Artificial intelligence (AI), machine learning (ML) and continual deep learning (DL) are the new age digital technique that are being anticipated to transform the consumer and enterprise experience.Artificial Intelligence is the future of news :-
Most of us turn to television for big news breaks and television news has been regular in one’s lifetime. There is great significance of trends toward digital news content and this is generational gap, with millennials twice as likely as their parents to get their news online.
The business model of TV news is changing There’s been many hypothesis about the end of newspapers as mastheads go digital, but online content is also killing off commercial TV news. Desire for more user-paid content hasn’t only given rise to new media platforms but it also gives impact to delivery mechanism used to connect consumers with the information that matters to them. According to Deloitte Global, 50% of adults in developed countries will have at least two online-only media subscriptions by the end of 2018, which will be rising to four by 2020. Prevailing form of media media is video. According to Cisco, video will soon represent around 82% of total consumer web traffic by 2021, up from 73% in 2016. A research report from the Reuters Institute, "The Future of Online News Video", found 79% of the world’s senior digital news leaders are investing more in online news video. But, it’s not all smooth sailing.
Enter AI-enabled news-as-a-service. Artificial intelligence (AI) is the future of news.Artificial Intelligence to deliver Hyper-Personalized News- AI and automation are already being used in newsrooms across the world to deliver content at fast pace, at scale, with less cost. Many journalists use AI-generated smart templates to produce "commodity news," such as reports on finance markets and company earnings (see Bloomberg's Project Cyborg initiative, as reported on Poynter.org).Artificial intelligence in customer service improves CX :-
Now days consumers have many ways to communicate with the brands like private chat or in public on social media sites such as Twitter, FB. If a conversation conveys a negative attitude or opinion, it can give adverse effect if it's not addressed quickly. AI in customer service enables companies of products and services to respond to consumers before the customer experience breaks down and negatively affects retention. Many companies are inclining towards an early stage on AI tools for help.
Companies n customer service can use artificial intelligence to build a brand that's linked with excellent customer experience (CX).In customer service, AI enables companies to understand what their customers are doing today and in response quickly scale CX strategies. Chatbots can be deployed relatively quickly to handle customer requests around the clock, while social listening tools can track customer sentiment online to gain insight, identify potential new customers, and take proactive action to protect and grow brands. AI technologies including text analytics, sentiment analysis, speech analytics and natural language processing all play a very important role in customer experience management. According to Gartner, by 2021, 15% of all customer service interactions will be handled by AI - that's 400% higher than in 2017. The first step in choosing software for artificial intelligence in customer service is to understand that there is no single tool that works for every customer in every scenario, said Whit Andrews, an analyst at Gartner.Artificial intelligence is required for email security best practice :-
Artificial intelligence is needed to fight the threat of email security as its landscape is changing and is more dangerous than ever. In vendor security products and solutions more and more artificial intelligence is being used. This use of innovative technology, coupled with cyber security training, are crucial aspects of cyber and email security best practice. AI can be used in cyber security training to simulate attacks based on recent data compiled by recent incidents — which makes training courses relevant. Employees security awareness can be monitored by AI systems and this can give information about what types of attacks would employees fall for, and where an organisation is more susceptible.
The old days are over now. Cyber attacks model has become more focused and targeted. The old approach of doing email security is no longer sufficient.In these old days, mass campaigns were just reusing the same attack over and over, and sending it to many different people. Security vendors would just detect the threats and create signatures based on all the observed breaches. Once one attack was solved, one solution fitted all as they were all same. The use of artificial intelligence allows organisations to be much more flexible in their approach to security. It means vendors can tailor defences to each organisation.Artificial Intelligence in Real Estate :-
Technology can assist the industry in property management, application processing and data synthesizing this will help agents and brokers to focus more on connecting with clients and helping them with the process of buying or selling a home.
Let’s look at some ways how real estate professionals can utilize AI to improve their operations and increase their business growth:
Artificial Intelligence to Make Petrol Pumps Safer by Picking Out Dangerous Behavior :-
- Advanced machine learning can standardize everyday processes for tenants, property managers and landlords by helping find tenants, locate vendors and provide alerts for routine maintenance and management tasks.
- Increase in the number of recommendations that clients can choose through AI. Agents can convert their knowledge of a client’s wants into data an algorithm which can be used to find a higher number of matches at a faster rate.
- Virtual tours can be provided by AI for interested homebuyers and answer simple technical questions about the property on behalf of the agent, such as square footage, questions about the lease and more, through 24/7 chat boxes.
- Through AI-based customer relationship management (CRM) systems, agents can maintain long-term relationships with their clients when they need help to move, sell or to buy multiple properties in the future
- Everyday tasks can be more efficient and AI can provide assistance to clients at a short notice.
Due to the huge amount of data on internet, machine learning and deep learning have the capability to predict and prevent various catastrophically dangerous events. Now, Shell wants to take advantage of artificial intelligence to make petrol pumps a safer place. Questionable frames could then be immediately uploaded to the Microsoft Azure cloud, which can power more sophisticated deep learning AI models. These can identify that the man is smoking and raise an instantaneous alert on an onsite dashboard, so the station manager can take action to shut down the pump before any harm is done, says a blog post on Microsoft.Artificial Intelligence: The fourth industrial revolution :-
Artificial Intelligence (AI) is not a new concept. AI’s roots can be found in ‘70s and ‘80s, where computers that were programmed with a human’s ‘expert’ knowledge in order to allow decision-making which were based on available facts. Artificial Intelligence can be described as the ‘fourth industrial revolution’. It will remodel all of our jobs and lives over the next 10 years.
We are all around Machine learning, unlocking our phones with a glance or a touch, suggesting music we like to listen to, and teaching cars to drive themselves.Data is growing at a vet fast pace. It is estimated that by the year 2020, every human being on the planet will be creating 1.7 megabytes of new information every second! There will be 50 billion smart connected devices in the world, all developed to collect, analyse and share data. This data is important to AI. Machine learning models need data. ML models need to be ‘trained just as we humans ‘learn’ through our experiences and improving upon.
Opportunities from data sharing- There are huge opportunities around the sharing and fusion of data, between different agencies (local government, health, police). But this also has some challenges around privacy, data protection and a growing public concern.The next step is to “predict” the future – for example when and where crime is likely to happen, or the risk or vulnerability of individuals, allowing the police to direct limited resources as efficiently as possible. Machine learning algorithms can be engaged in a number of ways – to automate facial recognition, to pinpoint crime hotspots, and to identify which people are more likely to reoffend.
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