In recent years, a technological and material shift has led to customers seeking products and services that meet their specific, individual requirements. This has filtered through from the retail into the business community where many organisations are now looking to digital transformation as a means of offering the “single complete view of customer” that will allow them to hyper-personalise their interactions. In addition to implementing the data analytics that will provide the necessary insight, businesses must also take steps to “SaaSify” their products, and ensure their applications are supported by platforms sufficiently robust to cope with the peaks and troughs in demand that will come.
Developers have to anticipate numerous variables, program rules, and defined inputs to anticipate what users will say to machines and what machines may say (or do) in response. Consequently, AI conversation will not thrive until it can understand not just the words a person is using but also their underlying meaning. For example, someone speaking a dialect of southern U.S. English might ask a friend to “carry” her to the store. Any native speaker would know that this person is asking for a ride in the friend’s vehicle. Someone not familiar with this expression would certainly request a clarification. An effective AI system needs to possess enough awareness to be able to understand the intent of a question or a command or, at least, when to ask for clarification that will result in the desired action or outcome.
The digital transformation effort then uses strategic education, mentoring, and specific activities (these might be hackathons, MOOCs, certification efforts, reverse mentoring, and #changeagents outreach) to proactively shift mindset across the organization and build the requisite digital skills and ideas. These include counter-intuitive notions that can be hard to otherwise learn: Designing advantageously for loss of control and using the intrinsic strengths of digital technology to change more rapidly and scale out faster. As the organization comes together and engages together on the change platform, it then generates the framework to identify their starting point and guide the ongoing process using rigorous measurement and action-taking, which are two other key success factors, though proactive communication remains the most important action to take.
While some robots may be out to take our jobs, there’s a big skills gap in the AI-fueled services industry just waiting to be filled There will be two major drivers around the jobs of the future. The first will be what can be automated, and the second will be what level of comfort do we have for things being automated. However, far from the widespread fear that automation and artificial intelligence (AI) will make human workers redundant, it seems people are becoming more comfortable with the idea of automation and AI in the workplace every day. Recent research conducted by Adecco Group reveals that many employees feel AI will have a positive impact in creating a future workplace with a myriad of opportunities for more flexible, rewarding work. So if our current roles in the workplace are set to be replaced, what will we be doing instead?
What exactly is dark data? Our connected, digital world is producing data at an accelerated pace: there was 4.4 zettabytes of data in 2013 and that’s projected to grow to 44 zettabytes by 2020, and IBM estimates that 90 percent of the data in existence today was produced in the last two years. But between 70 percent and 80 percent of that data is unstructured — that is, “dark” — and therefore largely unusable when it comes to processing and analytics. Lattice uses machine learning to essentially put that data into order and to make it more usable. Think of it in terms of a jumble of data without labels, categorization or a sense of context — but with a certain latent value that could be unlocked with proper organization.
Organizational and Cultural Changes Are Often Underestimated. This is the number one challenge we hear about when we talk to end-users who have implemented IoT projects and ask them about their biggest lessons learned. Take the German cleaning machine manufacturer Kärcher, for example. Their director of digital product, Friedrich Völker, mentioned that when they started rolling out their connected fleet management solution “our team had no experience in pitching software and virtual offerings to the customers. Rather than making a one-off sale, they are now in continued talks with the customer regarding the ongoing performance of the machine. This change in mindset, as well as the education of the sales team, takes time, and it is just one of many organizational challenges we are faced with.”
Data is anticipated to be the ruler of the digital world in the coming years. It is observed that the world’s data doubles every 18 months while the cost of cloud storage decreases at almost the same rate, which suggests that data will be available in abundance after a few years. This availability of high amount of data will open the doors of better and extensive machine learning experiments as well as deployment. With the use of the improved machine learning services we will be able to get a hold on more refined data. Ultimately the users of these services will increase which will give us more data. This data flywheel will keep on rolling and expanding. For instance, Tesla’s data flywheel is planning to release a self-driving car by 2018, and for that project they have collected a massive driving data of 780 million miles and are adding a new million within every tenth hour.
The need for biodegradable tech in an era when new gadgets are constantly being introduced and quickly discarded, causing tons of electronic waste, presented the key concern and main focus for the team of researchers who have shared this new device. ... Having a biodegradable wearable option presents an answer to the privacy problem that occurs when new devices are discarded in favor of new ones, hoping that the cautionary measures taken to erase old data are effective enough to keep it from falling into the wrong hands. The decomposing polymer that the device is made of ranks as one of the thinnest and lightest electronic gadgets that’s ever been made. The team has synthesized the biodegradable semiconductor by utilizing a molecule taken from tattoo ink, and has created a base by weaving plant fibers into a new, extra-thin film. Inside the structure are embedded electronics.
Systemic methodologies allow us to study systems, to model them, and to therefore use them as a communication vehicle. A systemic methodology that seems promising to apply to the governance of IT is the viable system approach, which focuses on active learning, adaptability, and control. This methodology is deemed useful for the understanding and governance of complex phenomena. Digital disruption is a major concern for many contemporary organizations, and provides challenges that, due to their digital nature, should ultimately be accounted for in the IT governance system. Failure to adapt to constantly-changing circumstances can be problematic for organizations, as was the case with Eastman Kodak. It is said that their collapse was primarily induced by their inability to keep up with technology change and digital disruption.
IT systems and services procurement must be decided and approved, and the management should disclose the proper investments regarding infrastructure and competencies by confirming assigned responsibilities, ensuring appropriate ways and means, and having sufficient expertise to uphold and care for the IT schemes and systems Zhang & Harte, which are accompanied by a smart investment in workforce and proper IT-related human resource planning and recruitment, and a planned retention scheme of skilled IT staff. RM is documented in various learning studies such as in the study by Drucker as the critical essentials in manipulating the productivity and innovation in an organization, where it can be seen as the key activities to be implemented in the organization. RM entails performing risk awareness, risk understanding, and assessing the organizational desire for risk.
Quote for the day:
"Half of the harm done in this world is due to people who want to feel important." -- T.S. Eliot