Software Agents

Agents are here to stay, not least because of their diversity, their wide range of applicability, and the broad spectrum of companies investing in them. As we move further and further into the information age, any information-based organization which does not invest in agent technology may jeopardize the  commercial potential of their organization.

Biometrics: Facial Recognition

Facial recognition systems are capable of identifying individuals from a video frame or digital image. The technology has been deployed in areas such as law enforcement, customs, and airports, but retail businesses are beginning to realise its potential in not just surveillance but in customer tracking. The US retailer Saks is testing facial recognition in its new Canadian shops, with many other large US retailers planning its use in the near future.

Facial Recognition Software

Neo Face Facial Recognition Software. Image Courtesy of NEC

Facial recognition will allow for one to one personalized marketing strategies, to enable bricks and mortar retailers to compete with online shops who can gather information about customers more easily. NEC has several different facial recognition software packages including Neo Face Reveal for forensic applications, and Neo Face Watch for operational security.

Biometrics: Fingerprint & Palm Vein Identification

A fingerprint or palm vein can be used to verify identity by creating a digital image captured by sensor which can be compared against a database. The technology has been in use in areas such as smart phones, operating systems, time and attendance systems, and national identity cards. The Biometrics Research Group estimates that mobile authentication will generate $9 billion in revenue by 2018 by means of instant electronic payments, unlocking devices, and other authentication services. The group estimates that worldwide, the mobile biometric market will generate $45 billion by 2020. In addition, the group forecasts the rise of biometric smartphones from 200 million in 2015 to 2 billion by 2020.

Biometric Fingerprint Identification

Biometric Fingerprint Identification. Image Courtesy of KONA I

Many other applications for fingerprint authentication are coming into existence such as biometric door locks. Instead of a key, the locking mechanism is controlled essentially by a finger lock. The device can store multiple approved fingerprints, and those prints which don’t match the database are refused entry. The device can also allow temporary authorization, and can be integrated with smartphone apps by Bluetooth or WiFi.

Intelligent Personal Assistant

VIV

VIV. Image Courtesy of Viv.Ai

An intelligent personal assistant (or simply IPA) is a software agent that can perform tasks or services for an individual. These tasks or services are based on user input, location awareness, and the ability to access information from a variety of online sources (such as weather or traffic conditions, news, stock prices, user schedules, retail prices, etc.). Examples of such an agent are Apple’s Siri, Google’s Google Now, Amazon Echo, Microsoft’s Cortana, Brain, Samsung’s S Voice, LG’s Voice Mate, BlackBerry’s Assistant, SILVIA, HTC’s Hidi, IBM’s Watson and Facebook’s M- Wikipdedia.com

Dag Kittlaus the co-founder of Siri Inc. considers Siri to have been only the beginning of the Intelligent Assistant technology story, and is currently working on what Kittlaus describes as a “global brain” – a new form of voice-controlled virtual personal assistant. Viv will not be confined to phones, but will integrate into everyday objects such as fridges and cars. Viv is expected to launch this year. Intelligent assistant technology is seen by many manufacturers as a means to add sophistication to their products.

Data Mining Agents

Data Mining Agents are intelligent computer programmes used to track, analyse, summarise, and extract knowledge from data sources. Intelligent mining agents can detect patterns, relations and associations present in data which may have initially been considered of little value. The importance of data mining has become apparent to business in relation to Knowledge Management, as the agents can be used to detect trends in data. Data analytic strategies are considered essential by business to increase sales and revenue.

Hewlett Packard Enterprise has just launched HPE Haven On Demand, a machine- learning-as-a-service, cloud version of its big data analytics software. Haven On Demand offers 60 API’s to allow developers and business build data rich applications. The platform offers deep learning analytics, face recognition, speech to text, and knowledge graph analysis.

Product Intelligence

The integration of IT into products is considered to be the third wave of the technology revolution, where products not only communicate with people, also with each other. What were once considered dumb products will now be able to “speak” and this is vital to areas such as stock control and the supply chain. Business is beginning to realise the importance of personal relationships with customers, and intelligent products offers opportunities not available through traditional marketing strategies. Tesla has recently transmitted a software update to deal with hill starting issues, and the speaker manufacturer Sonos recently sent an update that optimises the acoustic settings based on speaker locations. John Deere assists farmers by analysing soil and weather conditions from sensors on their farming equipment. The primary goal is customer loyalty, where products can introduce a host of additional services as add-ons, such as platforms connected by sensors to smartphone apps.

Smart products will require a new technology stack to support the new infrastructure including, product hardware and software, connectivity, product platform, security, gateway, and business integration systems. Bosch has recently fitted its software platform to Jaguar, and has invited developers to create apps. A products connectivity will become a determinant in a consumer’s choice of purchase, and platform loyalty will provide customer loyalty. Business will need to think beyond bricks and mortar in assessing their business model; Uber owns no cars, Ebay has no inventory, and Airbnb has no hotels. Information Technology specialists will need to co-exist with engineers as products move into the software domain.

The Data Supply Chain

The Data Supply Chain refers to the collection, storage, analytics, and the resultant business insights and actions that are used in all aspects of decision making within the organization. The data can be considered in a similar fashion to raw material in a traditional supply chain. Amazon uses Big Data to monitor, track and secure 1.5 billion items in its inventory that are laying around 200 fulfilment centres around the world, and then relies on predictive analytics for its ‘anticipatory shipping’ to predict when a customer will purchase a product, and pre-ship it to a depot close to the final destination. Professional social network LinkedIn uses data from its more than 100 million users to build new social products based on users’ own definitions of their skill sets (N.R. Sanders, 2014). The Data Supply Chain can provide large scale efficiency in operations and performance, but the resource is underutilized in many organizations.

Through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage- Gartner

Automation & the Workforce

Potential Effect of Automation

Potential Effect of Automation. Image Courtesy of Oxford Martin School & Faculty of Philosophy

Technology is also the foundation of new species of businesses that are capable of wiping out entire industries (D. Tapscott). The advent of autonomous vehicles, digitised medicine, workplace robotics, self-service systems, and supercomputers, seem highly likely to eliminate many present day industries and the employment they provided. Technology can potentially automate most forms of transport and shipping. All repetitive Physical and manual labour is at risk of being automated. Highly skilled work will be a collaboration with cognitive computers. The workforce will need to be highly educated and highly skilled, or risk obsolescence.

What we’re going to see is automation. Right now, manufacturing is trickling back to the United States. It’s not rushing back, because of the infrastructure costs, because of the difficulty in retraining a workforce. Give it 5 or 7 years and that trickle becomes a flood-  V. Wadhwa

Key Technology Inc. expects that automation in agricultural vegetable processing will occur in a low oxygen, lights off environment, with no operators on the production line, and sensors collecting equipment and product parameter data to allow for optimum processing.

The near future applications for the Data Supply chain include:

  • Supply Chain Management.
  • Machine generated information from sensors, RFID tags, meters, and GPS. Containers will take stock of their own contents, pallets will geo-locate themselves.
  • Entire supply chain will be interconnected, including assemblies, sub-assemblies, parts, and smart objects.
  • Intelligent automatic decisions through analytics, and modelling

Supply Chain Management will require knowledge in mathematical modelling, statistics, forecasting, finance, economics, marketing, and accounting. At present supply chain talent is in short supply, with 85% of US students in supply chain management programmes with job offers before graduation.

The shortage of talent is the largest current risk to Supply Chain Management-  Gartner

Machine Learning

Machine Learning uses algorithms that iteratively learn from data analysis, and creates analytical models automatically without being specifically programmed to find the insights it produces from the analysis. The recent development in Machine Learning is the ability to apply complex mathematical equations to larger data sets, and at a much faster speed. Some of the applications include self-driving cars, the intuitive programme recommendations on Netflix and Amazon, and fraud detection. There are four main areas of Machine Learning, supervised, unsupervised, reinforcement, and deep learning. Deep learning is considered to be the most interesting area for business as it attempts to teach computers to contextualize through neural networks, producing high value predictions.

Business uses for Machine Learning include:

  • Recommendation engines- suggesting the right product to the right customer at the right time.
  • Medicine- helping medical staff diagnose patient’s symptoms.
  • Benchmarking- predicting trade prices in bond markets.
  • Banking- Machine Learning engines to reduce cost and churn.

Cognitive Computing

The aim of Cognitive Computing is to augment rather than replace human knowledge. It attempts to make computer systems work jointly with people, in an information rich dynamic environment, where there is constant change. Cognitive technologies have the potential to complement, improve, or automate a wide variety of activities, both manual and knowledge based. Technology companies are moving swiftly to create and capture value in this emerging area. High-profile acquisitions by Google, Apple, and Facebook are piquing interest in Cognitive Computing technologies such as robotics, expert systems, computer vision, and speech, gesture and facial recognition.

Quantum Computing

Unlike a traditional computer which works on bits of binary data that can only have a value of 1 or 0, a Quantum Computer by means of quantum mechanics uses quantum bits called qubits to encode the 1 or 0 into two distinguishable quantum states. This allows for the quantum system to be in multiple states at the same time, known as superposition. There is also an extremely strong connection between particles regardless of the distance between them, known as entanglement. Both superposition and entanglement allow a Quantum Computer to process very large amounts of data at the same time, rather than in sequence as with a traditional computer.

The applications for Quantum Computing include:

  • Solve enormously complex problems.
  • Software validation.
  • Drug discovery.
  • Complex predictive analysis.
  • Security and communication encryption.
IBM Quantum Computing as a Service

IBM Quantum Computing as a Service. Image Courtesy of IBM

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