquiries. Machine learning helps chatbots learn when to give specific responses, from where to gather necessary information and most importantly when they should hand off a conversation to a human agent. Virtual assistants, with the help of machine learning focus on specific areas where they can provide assistance to the customers. In order to continually optimize, customer service needs measurable analytics. Machine learning can help add a predictive element to support analytics. Thus machine learning helps in delivering better customer experiences. Read more at: https://www.business2community.com/brandviews/zendesk/how-is-machine-learning-being-used-in-customer-service-02215814

Irrespective of the business size, employees are always on the front lines of the company’s cybersecurity. Based on studies, it has been concluded that two out of three cybersecurity incidents occur because of employee negligence. Employees can be the company’s best defense or its biggest liability. Therefore, cybersecurity training is very important. Employees tend to reuse the same password everywhere. This is a risky practice making it easier for sensitive information to go in the wrong hands. Company Policies must ensure that employees change their passwords every few months. Moreover employees should be taught to spot phishing attempts, including spelling and grammatical errors, suspicious attachments, request for personal information, free offers etc. Sometimes just visiting a site can infect the entire network. In order to maintain security, employees should be taught to hover over the links before clicking on them to see the actual URL; if it looks suspicious it would be best to avoid it. Thus as we see, untrained employees can be the biggest cybersecurity threat. Hence companies should shore up their security systems, starting right from its employees. Read more at: https://channels.theinnovationenterprise.com/articles/employees-cybersecurity-threat

Google’s internet bot detector has evolved over the years from Captcha to reCaptcha to reCaptcha V3. Now, no more boxes need to be checked, nor is identifying objects or alphabets required. Studying the way an individual navigates through a website, conclusions can be drawn on it being a bot or not. This improvement however comes in at the cost of the user’s privacy. According to a researcher, Google uses cookies to identify whether a user is a bot or not; the same cookies which enable us to open new tabs and open accounts without the need of logging in everytime. Another researcher however states that a browser connected with a Google account is more secure than the one which isn’t. Hence, although security is gained, more of the user’s data is also going into the hands of Google. Read more at: https://www.fastcompany.com/90369697/googles-new-recaptcha-has-a-dark-side

Google maps new features not only includes the updates of traffic, but also how crowded the public transport will be. Google has been asking people to fill up forms wherein they are asked about their departure time and number of seats available. This data helped the Google maps in predicting the peak time and the most populated stops. Similar study has been done about predicting the crowds at restaurants. Also, delays in bus and their expected time of arrival is notified by the Google maps, which are available on both Windows and iOS for almost 200 cities. Read more at https://techcrunch.com/2019/06/27/google-maps-can-now-predict-how-crowded-your-bus-or-train-will-be/

With advancement in Internet of Things (IoTs) such as asking the personal digital assistant to read the news, give weather reports, turn on/off the lights, locking up the house and many more, our lives are being simplified, though not as seamless as expected. In Edsger Dijkstra’s words, “Simplicity is a great virtue but it requires hard work to achieve it.” It is analytics that trains the technology for doing hard work so that its virtue can be enjoyed by the people. Hence, with every passing day, as IoT is taking over, Analytics market is becoming more complex since machines have their own languages and specifications and idiosyncrasies. Read more at https://www.experfy.com/blog/to-serve-man-the-internet-of-things-and-analytics

Walmart acquired Flipkart is one of the two biggest e-commerce platforms in India with eight million shipments per month on an average. With ever increasing speed of people coming online each day, Walmart new Automated Guided Vehicles (AGVs) will help in meeting these demands faster. This pilot project of sortation will be applied at Flipkart’s Soukya sortation center in Bangalore, where the 100 self-guided robots will sort the packages according to the delivery pin codes. While these AVGs are expected to increase efficiency by 60%, i.e. 4500 shipments in an hour, the easy mobility of the AVGs will always help Flipkart in expansion of supply chain operations. However, according to Jeff Bezos, AVGs won’t take away employment opportunities but only increase efficiency. Read more at https://www.businessinsider.in/flipkart-announces-delivery-robots-automation/articleshow/68493938.cms

Few factors where data analytics can be seen creating an impact on this sector as stated below: • Reporting and Business Intelligence: With the help of reporting and dashboards, the travel companies can draw inferences. The most important factor for this industry is seasonality, which can be studied by performing analytical operations on the data. • Alerting and monitoring system: Anomalies and rare observations can be detected which is not possible manually due to huge chunks of data available. • Optimization and efficiency building: Analytics help organizations prioritize their investments and redirect them towards high priority factors. • Personalization: Personalizing always attracts attention of customers. • Enhancing business strategy and customer experience: Along with variations in seasonality, forecasting is equally necessary to define the business goals. Read more at https://www.moneycontrol.com/news/technology/travelling-on-data-how-data-analytics-is-transforming-the-travel-industry-4131941.html

The field of AI is largely unexplored and we have only scratched the surface of what AI is really capable of.Understand the AI types will allow us to gain perspective on its capabilities and impacts in future.The degree to which an AI system can imitate human performance is used as a criterion for determining the type of AI.Based on this, AI is classified into two types-One type is based on classifying AI enabled machines based on their resemblance to the human mind , ability to think and feel.According to this system,the four types of AI are-reactive machines,limited theory machines,theory of mind and self aware AI.The second type of AI classification usually used in tech has three categories- Artificial Narrow Intelligence(ANI),Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI). Read more at: https://cognitiveworld.com/articles/7-types-artificial-intelligence