According to the head of Russian bank Sberbank, Artificial Intelligence (AI) will see a sixteen-fold increase in to the pool of global GDP, to become worth US$ 16 trillion in this period. This fact has been reiterated by a study conducted by the McKinsey Global Institute which put AI-induced annual GDP growth to about 1.2 % for a decade. This will lead a huge demand for talent recruitment of specialists in this area. Consulting firm PwC also confirmed that about 7.2 million jobs will be created in the UK alone, where 7 million jobs will also be lost. So, that will be a net gain of two-hundred thousand jobs, silencing the fears of job losses.

Source:https://www.rt.com/business/444240-ai-global-gdp-growth/

Uploaded Date:21 November 2018

Artificial Intelligence (AI) is impacting digital marketing in several ways now. In fact, the two are now working in tandem. One of those ways is an improved user experience as brands are able to gauge what are the true requirements of their customers, so delivering accordingly. Voice search technology being used through digital assistants such as Siri, Google Home and Alexa is an example of this trend. Another benefit is predicting customer behaviour thanks to the vast treasure trove of data warehousing now done, which allows for an analysis of historical trends down to granular bits of detail. This data collection is now being done by third-party DMPs (Data Management Platforms) AI enabled chatbots are also facilitating real-time customer support. Facebook Messenger is an example of such initiatives being undertaken.

Source:https://www.forbes.com/sites/nicolemartin1/2018/11/12/how-ai-is-revolutionizing-digital-marketing/#5ee990c81f62

Uploaded Date:17 November 2018

Artificial Intelligence (AI) and Machine Learning (ML) may now be applied in a multitude of business applications, though many organizations do not yet understand the exact ways of doing so. Infrastructure services and solutions may be worked out by this tech as being done by Cisco. Financial advisors RAA is using the same for its cybersecurity requirements. In the health care industry, it helps in early detection of anomalies as well as helping in cost saving. AI automated tools are helping in talent recruitment by identifying the right fit candidates. AI is also building intelligent conversational interfaces which provide product information, particularly useful for hotels and retail outlets. Energy use and costs have been lowered at Atomiton. Vulnerability can be detected early on as done at Kenna Security. The vast data warehousing taking place is enabling business insights to help with predicting market behaviour and to deliver most customer-centric solutions. The speed of reading written tests is going up and resilience validation taking place. Intentions and behaviours can now be better understood using AI. The accounting, fintech and billing functions performing much better. Even while submitting proposals, the review process, now taking place quicker.

Source:https://www.forbes.com/sites/forbestechcouncil/2018/09/27/15-business-applications-for-artificial-intelligence-and-machine-learning/#23a7b275579f

Uploaded Date:13 November 2018

It is clear from Toyota’s investments that the Japanese automotive giant is in no mood to give up its industry leader status in the upcoming 4^th industrial revolution. It is investing resources on capabilities in Artificial Intelligence (AI), robotics and big data. The Toyota AI Ventures has invested in May Mobility to propel self-driving shuttles ferry passengers across college campuses and central business districts. Its other investments in this field include Nauto, Intuition Robotics, Box bot and SLAM core. In terms of robotics, the company has invested in Human Support Robot (HSR) which is one of the most powerful of its kind. Toyota’s AI enhancements are also working on automobiles. It collaborated with Japan Taxi to curate an AI-enabled cab dispatch mechanism. It engages in large-scale data warehousing which it tracks using real time feed. This warehouse is then used to gauge trends to reduce accidents by as much as possible. In this field, Toyota has also invested in Perceptive Automata and developed its own Concept i-electric vehicles.

Source:https://www.forbes.com/sites/bernardmarr/2018/11/09/the-amazing-ways-toyota-is-using-artificial-intelligence-big-data-robots/#69bbc3f43863

Uploaded Date:13 November 2018

A recent book has been released titled Prediction Machines: The Simple Economics of Artificial Intelligence (AI) written by the trio of Ajay Agrawal, Avi Goldfarb and Joshua Gans. The book speaks about the essentiality of prediction behind the success of any AI application. It includes predicting speech intention with Amazon Echo, command context in Apple’s Siri danger so brakes may be applied when using Tesla’s autopilot, news to read on Facebook or information to search on Google. This depends a lot on the data warehousing that platforms do to generate information for future use. While a lot of fear has been instilled in us about the effect AI on employment, the good news is that there will be an added requirement of judgement, which only humans possess as of now. So, while AI can render many tasks obsolete, new tasks get added. The book also mentions certain inputs which company leaders need in case they are planning to shift towards a more AI-centric strategy.

Source:https://www.strategy-business.com/article/When-Prediction-Gets-Cheap?gko=fa526

Uploaded Date:10 November 2018

The availability now of huge treasure troves of data presents a golden opportunity for the healthcare sector. In particular the proliferation of EHRs (Electronic Health Records) has created a great source for generating business intelligence on patient care. This presents an opportunity to provide personalized care to patients. Machine Learning in addition is being applied to EHRs to fuel further information. Some guiding principles have emerged which pharma companies can use while deploying business analytics. Before any project is undertaken, it is necessary to devise a hypothesis against which the results can be tallied. To formulate a hypothesis, one needs to engage with the right stakeholders beforehand. One also needs to understand that the best source of data may not be one but several. Indeed, several data points may be needed to get the right hypothesis. For achieving really good results, the right feedback loops are needed. Critical insights can then be uncovered.

Source:https://hbr.org/2018/10/how-a-pharma-company-applied-machine-learning-to-patient-data

Uploaded Date:10 November 2018

The USA faces a wide-ranging mental illness problem. The number of suicides as a result of depression and the consumption of anti-depression pills has gone up immensely. Yet, about 40% of the country’s population lives in area inaccessible to psychiatrists. That is where the use of Artificial Intelligence (AI) can avert the epidemic to some extent. AI in its present avatar is not without flaws, especially when dealing with people. Inherent biases creep up depending on the samples modeled on, so false alarms may be sent. This is something that AI vendors will have to work on. There are four main approaches that AI can be used in to detect and cure mental illnesses. First of all, humans can be made better using the virtual mental health services of Ginger.io. Problems can be anticipated using the services of Quartet Health. How Quartet does this is through its enormous data warehousing capabilities, where it maps the various symptoms to predict what can happen. A Dr. Bot can also come in use, which will genuinely present medical advice and treatment. Next generation services can also be predicted. We do not as of now know what kind of mental problems could afflict the people in the future, so these may be compiled by using the services of players such as Ellie.

Source:https://hbr.org/2018/10/ais-potential-to-diagnose-and-treat-mental-illness

Uploaded Date:10.November 2018

The definition of a successful business may differ from what people perceive, but bankers have a very specific lowdown with key defined financial figures. Machine Learning (ML) is now playing an increasingly crucial role in running banks’ operations and to provide readymade solutions. They are already processing the huge quantities of data available to gauge authentic business intelligence on the market. Unlike the traditional top-down model used by bankers, ML has a more holistic approach, even bottom-up. ML is not all about machines. It is about helping a human make a better decision as has happened with credit decisions. ML-based suites can even put in recommendations on other more creditworthy applicants. Rather than helping small-business lending portfolios, entire communities will get empowered using Machine Learning.

Source:https://www.forbes.com/sites/forbesfinancecouncil/2018/11/01/how-machine-learning-is-quietly-transforming-small-business-lending/#5cf9ea5b6acc

Uploaded Date:06 November 2018

Air Asia, the low-cost airline has teamed up with Google Cloud to align the company’s business operations with Machine Learning and Artificial Intelligence. The company owns vast reams of data which it plans to leverage using these technologies. The insights gathered will then be used for digital marketing purposed, providing personalized content to targeted markets. Two platforms are to be launched which are- airasia.com and Big Life. The first is primarily for bookings, but the latter is a platform with features combined from players such as Groupon, Kayak, e-Bay and Trip Advisor. To ensure the success of such plans, Air Asia has decided that its technical team will undergo the same corporate training that Google Cloud’s employees go through. It will help the internal team gain a solid foundation on the subject.

Source:http://www.gmanetwork.com/news/money/companies/672424/airasia-taps-google-cloud-for-machine-learning-artificial-intelligence/story/

Uploaded Date:05 November 2018

Machine learning and advanced business analytics are increasingly being deployed in various business situations across industries. The vast quantities of data warehousing now in place, is allowing firms such as Netflix, Nordstrom and Amazon to provide personalized services to their customers. However, merely deploying the same may not have the desired effects. For better utilization, certain aspects need to be checked in beforehand. The first of them is whether the data being used is clean, and not factually compromised. A proper data governance measure needs to be put in place across the organization’s functions. Equally, the data management team must be comfortable with the external, unstructured data, as that may bring in valuable inputs. Proper tools need to be identified to ensure connected planning across functions. Dedicated time needs to be allotted for curating an end-to-end organizational planning. Finally, capabilities need to be built or existing ones utilized to ensure the right business partners are identified.

Source:https://www.financialexecutives.org/FEI-Daily/October-2018/How-Finance-Can-Use-Machine-Learning-To-Improve-FP.aspx

Uploaded Date:31st October 2018

The nature of marketing is evolving rapidly thanks to the use of Artificial Intelligence (AI) in various processes. More than half of the marketing leaders surveyed at the 4^th Annual State of Marketing have confirmed to making use of AI. Brands are even using AI tools to evoke emotions among their audiences. Digital marketing campaigns are now getting personalized to target individual buyer personas. Research firm Gartner even claims that brands with personalized targeting will do 30% better than those not adopting such methods. While brands are making use of the tremendous data warehousing capabilities around, sometimes there is excess of the same leading to analysis paralysis. AI based tools are helping marketers be pinpointed with the relevant information. This further allows marketers to curate content specific to the audience.

Source:https://www.martechadvisor.com/articles/machine-learning-amp-ai/use-artificial-intelligence-connect-with-audience/

Uploaded Date:31st October 2018