The Rise of the Machines | Cassandra Voices

The Rise of the Machines


‘Hey Siri, how will AI impact the Future of Work?’

If you have already worked out that whoever lives inside your phone when you say ‘Hey Siri’ or ‘Hey Google’ can read emails out to you, find the nearest movie theatre, or reserve a restaurant table, then Artificial Intelligence (AI) is already in your life. AI automates ‘real time’ scans on your travels, gives current and projected weather data, identifies a spam mail, and, above all is operating on Google’s ever-evolving search engine.

Businesses, big and small, are leveraging artificial intelligence in multiple ways. Large-scale organisations are already making the move towards intelligent data analytics. Two prime examples are chatbots and recommendation systems that we encounter online almost every day. Artificial intelligence enables businesses to process bulky data in real-time. Through this, AI can provide meaningful insights into solving recurring business issues.

For instance, businesses can identify inconsistencies in their operations and anomalies in their patterns to re-strategize their processes. Not just this, but through in-depth analysis provided by artificial intelligence, businesses can also determine the root cause of problems they face.

‘Data-driven’ and ‘AI-driven’ are not synonymous though. The former focuses on data and the latter on processing ability. Data holds the insights that can better enable decisions; processing is the way to extract those insights and take actions. Humans and AI are both processors, with very different abilities.

Among the benefits that AI offer are:

  1. Activising quicker decisions: for example, oil companies can alter the price of gas according to the demand with the help of AI-powered pricing. Similarly, travel sites, retailers, and other services use dynamic pricing on a regular basis to improve their margins.
  2. Effective handling of multiple inputs: machines certainly can do better than humans when it comes to managing big data, and can make complex decisions to predict the best decision and avoid certain errors.
  3. Reduce fatigue: when people are forced to make numerous decisions in a limited time the quality of those decisions diminishes. This is the reason you see candy and snack bars near cash registers at supermarkets; shoppers get exhausted with so much decision-making while shopping, making it much more difficult to resist the sugar craving at the point of sale.

Algorithms have a few weaknesses…

Algorithms can help make equally good decisions at any point in time, helping executives to avoid bad decisions due to exhaustion. This can lead to non-intuitive predictions through more original thinking. Thus, through AI, executives can identify patterns that may not be immediately clear to human analysis.

AI refers to machine intelligence or a machine’s ability to replicate the cognitive functions of a human being. It has the ability to learn and solve problems. In computer science, these machines are aptly called ‘intelligent agents’ or bots.

There are three broad types or categories. Firstly, assisted intelligence, which refers to the automation of basic tasks. Examples include machines in assembly lines.

Secondly, there is augmented intelligence, where there is give and take with augmented intelligence. An AI learns from human input. We, in turn, can make more accurate decisions based on AI information. As Anand Rao of PricewaterhouseCoopers (PwC) Data & Analytics puts it: ’There is symmetry with augmented intelligence.’

Thirdly, there is autonomous intelligence AI, with humans out of the loop. Think self-driving cars and autonomous robots. We see this in something as basic as automatic photo-tagging on Facebook, which came out with an augmented reality application that employs deep learning in real-time object recognition in 2015. You can look forward to driver-less cars and so much more. In the same way, we can expect AI to be applied further in business, particularly in decision-making.

Today’s AI systems start from zero and feed on a regular diet of big data. Data-supported decision-making has been a reality for quite some time now. AI has helped in improving innovativeness and the quality of decision-making. This is augmented intelligence in action, which eventually provides executives with sophisticated models as a basis for their decision-making.

Marketing Decision-Making with AI helps in identifying and understanding customer needs and desires, and align products to these needs and desires. AI modelling and simulation techniques enable reliable insight into your buyer personas. These techniques are now used to predict consumer behaviour. Through a Decision Support System your artificial intelligence system is able to support decisions through real-time and up-to-date data gathering, forecasting, and trend analysis.

Customer Relationship Management (CRM) is another area where Artificial intelligence involves automated functions such as contact management, data recording, and analyses and lead ranking. AI’s buyer persona modelling can also provide you with a prediction of a customer’s lifetime value. Sales and marketing teams can work more efficiently through these features. Recommendation System is another domain where the AI system learns a user’s content preferences and pushes content that fit those preferences. This can help you reduce bounce rate. Likewise, you can use information learned by your AI application to craft better targeted content.

The many blessings of AI: Examples across Sectors

Example of AI are noted across sectors. Volvo would be a good case in manufacturing since it uses AI to improve continually its safety reputation. In 2015, Volvo fitted 1,000 cars with sensors to detect and analyse driving conditions and to monitor the vehicle’s performance in hazardous conditions. The collected data is then uploaded to their cloud. Volvo works on this data with Teradata for carrying out machine-learning driven analysis across its collected data. Volvo’s early warning system now analyses over a million events per week to predict breakdowns and other failures in their cars.

Energy is another sector where application of AI has emerged rapidly in the past five years. BP Plc for example has installed sensors in its gas and oil wells, which continuously collect data to monitor and understand the working conditions of the wells at each site, irrespective of the physical location. Analysing this data helps BP monitor and optimize the performance of their equipment and keep a tab on their maintenance needs to enable smooth and unhindered functioning. This improves operational efficiencies and cost-saving.

The two keywords that we are beginning to see in any AI related discussion on debates are social computing and opinion mining. Social computing helps marketing professionals understand the social dynamics and behaviours of a target market: for example how the social media platforms can track, analyse, evaluate and project consumer behaviour.

Opinion Mining is a form of data mining that searches the web for opinions and feelings. AI has helped shorten the long hours required to do this through reliable search and analyses functions. Typically search engines use this method, which continually rank people’s interests in specific web pages, websites and products. Thus perhaps when you visit a webpage it might tell you that ‘you have visited this page 20 times in the past seven days’.

‘In the end, all technology revolutions are propelled not just by discovery, but also by business and societal need. We pursue these new possibilities not because we can, but because we must.

AI shall lead to enhanced decision-making for a wide range of business stakeholders. With increasing dependency on devices and mobile apps that are AI managed at the core, the new desire creation or consumption of some of these are AI-driven, consciously or unconsciously.

Ethical Concerns

Artificial intelligence is kind of the second coming of software. Instead of serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Prior to exploring the many ways that Artificial Intelligence can be defined or recognise potential opportunities and challenges in machine- or deep-learning, common debates seem to first point out some of the ethical concerns that AI brings in the contemporary society.

Below is a summary of concerns and possible remedies in terms of AI that have been discussed by policymakers and scientists:

(a) Increased application of automation technology will give rise to job losses, but applying the sophistication and complexity of AI should lead to the redeployment or workers, if necessary retraining them for tasks that are still the sole preserve of human beings.

(b) AI will trigger continual machine interaction on human behaviour and attention, igniting a need to address algorithmic bias originating from human bias in the data.

(c) We will need to mitigate against unintended consequences, as it is believed that smart machines may learn and develop independently.

(d) Finally, it will be necessary to addresses burning issues around customer privacy, potential lack of transparency, and technological complexity.

The benefits of AI, however, are so numerous and multi-dimensional that it would be a shame to dismiss this technology outright. For businesses, AI can support both product and process innovation.

This includes improving simple features like simple spam filters, smart email categorisation, voice-to-text recognition, or utilising what our smart personal assistant – such as Siri, Cortana or ‘Google Now’ – can do for us on a daily basis, in addition to automated responders and online customer support.

AI further helps in sales and business forecasting, improving security surveillance, as well as adjusting smart devices to accord with our behaviour.

‘Day-to-Day’ Benefits

At a quick glance let us understand the ‘day-to-day’ benefits of AI for businesses. Firstly, AI improves customer services, linking to virtual assistant programs that provide real-time support to users (e.g. billing).

Secondly it can efficiently optimise logistics and procurement assignments – e.g. using AI-powered image recognition tools to monitor and optimise infrastructure, plan transport routes, etc.

Thirdly, AI improves and increases manufacturing output and efficiency, especially in the automobile industry production line, by integrating industrial robots into workflows, and teaching them to perform labour-intensive or mundane tasks.

Fourthly, AI can predict performance, for example by using AI applications to determine when you might reach performance goals, such as in response time to help desk calls.

Fifthly, AI can predict behaviour, for example by using Machine Learning algorithms to analyse patterns of online behaviour to, for example, serve tailored product offers, detect credit card fraud or target appropriate adverts. This list is certainly not exhaustive, but it gives an idea of the scope of benefits that AI brings to businesses.

Along came Machine Learning and Deep Learning…

Machine learning is one of the most common types of artificial intelligence in development for business purposes. It is primarily used to process rapidly large amounts of data.

Machine learning is useful for putting vast troves of data –  increasingly captured by connected devices and the internet of things – into a digestible format for human consumption. For example, if you manage a manufacturing plant, almost all of your machinery is connected to the network.

Connected devices feed a constant stream of data about functionality, production and more, to a central location. Unfortunately, it’s too much data for a human to ever sift through, and even if they could, they would likely miss most of the patterns. This is where Machine Learning really comes in.

It is also a broad category. The development of artificial neural networks, an interconnected web of artificial intelligence ’nodes’, has given rise to what is known as ‘deep learning’.

Deep learning is a more specific version of machine learning that relies on neural networks to engage in nonlinear reasoning. Deep learning is critical to performing more advanced functions, such as fraud detection. For example, for self-driving cars to work, several factors must be identified, analysed and responded to at once. Deep learning algorithms are used to help self-driving cars contextualize information picked up by their sensors, like the distance of other objects, the speed at which they are moving and a prediction of where they will be in five to ten seconds. All this information is calculated simultaneously to help a self-driving car make decisions such as when to switch lanes.

It would be useful to look at some examples of how AI changes customer experiences as well as making business processes and internal systems more efficient.

AI At Your (customers, retailers, supply chain, e-tails) Service

Let’s turn our attention to Sephora, the makeup brand. When a customer walks into a Sephora store to find a makeup shade before trialling anything on the face a Colour IQ scans her face and provides personalized recommendations for foundation and concealer shades; while Lip IQ does the same to help find the perfect shade of lipstick. This can be a huge help to customers who know the stress of finding the perfect shade by trial and error!

Walmart, the retail giant, are planning to use robots to help patrol their vast aisles. Walmart is testing shelf-scanning robots in dozens of its stores. The robots can scan shelves for missing items, items that need to be restocked or price tags that need to be changed. These robots can free human employees to spend more time with customers and ensure that customers aren’t faced with empty shelves.

Another company to utilize AI is North Face. The company uses IBM Watson’s cognitive computing technology to ask questions of customers about where they’ll wear the coat and what they’ll be doing. Using that information, North Face can make personalized recommendations to help customers find the perfect coat for their activities.

Uniqlo the clothing chain is another example. They are pioneering the use of AI to create a unique in-store experience. Select stores have now AI-powered UMood kiosks that show customers a variety of products and measures their reaction to the colour and style through neurotransmitters. Based on each person’s reactions, the kiosk then recommends products. Customers don’t even have to push a button; their brain signals are enough for the system to know how they feel about each item, which might sound a bit scary!

Amazon Go is Amazon’s cashier-less grocery store where the company is attempting to revolutionize not only the way people shop online, but also the way we interact with bricks-and-mortar stores. The company completely automates the grocery shopping experience. Once the shopper checks in via app, the sensors throughout the store track whichever items they put in their basket. Once their shopping is complete, customers can just take their items and leave. No checkout lines, no cashiers, no baggers. Amazon automatically charges shoppers when they leave the store.

Finally, an extended example would be DOMO, a fast-growing business management software company that has raised over $500 million in funding. They have created a dashboard that gathers information to help companies make decisions. The cloud-based dashboard can scale to the size of the company, so it can be used by teams as few as fifty, or by much larger enterprises. There are more than four hundred native software connectors that let Domo collect data from third-party apps, which can be used to offer insights and give context to business intelligence.

This gives companies using Domo a way to pull data from Salesforce, Square, Facebook, Shopify, and many other applications that they use to gain insight on their customers, sales, or product inventory. For instance Domo users who are merchants can extract data from their Shopify point-of-sale and e-commerce software, which is used to manage online stores. The extracted information can be used to generate reports and spot trends in real-time, such as in product performance, which can then be shared to any device used by the company.

Cut to Credits…

It is now evident that AI brings a colossal amount to the table for a wide range of business stakeholders to add convenience and simplicity to customer experiences, while also saving time and money for business, along with making processes and planning more efficient and future-facing. Debates, nonetheless, should continue to trigger innovative learning solutions around how to offset or reduce some of the ethical concerns that AI brings along with its benefits.

Feature Image: Kismet, a robot with elementary social skills at MIT museum (wikicommons)


About Author

Dr. Boidurjo Rick Mukhopadhyay, DSc, graduated Summa Cum Laude with a BA (Hons) in Economics following which he received a MA from the Institute of Development Studies (UK) and a PhD from the University of Sussex (UK). Rick is an International Development and Management Economist working extensively with the Government Ministries, higher education industry, and think tanks across the UK, EU and China. He is currently researching, consulting and advising in the areas of Development and Environment Economics, Gig economy/ Collaborative Consumption, the Future of Work, Social Innovation and Entrepreneurship. Rick currently sits on editorial and reviewer boards of over a dozen top international peer-reviewed journals and also serves as non-executive director for various nonprofits and charities. Besides publishing his research and speaking internationally, Rick also has experience in leadership development workshops, leading international summers schools (at Sussex, LSE, and several other Universities in the EU), quality assurance visits, accreditations (EQUIS, AMBA), blended-learning (with Pearson), and motivational training. He is currently a Senior Lecturer at WIUT.

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