According to authors, innovations in the areas of Artificial Intelligence(AI),Machine Learning and Deep Learning are beginning to dominate the technology landscape but the actual user experience and concerns about privacy and security are obstacles that can prevent AI from reaching its potential. Despite it’s negative implications, a Consumer Intelligence Report by PwC, revealed, that most consumers believe AI will help humankind, by solving complex problems, like fatal diseases, city traffic, cybercrime, infrastructure and fraud, that plague modern societies , thereby helping people to live more fulfilling lives. AI could revolutionize  personalized healthcare by analyzing data thus helping in maximizing life expectancy and enhancing wellbeing.AI will expand access to financial, medical, legal and transportation services to those with lower incomes thus emerging  both as a process and a product that instills trust and transparency in consumers. Read more at https://aitrends.com/security/consumers-welcome-ai-despite-lingering-privacy-concerns/

According to authors, modern-day marvels are the result of machine learning, which are programs that combine through millions of pieces of data and start making correlations and predictions about the world , by using machines that uses cold hard data to make decisions that are sometimes  more accurate than a human’s, thereby reducing biases. Computers don’t hold any inherent biases, as machine  knows only one approach i.e. the objective analysis and are capable of analyzing massive amounts of information, thus having  a distinct advantage over humans. Utilizing the data for machine learning, can uncover contradictory and surprising results by making data more accessible, more understandable and  enabling the organization to achieve a new level of business intelligence, thus empowering decision makers at all levels with a powerful tool. Read more at https://aitrends.com/machine-learning/machine-learning-cure-decision-bias/

According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/

According to authors, the development of Artificial Intelligence (AI) creates several opportunities for retailers to improve their customer services , insights and business processes. AI makes it possible to generate insights on a scale like never before, it does this by combining customer data  across all platforms( from social media to CRM) like past purchases, search habits, click behavior, age, gender, season of the year and various other variables and self learning algorithms. By crawling the web and aggregating various forms of customer data, AI gives retailers the opportunity to engage with customers on a more personal level. Through chat applications such as Facebook and Messenger and by using chat bots on websites, the potential customers can communicate with the retailers using speech or text, that will assess and answer customer queries, thereby assisting in the selection process and helping in the execution of simple tasks. Read more at https://aitrends.com/retail/robots-ai-retail-8-things-must-know/

According to authors, the days of journeying to a travel agency and sitting at the desk of an agent to book a trip have all but disappeared as technology is increasingly putting that level of personalised service in the palms of our hands by launching travel apps that incorporate artificial intelligence and technology .One such app is the Green Card app, that simplifies the process of getting a green card to  enter  into a foreign country, on the basis of a bot, that files out a questionnaire ,which when answered,  generates  a package of documents that can be filed with U.S. Citizenship and Immigration Services to complete the application process. This saves ample time. Another personal travel assistant Mezi,  designed for travel management companies, corporate and travel agents was launched two years ago and claims to have AI capabilities that customises travel suggestions based on past searches and online habits. Read more at https://aitrends.com/mobile/a-i-innovation-finds-a-home-on-mobile-devices/

According to authors, two of the machine learning APIs introduced by Google in the month of March, has moved forward into a new dimension i.e. open beta status. Cloud Natural Language APIs which analyses syntactically and assigns a constituent structure to the meaning of texts, helps in finding out the sentiments expressed via texts and online reviews, the syntax of the text and assists in understanding  entities like people, places, events, products, and media. New York used this techie for exploring sentiment analysis of  the stories published in The  New York Times and it was found that relevant updates about news didn’t enjoy a favourable environment, whereas, the arts stories enjoyed positive support. Read more at https://aitrends.com/business/googles-cloud-adds-machine-learning-apis/

Deep Learning(DL) is a subfield of Machine learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. It  interprets the raw data through multiple processing layers, where each of these layers, uses the output of the previous one as its input, thus  creating more abstract presentation, tackling conceptual problems ,like image classification and natural language processing  and helping to infer logically. Industries that leverage Machine Learning at present , can switch over to DL approaches in future as it’s an approach of AI , which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. DL drives sales, increases engagement  and improves user experience, thus it  will be the future of Personalization, which enables a business organization to enjoy amass customer appreciation. Read more at  https://aitrends.com/deep-learning/deep-learning-personalizing-internet/

According to authors , a recent global survey from the medical industry workforce, threw light on the following for a business to flourish:

1)80% of the workforce in the remote counterparts, pointed out that Management/Managers should stay in touch with them, via instant messaging apps and frequent conversations over phone.

2)56% of them, stressed on video calls once a week .

3) Mandatory face-to-face interaction, via  attending monthly meetings with managers .

4) Utilizing IT system for both tracking and measuring productivity per worker.

5) Research and evaluation of tools and techniques on a regular basis to stay notified about the new techies .

6) Assuring full support and engagement of the employees in the remote areas. 

 Read more at https://www.processexcellencenetwork.com/pex-tools-technologies/articles/remote-control

According to authors, target  marketing  strategy  is the key to success ,which propels new technology into action to communicate effectively and quickly with the customers. Understanding the intricacies of both changing technology and Artificial Intelligence, is crucial for achieving success, as much of this is happening through machine learning and taking advantage of robots and AI. It’s the responsibility of the Chief Marketing Officer(CMO) to transform and spot out the same, thereby, changing the company’s identity. This requires skilled manpower, who can understand and effectively combine human interaction with machine learning. According to stats, the top revenue generator in the upcoming years will be marketing. Therefore leveraging AI is crucial to amplify the potential gains. Read more at https://www.forbes.com/forbes/welcome/?toURL=https://www.forbes.com/sites/blakemorgan/2017/06/30/how-the-cmo-can-leverage-ai-internally-and-externally/&refURL=&referrer=#6974b0db367d

According to authors, digital disruption, is an unavoidable deluge of new digital tools  that is implemented to refine the business process on the digital front. Categorising the vast arena of business, technology, industry and social changes and planning the technological stack to identify digital disruption over time, helps an organisation in escaping its adverse effects as well as flourish. Following measures will nullify its adverse impacts:1) Maintaining  lean and agile technologies, thereby ensuring flexibility to adapt to the changing environment 2) Staying connected to market research, customers and business partners 3) Implementing formal innovation management programs must be prioritised 4) Regular brainstorming sessions across different departments and teams to strengthen cooperation 5)Provisioning the requisite internal resources to ensure compatibility between existing and upcoming systems. Read more at https://www.qmarkets.net/digital-disruption-businesses-can-best-leverage/

According to authors, MLBox is a powerful  Automated Machine Learning Python library ,but, weighing  the following pros and the cons is crucial before using  it.Pros:1)Automatic identification  and classification of task 2) Pre-processing of data while reading and removing the drift variables, thereby cleaning  files 3) Fast and accurate hyperparameter  optimization and aims at low validation score 4)Constructing a Machine Learning Regressor  for forecasting  using minimum lines of code 5)Uses Entity Embedding for encoding categorical variables for creating new informative traits.  Cons: 1)Things may collapse suddenly since it’s under active development 2) Unobserved Learning enjoys no support 3)Basic Feature Engineering ,requires creating own features 3)Selection method being purely mathematical drops out variables significant on the business front 5) Not truly an Automated Machine Learning Library.Read more at https://www.analyticsvidhya.com/blog/2017/07/mlbox-library-automated-machine-learning/

According to authors, the remarkable impact of Application Programming Interfaces(APIs), is in the analytics front. From accumulating data from new root to evaluation, it has radically changed the face of analytics and the core role of the citizen data scientists ,by gathering right and reliable datasets ,thereby simplifying advanced analytics and enabling them to devote more time behind identifying and interrogating new and valid questions about data. Developers can borrow functionality from other apps by allowing interaction between two pieces of codes , thereby bringing different bits of software together. Efficiency depends on use. Whether APIs will replace citizen data scientists or not, in future, is unpredictable, as, on one hand, business users will have easy access to analytics without their support and on the other hand, their role will also evolve. Read more at http://blogs.sas.com/content/hiddeninsights/2017/06/23/apis-data-scientists/

According to authors, AI and Machine Learning algorithms, introduces us to an era of intelligent conversational environments , by  bringing about a paradigm shift in technologies and strategies ,thereby collaborating the rate of progress in business and process for automation, thus making an enterprise more smarter ,flexible and conversational. AI mainly focuses on making support systems more efficient across a multitude of applications, processes and business domains, bringing about intelligent collaboration capabilities to the emerging Conversational Workspace platforms, improving workflows, automating repetitive tasks and initiating automated interactions via voice assistance ,thereby, helping an organization to take better decisions and grow. Read more at https://aitrends.com/speech-recognition/idg-contributor-network-the-growing-impact-of-artificial-intelligence-on-workplace-collaboration/

According to authors, both Artificial Intelligence and Pattern-Recognition algorithms, which metamorphoses horses into zebras and winter scenes into summer, have revolutionised healthcare, where robot radiologists, can visually diagnose and examine  X-rays ,stained breast cancer slides and medical signs involving correlations between normal and abnormal health patterns,  in detail and with remarkable precision, thereby empowering the doctors to conduct a detailed research and to better  patient’s welfare. The core activity of AI is to make the machines intelligent and using this intellect, an entity can function smoothly, efficiently and  with perfect foresight in its environment, thus allowing an organisation to flourish and expand its operations. Read more at https://aitrends.com/healthcare/supercharge-healthcare-ai/

According to authors, Artificial Intelligence , can easily and effortlessly make a copy of audio and video content with an  intent to deceive the viewers, in a world which believes in Seeing is Believing. Though analytics, big data, and machine learning have helped  us in unfolding the truth, yet  these technologies cannot be fully relied upon, as it lies, by forging audio and video content, leading us to a fake world. As far as the audio and the video forgery is as concerned, anyone can  generate realistic-sounding speech with  varied intonation, in the voices  of famous personalities  and realistic-looking videos  by changing  both the facial expressions and pitch contour, on You Tube Videos, by using AI-based software. Recently, Adobe is working on similar technology  which aims at producing  “Photoshop  For Audio”. Read more at https://aitrends.com/opinion/ai-will-make-forging-anything-entirely-easy/

According to authors, AI is radically changing  the face of banking.  From daily  customer dealings ,learning from them and interacting  with them to becoming the face of their brand , AI/Robots simplifies complex tasks, by, informing  customers about their account balances, prevailing interest rates on mortgages, securities, making  transfers and payments based on voice recognition authentication, interacting in different languages thereby making transactions smooth and flexible and resolving customer grievances online. AI covers the following three techies :   1) Language processing, enabling  communication between computers and  humans, 2)Enables computers in locating similarities and comparing disparities between new and existing datasets , to sketch out patterns, via Machine Learning  and  3)Advanced software system  that provide personalized advice.   Read more at https://aitrends.com/financial-services/how-artificial-intelligence-will-revolutionize-banking/

According to authors,Machine-Learning, a subset of Artificial Intelligence (AI), is applied by firms in evaluating  financial decisions like assessing credit worthiness and eligibility criteria for an insurance policy, fraud prevention, by tracking fraud indicative transactions, thereby reducing the fraud rate, risk management, tasks of compliance, natural language processing, where this system can shift through thousands of commercial- loan contracts in seconds  and trading, where it is used both to crunch market data and to select and trade portfolios of securities. In the quantitative front, machine-learning algorithm , helped in making investment decisions, by picking up acquisitions before announcement, managing the client money and moving beyond the boundaries of conventional trading strategy. Read more at http://www.economist.com/news/finance-and-economics/21722685-fields-trading-credit-assessment-fraud-prevention-machine-learning?zid=291&ah=906e69ad01d2ee51960100b7fa502595

According to authors, today, a new set of analytics tools,  ”Adobe Sensei for Voice”,  has  been  launched by Adobe, which will aid brands to better its metrics, in the field of targeting customers across social media,email and actions often taken by users  with their conversational  AI of choice and the things they daily deal with, by seeking the help of conversational  data- both user intent and contextual  data. This will help developers and brands, worldwide, to have easy access to  total customer interactions, usage time and frequency of intent.  Amazon Echo Dot has been marked as the leading market in the growing space.  Read more at https://techcrunch.com/2017/06/29/adobe-releases-analytics-tools-for-conversational-ai/

According to authors , ABM is an account based strategy that merges sales and marketing. It mainly works with two datasets Ideal Customer Profile (ICP) and Total Addressable Market (TAM) to shortlist customers and individual accounts. Sales allot Tier and Play to each account and decision about the most sensible messaging paths and plays for individual accounts, depends upon the recommendations from marketing. The following technology set up aided ABM to achieve greater success:
• Major technology cleanup
• Marketing Automation and CRM
• Major Database Cleanup
• An account and ‘People’ Build-out
• Good Spreadsheets
Though it has information on target accounts and concerned people, SDR’s(Sales Development Reps) have limited bandwidth and lacked provision to distinguish between the strangers and acquaintances. Read more at  https://blog.insideview.com/2017/06/08/hear-learnings-from-insideviews-own-abm-journey/

According  to the authors, artificial intelligence is applied when a machine mimics “cognitive” functions that humans associate with other human minds such as “learning” and “problem solving”.  So  Han Wang, Jing  Guo  and Colleagues reported in American Chemical Society(ACS) NANO , that  they were successful in developing an artificial lab-made synapse, which was flexible and diverse and  has the capacity of simulating a primal function of our nervous system, i.e., the release of both excitatory and inhibitory signals from the same pre-synaptic  terminal. . This amassed huge demand and popularity  in artificial neural networks and it simplified the design and role of nervous simulations. Read more at https://www.sciencedaily.com/releases/2017/06/170628095814.htm