Trends Identified
Computing fore-cast: Into the clouds
Long foreshadowed under names like “grid computing” and “network computing,” cloud computing is finally gaining momentum. Rather than simply replacing one computing paradigm with another, the era of the cloud looks to create a somewhat chaotic proliferation of options, with many paradigms coexisting. Any layer of the technology stack—from computing power to storage to services—can be sourced from the “cloud” and, because IT needs are diverse, every cloud layer should be able to find a market. Organizations will be free to evolve individual IT models, based strictly on business needs rather than on technology constraints; hybrid, “partly cloudy” models will be the norm. This new, adaptable IT frame - work may make it much easier to manage 4 issues of cost, scale and agility. But decision makers must also be prepared to navigate a new set of tradeoffs: the price of agility may be the loss of some visibility—or some control. Most enterprises will want to take their bearings carefully before heading off into the cloud.
2010
Accenture technology vision
Accenture
The Quantum Leap
Long the domain of science fiction and theory, quantum computing looks poised to outperform traditional supercomputers and achieve supremacy in 2018. Competitors in the space are aggressively racing to increase their qubit computing power, while minimizing the potential for errors. Experimentation of quantum computing in conjunction with encryption, AI, materials, and qubit generation will be key areas of focus in 2018. Be ready to hear “Quantum” as the next buzz word.
2018
Top 10 Tech Trends For 2018
Forbes
Carbon dioxide (CO2) conversion and use
Long-promised technologies for the capture and underground sequestration of carbon dioxide have yet to be proven commercially viable, even at the scale of a single large power station. New technologies that convert the unwanted CO2 into saleable goods can potentially address both the economic and energetic shortcomings of conventional CCS strategies. One of the most promising approaches uses biologically engineered photosynthetic bacteria to turn waste CO2 into liquid fuels or chemicals, in low-cost, modular solar converter systems. Individual systems are expected to reach hundreds of acres within two years. Being 10 to 100 times as productive per unit of land area, these systems address one of the main environmental constraints on biofuels from agricultural or algal feedstock, and could supply lower carbon fuels for automobiles, aviation or other big liquid-fuel users.
2013
The top 10 emerging technologies for 2013
World Economic Forum (WEF)
Labor productivity and talent management
Low birth rates and graying workforces in most developed economies will make it hard for them to achieve steady growth unless they continue to make sizable gains in labor productivity. A majority of all respondents, 62 percent, do expect moderate gains in the next five to ten years in developed economies, and another 13 percent expect the gains to be significant.
2010
Five forces reshaping the global economy: McKinsey Global Survey results
McKinsey
On startups built on voice platforms
M.G. Siegler, general partner, Google Ventures. On startups built on voice platforms: I continue to be on the lookout for startups in the audible-computing space. The rise of Amazon’s Alexa and Google Home in 2018 has these devices in millions of homes already, and this holiday season should only accelerate that trend. I would include Apple’s AirPods in this general space as well. These are not niche products. But the jury is still out—people need to learn to use these devices beyond just listening to music or asking for the weather. I believe they will, especially as young people grow up with them integrated into their lives. It will take time, but I think the groundwork can be laid in 2019.
2019
The biggest tech trends of 2019, according to top experts
Fast Company
Adversarial Machine Learning Becomes Key for Security & Fraud Prevention
Machine learning has had its advantage in effectively delivering rapid prediction of trends and establishing robust risk management and inference. Much investment, time and focus by organisations has been dedicated to programming and training machine learning algorithms to fulfil these functions. However, if these algorithms become compromised, they will be prone to attacks from threats and viruses. The chaotic damage that permeating cyberattacks have inflicted in algorithms can dangerously result in the misclassification/alteration of information within them; in effect an organisation’s entire system’s security can be at stake. Cybercriminals constantly seek to successfully exploit weaknesses of learning algorithms of highly valued organisations. Fraudsters are responding to the enhanced detection capabilities for transaction fraud and account fraud offered by fraud detection and prevention service providers. In some instances, they are also using machine learning algorithms to uncover weaknesses in fraud detection systems, in a type of machine learning chess match. It is here that the choice of FDP vendor becomes important, in terms of how its machine learning solution is implemented. Is a static model used, or does the vendor employ an adversarial model that adapts to changing conditions? Fraudsters will have very little knowledge of the precise algorithms being used to detect fraud. As a result, time, effort and funding must be sourced to identify weaknesses, which may then be applied or replicated across other merchants assumed to be using similar algorithms. Sectors (healthcare, industrial, advertising) where protection of huge amounts and types of sensitive data (e.g. consumer/public data) is a high priority – will be the drivers here. Spend on Fraud Detection & Prevention software in the financial sector, ie for eCommerce transactions including ticketing, money transfer and payments, will reach $10 billion by 2022. These sectors recognise the value of determining and containing susceptibilities (e.g. to detect unauthorised access points, weak security infrastructures, etc.) in machine learning approaches within adversarial circumstances. They will prioritise increasing their understanding of these vulnerabilities in machine learning algorithms. They’ll also engage with machine learning specialists to design and implement effective action steps to address these vulnerabilities. Juniper Research believes, moving forward into 2019 and beyond, that adversarial machine learning will be required by numerous industries to: · Identify weaknesses of machine learning algorithms during the learning and identification process · Enforce the protection and integrity and validity of data in these systems · Action steps in response to specific threats · Assess the potential damage of these threats · Programme algorithms to enhance their resistance to viruses · Eliminate the presence of opaque ‘black boxes’ · Grant adequate time to algorithm developers to invest into ‘breaking’ the efforts of cybercriminals to infect data Related Research: Online Payment Fraud: Emerging Threats, Segment Analysis & Market Forecasts 2018-2023.
2019
Top Tech trends 2019
Juniper Research
Conversational Systems and User Interfaces –Talking to Machines
Machine Learning has reached a state in which conversational systems can have increasingly engaging and human like conversations. These chatbots or digital assistants use mainly natural language processing (NLP) to interact with humans but will use sight, sound, tactile, etc. in the near future as well. Conversational systems can be viewed as a step towards true personal virtual assistants and will replace most common interfaces, simplifying human-machine interactions and replacing less interactive interfaces. The technology will probably merge in part with Immersive technologies and will also leverage the digital network of sensors, appliances, and IoT systems to interact with humans. The chatbot ecosystem is already robust, with many different third-party chatbots, native bots, distribution channels, and enabling technology companies. Messaging platforms such as Facebook Messenger, WhatsApp, Tencent’s WeChat, Google Allo, Apple iMessage, Slack and Kik all use chatbots as well as voice-controlled consumer devices like Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, Google Assistant and SAP Co-Pilot. While the focus is on consumer applications right now, especially based on mobile technologies, chatbots will go business very fast, allowing businesses to leverage the inexpensive and wide-reaching technology to engage with more consumers and to support, simplify and speed-up many business processes. Examples are commerce applications as taxi cab ordering (such as Uber), concierge facilities (such as Sephora), B2B contract workfl ows (such as Apttus), food ordering (such as Domino’s Pizza) and others. Use cases for chatbots are helping humans to use technology without the need to know technology like in navigating and exploring, automated customer services, and supporting functions for hand-busy tasks in fi eld service, logistics, maintenance, medicine and others.
2018
Trend Report 2018 - Emerging Technology Trends
SAP
Machine Learning Coming to Verify Your Identity
Machine learning will come into its own as a technology underpinning digital identity. Identity verification still largely relies on manual processes, bringing documents into the bank. Machine learning maturing as a technology which can carry out suitable verification checks.
2018
Top Tech trends 2018
Juniper Research
Specialized Artificial Intelligence And Machine Learning
Machine-learning driven automation will lead to a wide range of new business opportunities, Evans says, including many companies focused on single-purpose implementations of AI and automation. As well, he says, automation will provide a massive multiplier effect by being able to do the small tasks that thousands of people could do–like look for patterns in images. Kocher says his firm will continue looking for digital health companies with novel ways to reduce costs and improve outcomes. “We continue to look at things that take economic responsibility for the cost and outcomes of care, and use technology and data to make both of those better and more efficient.”
2018
The Most Important Tech Trends Of 2018, According To Top VCs
Fast Company
Machine / robotics Automating common tasks
Machines and robotics are endowed with AI (programmed algorithms) to ful l set tasks and goals. These generally fall into two key categories: ‘speci c task-based AI’ (e.g. a web search engine or an autonomous vehicle) and ‘general AI’ that aims to replicate aspects of human intelligence (e.g. IBM’s Watson or humanoid robots like Honda’s ASIMO).
2017
Surfing the digital tsunami
Australia, Commonwealth Scientific and Industrial Research Organisation (CSIRO)