POTENTIAL TRENDS WITHIN TECH
Biotechnology is the broad area of science involving living systems and organisms to develop or make products, or “any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use. Bionic Hand controlled by brain signals is one of the major example.
In recent years, there has been a significant increase in VC investment in UK biotech. The amount of capital invested alongside VC into UK companies increased from an average $105m per year in 2008 – 2010 and rose to $647m in 2015, a more than six-fold increase. About 60% of financing rounds in 2016 included VC. 2018 should be another intensive year for Biotech, with many important clinical trial results and regulatory approvals expected to keep the biotechnology industry and Biotech’s holdings in focus.
In near future everything will be done by robots. More accurately. Less Time consuming.The first decade of the 21st century has been a remarkable time for innovation in robotics. While we’re still far away from having robots helping around the house or doing our construction, big strides have been made towards that future.
The robotics industry continued to go from strength to strength as 2017 represented the largest year of investment, with the total amount adding up to $2.7 billion, according to ABI Research, a market-forecast advisory firm. Robotics investment has accelerated since 2015 with current evidence already suggesting 2019 will represent another year of success for robotics companies seeking funding.
IoT becomes BIoT
The Internet of things (IoT) involves adding smart sensors to connected devices so that users can do things like ask Amazon’s Alexa digital assistant to turn off the lights or order a pizza.
Blockchain, one of the underlying technologies for the hot cryptocurrency bitcoin, can make IoT devices even more useful. It creates a digital record across hundreds or thousands of computers, vastly reducing the risk of hacking.
Combining IoT with blockchain or BIoT ushers in a whole host of new services and businesses. For example, BIoT can be used to track shipments of pharmaceuticals and to create smart cities in which connected heating systems better controls energy use and connected traffic lights better manage rush hour.
In 2019, companies will begin to use Application Programming Interfaces, or software used to connect different databases and computer services. Combined with the blockchain Internet of things, it will be as easy to get data from sensors in a warehouse as accessing websites on our mobile phones. When manufacturers, retailers, regulators, and transportation companies have real-time data from sensors imbedded on products, trucks and ships, everyone in the distribution chain can benefit from insights that they were previously unable to get. With BIoT, companies and consumers can also be assured that their most valuable data on the blockchain cannot be hacked.
Cars and Fuel
Many scientist are working on running cars and vehicles with low costing fuels and are available.
A new wave of venture capital focused on the car technology sector is sweeping across Silicon Valley.
Venture capitalists poured $10.6 billion into healthcare startups in the first half of 2018, according to new data out today in the MoneyTree report from PricewaterhouseCoopers and CB Insights. In the second quarter alone, $5.3 billion was invested in 216 healthcare deals, placing the sector at number two on the list of the top five fundraisers, second only to internet companies.
Positive trends in the health sector include:
– The aging population and the baby boomers
– People living longer with chronic disease
– Obesity and diabetes epidemics
– Technological advances
– The global reach of disease
– Personalized medicine
Nano -Fibers will make garments tremendously more comfortable and durable. By this process the textile products can be made more attractive, strong and responsive to customers’ choice.
Some of the companies who are delivering tomorrow’s futuristic nano clothing:
Bolt Threads: founded in 2009, Emeryville in California, it has taken in a whopping $213 million in funding so far to create synthetic spider silk using genetically modified yeast cultures.
Spiber: founded in 2007, Japanese startup Spiber has taken in $148 million in funding to develop a technology that allows for the use of DNA coding in proteins to manufacture basic industrial materials such as textiles, metal or plastics.
Modern Meadow: founded in 2011, New York startup Modern Meadow has taken in $53.5 million in funding from names such as Sequoia Capital and Temasek Holdings. The team is using DNA sequencing to grow collagen in a laboratory.
Osmotex: founded in 2008, Switzerland startup Osmotex has taken in an undisclosed amount of funding to create an electronically controlled active membrane called HYDRO_BOT that transports moisture very effectively. Sports, work, and protective clothing that utilize the technology can release moisture at the same rate that humans sweat, even under challenging conditions or during intensive activity.
Solar energy will soon leave fossil fuels and inefficient wind farms in the dust. The cost per watt of solar energy is coming down rapidly and the total amount of solar energy is growing exponentially. It has in fact been doubling every two years for the past 20 years and is now only eight doublings away from meeting all of the world’s energy needs.
The solar sector saw growing investments and venture capital (VC) funding in the first half of 2017 globally. Major deals were huge photovoltaic projects in the UAE and energy storage investments in China.
Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.
Artificial intelligence, particularly its applications in health, finance and the automotive sector, attracted US$12 billion of investment from venture capitalists globally last year.
The importance of AI:
AI automates repetitive learning and discovery through data. But AI is different from hardware-driven, robotic automation. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks reliably and without fatigue. For this type of automation, human inquiry is still essential to set up the system and ask the right questions.
AI adds intelligence to existing products. In most cases, AI will not be sold as an individual application. Rather, products you already use will be improved with AI capabilities, much like Siri was added as a feature to a new generation of Apple products. Automation, conversational platforms, bots and smart machines can be combined with large amounts of data to improve many technologies at home and in the workplace, from security intelligence to investment analysis.
AI adapts through progressive learning algorithms to let the data do the programming. AI finds structure and regularities in data so that the algorithm acquires a skill: The algorithm becomes a classifier or a predicator. So, just as the algorithm can teach itself how to play chess, it can teach itself what product to recommend next online. And the models adapt when given new data. Back propagation is an AI technique that allows the model to adjust, through training and added data, when the first answer is not quite right.
AI analyzes more and deeper data using neural networks that have many hidden layers. Building a fraud detection system with five hidden layers was almost impossible a few years ago. All that has changed with incredible computer power and big data. You need lots of data to train deep learning models because they learn directly from the data. The more data you can feed them, the more accurate they become.
AI achieves incredible accuracy though deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them. In the medical field, AI techniques from deep learning, image classification and object recognition can now be used to find cancer on MRIs with the same accuracy as highly trained radiologists.
AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property. The answers are in the data; you just have to apply AI to get them out. Since the role of the data is now more important than ever before, it can create a competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.
How AI is being used:
AI applications can provide personalized medicine and X-ray readings. Personal health care assistants can act as life coaches, reminding you to take your pills, exercise or eat healthier.
AI provides virtual shopping capabilities that offer personalized recommendations and discuss purchase options with the consumer. Stock management and site layout technologies will also be improved with AI.
AI can analyze factory IoT data as it streams from connected equipment to forecast expected load and demand using recurrent networks, a specific type of deep learning network used with sequence data.
AI is used to capture images of game play and provide coaches with reports on how to better organize the game, including optimizing field positions and strategy.
Challenges of using AI
Artificial intelligence is going to change every industry, but we have to understand its limits.
The principal limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.
Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.
In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.
Likewise, self-learning systems are not autonomous systems. The imagined AI technologies that you see in movies and TV are still science fiction. But computers that can probe complex data to learn and perfect specific tasks are becoming quite common.
HOW AI WORKS
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. AI is a broad field of study that includes many theories, methods and technologies, as well as the following major subfields:
Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without explicitly being programmed for where to look or what to conclude.
A neural network is a type of machine learning that is made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.
Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.
Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. Using AI and cognitive computing, the ultimate goal is for a machine to simulate human processes through the ability to interpret images and speech and then speak coherently in response.
Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.
Natural language processing (NLP) is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.
2. HOW TECHNOLOGY WILL GROW IN THE NEXT 20 YEARS
2019: eye-controlled technology: advances in face and movement recognition software usher in an age of machines that are controlled by gestures or eye movements.
2020: paper diagnostics: cheap diagnostic tools made of specially designed papel enable rapid screening for Ebola, tuberculosis, Zika, swine flu and many other diseases.
2023: designer antibiotics: bottom-up technology for building macrolides allow for cheap, bespoke antibiotics to defeat superbugs.
2024: ingestible robots: consumable, biocompatible microbots that repair our injuries from within.
2026: smart clothing: nanoporous fabrics, miniaturized electronics, and haptic feedback make for smart clothing that change color or shape and keep you cool or warm as the need arises
2027: photonics technology: increased bandwidth, a data rate 100s of times greater than RF, and lower power requirements for spacecraft communication.
Volcanic mining: precious metal and minerals extraction from active submarine volcanoes becomes feasible and economical.
Spintronics revolution: the rapid commercialization of spintronics (electron spin-orbit technology) revolutionizes smartphones and the IOT.
2029: carbon-breathing batteries. Electrochemical cells that suck in CO2 to generate electricity and valuable byproducts solving at once oue power pollution woes.
2030 super antivirals: broad-spectrum antiviral drug, based on the ISG15 mutation and other genetic therapies arrive on the market.
2031 diamond batteries: nuclear batteries are formed by encasing radioactive waste in artificial diamonds that convert radiation into electricity.
2032: optogenetics: after a decade of optogenetic engineering and research neurological disorders such as parkinson’s, alzheimer’s and many others become treatable.
Nano feasibility. Light driven photomotors and DNA- inspired technology finally make for widespread, inexpensive nanotech.
Cheap solar power: perovskite and organic solar cells near 100% efficiency; innovations in manufacturing techniques make solar power widely available.
2034: unhackable quantum internet: a satellite network using entangled photons for quantum-key distribution will create a fully secure, unhackable internet.
2035: biomimetic materials: new materials inspired by behavior of living thing, have led to self-cleaning clothing, self repairing buildings and the elimination of plastic packaging.
The next evolution of AI: big data analytics and predictive AI come of age-from weather, to elections, geopolitics, evolution and much else.
Designer molecules: artificial molecules made from superatoms with novel magnetic and chemical properties, enable the creation of revolutionary new materials.
2037: 3d printing in every home. The ultimate in home shopping cheap 3d printers in every home can print out almost anything- electronics, furniture, food, and medicine-from files purchased and downloaded from the internet.
Fully immersive computer interface: intuitive interaction with entertainment, infotainment, web-surfing and what have you through advances in VR/AR, projection mapping, haptics, and brain computer interface.
Self-sufficient energy ecosystem: microbial fuel cells, anaerobic digestion tanks, lithium-ion batteries and solar cell technology mean that virtually every home is now a closed-loop self-sustaining energy ecosystem
3. BIGGEST CONSUMERS OF TECH
The biggest consumers of Tech are between 18-29 years old according to a survey conducted by Pew Research Center in February 2018.
What they’re likely to need in 10-20 years
Enterprise apps getting new life with single feature apps
Unbundling an app into several small single feature apps has become a new trend in mobile app development. It will also be the future of mobile apps. In fact, several tech giants including Google and Facebook first unbundled their flagship apps into a several small, and single feature apps designed to address specific user needs with each app. When seeing that particular features enjoy quite a standout popularity with many users using that app expressly for that feature, these big tech brands separated the feature and packed it as a standalone app. Thus we got Messenger from Facebook and Docs and Sheet from Google.
The popularity of a single feature is the necessary prerequisite for such a move. Naturally unbundling several features into a contingent of standalone apps is not a lucrative opportunity for small and medium apps. Such a move unnecessarily will add to the challenge of marketing each app separately. Marketing the USP of an array of apps without enjoying the patronage of a user base is a hilarious task that will be out of reach for most apps.
App thinning will continue
App thinning first introduced by Apple was mainly about providing stripped-off app versions. They add more layers of content and features as and when the user context demands. For instance, when you download and install a gaming app, you will not have to start with all the advanced levels of the game. As soon as the game player advances to the next level the features and contents for the subsequent level can be allowed to access.
While Apple iOS 9 onwards introduced app thinning as an official feature, Android apps for long lacked a similar feature to do so. As apps now demand to be lighter in size and fast paced in performance, Android developers will push app thinning and on-demand availability of certain features with their development initiative. Sooner or later, Android as a platform will introduce some tools that can help thinning apps.
Chatbots will be more common future of mobile apps
Chatbots in mobile apps almost brought a revolution in mobile application development. First of all, thanks to chatbots the users can enjoy a better onboarding experience. They can get active guidance from the bot when facing difficulties about getting things done.
Secondly, thanks to chatbots businesses now can provide round the clock customer service from within an app. Thirdly, a chatbot by engaging users in conversation can help deliver a lot of insights. Like information about the user behaviour and preferences. Lastly, a mobile commerce app can experience more conversion. Chatbots will explain the products and services better and help users to find something and checkout after completing the buying process successfully.
Personalisation will be key in the future of mobile apps
Another major trend to push the user experience of the mobile apps is personalisation. Yes, the future of mobile apps will have more priority to the personalisation. It will be personalization of the look, feel and user experience of the apps.
Thanks to the advanced capabilities of the present generation sensors of mobile devices, app developers, designers and marketers can enjoy a deeper and wider access to user-generated information and insights concerning user contexts. App developers in the future can allow more personalisation. That works in certain features and design elements based on the device in use, user location, timing and typical preferences. Already personalised notifications have become a mainstay in the mobile marketing.
Android Instant Apps will become an established trend
When Google launched Instant Apps, it addressed several concerns at one go. We always wished to have a first-hand experience of an app before downloading it. Moreover, the app market became matured enough. Consequently, it allows users access to apps irrespective of the constraints involved in downloading and installation. From the lack of device storage space to connectivity problems creating issues for downloading, there are several factors that hold users back from downloading an app without giving it sufficient thought. Instant Apps just allow you to use it instantly without needing to download something. All these challenges and constraints will be a trend in the future of mobile apps development.
Smart wearables like the Apple Watch and Microsoft’s Hololens shows an upcoming change in computing and the transition from basic to smart wearables. This opens up new opportunities for vendors, app developers, and accessory makers. The smartphone will become the hub of a personal-area network consisting of wearable gadgets such as on-body healthcare sensors, smart jewellery, smart watches, display devices (like Google Glass) and a variety of sensors embedded in clothes and shoes. These gadgets will communicate with mobile applications to deliver information in new ways. And will enable a wide range of products and services in areas such as sport, fitness, fashion, hobbies and healthcare. Thus, wearable devices connected with smartphones will influence the next generation of mobile application development strategies.
Internet of Things and Mobile-connected Smart Objects
Gartner says there will be 26 billion connected devices by 2020 which includes several hundred smart objects such as LED light bulbs, toys, domestic appliances, sports equipment, medical devices and controllable power sockets etc. These domestic smart objects will be a part of the Internet of Things and will communicate through an App on a smartphone or tablet. Smartphones and tablets will act as remote controls, displaying and analyzing information, interfacing with social networks to monitor “things” that can tweet or post, paying for subscription services, ordering replacement consumables and updating object firmware. Established companies such as Microsoft, with its Intelligent Systems Service, and enterprise software vendors likes SAP, with its Internet of Things Solutions, are also adding Internet of Things capabilities to their offerings.
Various analysts believe positive trend in mobile purchases will continue over the next 4 years as more and more consumers adapt to m-commerce. Increasing popularity of Apple Pay and Google Wallet will facilitate purchases using the mobile phones instead of debit or credit cards. This will require developers to build a mobile application that can process transactions without the need of physical debit/credit cards or cash. Coupled with wearables that can process payments m-commerce will take a different shape. Beyond data collection and predictive analytics, wearables will also play a key role in the future of mobile payments and customer loyalty.
Motion and Location Sensing
Most mobile phones have location sensor capabilities which use multiple positioning methods to provide different granularities of location data. Knowing an individual’s location to within a few meters is useful for providing highly relevant contextual information and services. Motion sensing apps are used in security, anti-theft, power-saving and games. Location sensing is useful in Geotagging, Games, Vehicle navigation, and fitness apps. Apps exploiting precise indoor location currently use technologies such as Wi-Fi, imaging, ultrasonic beacons, and geomagnetic. In the longer run technologies such as smart lighting will also become important. Precise indoor location sensing, combined with mobile applications, will enable a new generation of extremely personalized services and information.
Enterprise mobile management
Enterprise mobile management (EMM) is a set of people, processes, and technology using mobile computing for streamlining businesses. The main dimensions of EMM are security, application management, and financial management. It also includes mobile device management, mobile application management, application wrapping and containerization, and some elements of enterprise file synchronization and sharing. Such tools will mature, grow in scope and eventually address a wide range of mobile management needs across all popular Operating Systems on smartphones, tablets, and PCs. Thus, EMM represents the future evolution and convergence of several mobile management, security, and support technologies.