To advance the functionality of today’s home appliances to a whole new level, LG Electronics (LG) is set to deliver an unparalleled level of performance and convenience to the home with deep learning technology to be unveiled at CES 2017. LG deep learning technology will allow home appliances to better understand their users by gathering and studying customers’ lifestyle patterns over time. This process never ends and improves over time to provide customers with new solutions to everyday problems.
Robot Cleaner: Improved Performance Through Memory
Using multiple sensors and LG’s deep learning technology, LG’s newest robotic vacuum cleaner will recognize objects around the home, such as wires and shoes, and react accordingly. By capturing surface images of different rooms, the intelligent cleaner remembers obstacles and learns to avoid them over time. The LG robotic vacuum can also maneuver around household objects and differentiate between them and a person. Should someone stand directly in the vacuum’s path, it will politely ask that they step aside in order to continue the cleaning process.
Refrigerator: Smarter Convenience
LG’s deep learning is also enhancing the convenience LG’s smart refrigerator brings to consumers. By analyzing usage and eating patterns, LG’s deep learning refrigerator performs a variety of tasks by “predicting” the family’s activities based on their past behavior, such as automatically filling the ice tray at the time of the day when cold drinks are most in demand. In the summer, LG’s smart refrigerator can initiate the 4-stage sterilization system on its own to extend food life when it senses temperature and humidity conditions that may contribute to food spoilage.
Air Conditioner: Even Better Energy Saving and Performance
LG’s smart air conditioner equipped with LG deep learning technology analyzes the daily behavior patterns of its homeowners, including the parts of the home most occupied at certain times throughout the day. With this information, LG’s deep learning enabled air conditioner is able to assess how to provide the most comfortable temperatures quickly and efficiently, providing fast cooling to specific areas of the home families use at a greater frequency and for longer periods of time such as the living room or kitchen.
Washing Machine: Optimal Performance in Any Situation
The new technology helps LG’s washing machine learn about the local environment and the user’s everyday activities in order to provide the optimal washing performance. For example, in areas with hard water, LG’s smart washing machine adjusts the temperature and the amount of water used to counter the effects of hard water on clothes. In areas where dust storms are common, the washing machine automatically adds another rinse cycle for enhanced cleaning.
“Deep learning technology is the next phase in the evolution of smart appliances, and as an industry leader, we have the responsibility of being an early mover,” said Song Dae-hyun, president of LG Electronics and Home Appliance & Air Solutions Company. “But even more important than what appliances are capable of will be how companies behave when entrusted with data. At LG, we believe performance and convenience do not mean having to sacrifice security and privacy. They can and should exist simultaneously.”
Consumer Interest in IoT Devices Varies Among Gender, Need
🟥 We’ve all seen a horror movie where a killer finds a conveniently unlocked door, pushes open a window, or breaks a glass windowpane without alerting the unsuspecting residents. Bad news for Michael Myers: developments in smart home technology have made it a little harder for these intruders to break in — in fact, that’s the main draw of this technology for some people who buy them.
🚀 What if, upon hearing a suspicious noise outside, you could tell your phone to lock your doors? Or if you could call the police to your residence simply by shouting the command out loud?
It might make horror movies a little less interesting, but it also makes real people safer. 🔻
A survey conducted by home insurance company Hippo broke down what drives consumer interest in smart home devices, finding varied results among gender and purpose. The survey asked 1,000 smart tech users to share their opinions. ⤵
➤ Women look for safety first; men want convenience ⤵
👉 If life was a horror movie, women might have the upper hand. Survey data showed that women are most interested in purchasing home monitoring systems and technology that will keep them safe. The general consensus was that smart alarm systems are the way to go when shopping for smart home tech.
Men, on the other hand, are more interested in energy-saving technologies that will help lower their utility bills. They’re also looking for technologies that will make their lives easier. When it comes to protection, however, men tend to opt for camera systems.🔻
➤ Overall, convenience is key
More than just scaring away things that go bump in the night, smart home technology has opened up a world of possibilities for people looking to make their lives more convenient. You can unlock your front door with your cell phone, adjust the thermostat without leaving your seat, and ask Alexa for that final recipe step without having to wash off flour-covered hands.
🔺Among homeowners, this added convenience was the biggest driver of smart home excitement — 46% of them said this was why they decided to invest. Also driving smart home tech sales are home monitoring capabilities, 17%; added protection, 16%; and lower utility bills, 16%.👇
➤ Today’s smart homes ⤵
🚩 It’s more common than not for a home to have some sort of IoT device, whether it’s a Google Alexa device or a doorbell with a camera attached. Today’s smart homes have a variety of devices performing a variety of functions. The most common four are the following: 🔽
◆ Smart appliances: Appliances like laundry machines, dishwashers, and refrigerators can be hooked up to a smartphone to alert you when you’re out of milk or let you start preheating the oven before you leave work.
◆ Alarm systems: With a smart alarm system, your phone will let you know any time someone opens or closes a door, a window opens or smoke is detected in your home.
◆ Cameras: Smart cameras can send footage to your phone so you can monitor an outdoor pet’s activity, or make sure no one is snooping around your property.
◆ Smart thermostat: A smart thermostat lets you control the temperature from your smartphone and can let you set the temperature to automatically increase or decrease based on lifestyle patterns or weather.
If it sounds like we’re living in the world of tomorrow, it’s because we are. Smart devices have made life more convenient, safer, and more connected than ever — what once required time and effort can now be done seamlessly from your mobile device.
🔥🚀 With a smart camera that alerts you every time a door or window is opened, good luck to the slasher movie villains of yesteryear.
↘ Source: Emily is a content creator for Hippo. When she’s not typing away at a computer, you can find her hiking with her dog or doing a crossword.
Forecasting for Fall Uncertainties
By Scott Lundstrom, Analyst, Supply Chain Futures
Over the last several months, the supply chain planning community has been faced with the question of how to deal with increased uncertainty as we enter the fall. While we are adjusting to COVID-19, we are not overcoming it. Pandemic forces will continue to impact our business as we enter the fall and move into winter. Widespread vaccine availability is still 9 to 12 months away for most people. Environmental and climate disruption challenges continue unabated. Political instability and challenges still dominate the front page.
Our relatively stable world of global supply chains has been upended in ways we could never imagine. What is a supply chain executive to do? While it might sound obvious at this point, COVID has impressed upon us all the need for digital transformation to drive resiliency and agility into our operations. First and foremost, we need to adopt an outside-in view of the supply chain. Viewing the supply chain as a demand-driven business network is essential to avoid execution failures, excess inventories, and the inevitable bullwhip effects of the chaotic business environment. AI and advanced supply chain and data analytics can help, but only if we have the data and processes required to make use of intelligence in creating agility and resiliency.
Changes in philosophy and strategy – from efficiency to resiliency. This really has little to do with technology. Change management among senior leaders can be incredibly challenging but is an absolute necessity. Adopting a focus on outside-in thinking and customer experience can be difficult after many years of internal process optimization to reduce costs and minimize inventory. Analytics can play a role in gaining a better understanding of where we are experiencing difficulties, and disappointing customers
Changes in sourcing agreements to improve supply stability and demand forecasting – Supply chain is a team sport. It is only by working with our partner suppliers that we can improve resiliency. Moves toward more flexible agreements that allow a range of order actions across multiple categories based on demand and availability will help make supply chains less brittle and restrictive. Partner data about tier 2 and tier 3 suppliers can help us improve our planning models to incorporate uncertainty in geopolitical, climate, logistic, and pandemic dimensions. Utilizing better, more detailed data about suppliers may be one of the most important changes we can make in improving the resilience of our planning optimization models. This is also essential data if we hope to utilize machine learning and auto ML in our planning models.
Changes in logistics planning embracing flexibility and local supply – One of the biggest changes we will see in supply chains this fall is a desire to move toward more local sources of supply. Geographical complexities driven by lockdowns, limited global shipping capacity, and geopolitical instability are causing the pendulum to swing back toward more local sources of supply.
Changes in supply and demand data requirements and digital twins – Real improvements in supply chain performance require more real time data. Real time data from customers, suppliers, distributors and logistic suppliers needs to be integrated to provide a real time view of the end-to-end process of meeting customer needs. Increasingly, supply chain software providers are turning to digital twin and digital thread data models to help provide this visibility. Advanced analytics and machine learning algorithms are ideally suited to identify and resolve issues when provided with this type of operating framework. Preparing for uncertainty and creating resilience should be a focus of every supply chain organization as we move into the next wave of pandemic uncertainty. Prepared organizations will experience much higher levels of customer satisfaction, and will experience better business outcomes and performance.
Scott Lundstrom is an analyst focused on the intersection of AI, IoT and Supply Chains. See his blog at Supply Chain Futures.
Top 10 Big Data trends of 2020
During the last few decades, Big Data has become an insightful idea in all the significant technical terms. Additionally, the accessibility of wireless connections and different advances have facilitated the analysis of large data sets. Organizations and huge companies are picking up strength consistently by improving their data analytics and platforms.
2019 was a major year over the big data landscape. In the wake of beginning the year with the Cloudera and Hortonworks merger, we’ve seen huge upticks in Big Data use across the world, with organizations running to embrace the significance of data operations and orchestration to their business success. The big data industry is presently worth $189 Billion, an expansion of $20 Billion more than 2018, and is set to proceed with its rapid growth and reach $247 Billion by 2022.
It’s the ideal opportunity for us to look at Big Data trends for 2020.
Chief Data Officers (CDOs) will be the Center of Attraction
The positions of Data Scientists and Chief Data Officers (CDOs) are modestly new, anyway, the prerequisite for these experts on the work is currently high. As the volume of data continues developing, the requirement for data professionals additionally arrives at a specific limit of business requirements.
CDO is a C-level authority at risk for data availability, integrity, and security in a company. As more businessmen comprehend the noteworthiness of this job, enlisting a CDO is transforming into the norm. The prerequisite for these experts will stay to be in big data trends for quite a long time.
Investment in Big Data Analytics
Analytics gives an upper hand to organizations. Gartner is foreseeing that organizations that aren’t putting intensely in analytics by the end of 2020 may not be ready to go in 2021. (It is expected that private ventures, for example, self-employed handymen, gardeners, and many artists, are excluded from this forecast.)
The real-time speech analytics market has seen its previously sustained adoption cycle beginning in 2019. The idea of customer journey analytics is anticipated to grow consistently, with the objective of improving enterprise productivity and the client experience. Real-time speech analytics and customer journey analytics will increase its popularity in 2020.
Multi-cloud and Hybrid are Setting Deep Roots
As cloud-based advances keep on developing, organizations are progressively liable to want a spot in the cloud. Notwithstanding, the process of moving your data integration and preparation from an on-premises solution to the cloud is more confounded and tedious than most care to concede. Additionally, to relocate huge amounts of existing data, organizations should match up to their data sources and platforms for a little while to months before the shift is complete.
In 2020, we hope to see later adopters arrive at a conclusion of having multi-cloud deployment, bringing the hybrid and multi-cloud philosophy to the front line of data ecosystem strategies.
Actionable Data will Grow
Another development concerning big data trends 2020 recognized to be actionable data for faster processing. This data indicates the missing connection between business prepositions and big data. As it was referred before, big data in itself is futile without assessment since it is unreasonably stunning, multi-organized, and voluminous. As opposed to big data patterns, ordinarily relying upon Hadoop and NoSQL databases to look at data in the clump mode, speedy data mulls over planning continuous streams.
Because of this data stream handling, data can be separated immediately, within a brief period in only a single millisecond. This conveys more value to companies that can make business decisions and start processes all the more immediately when data is cleaned up.
Continuous Intelligence is a framework that has integrated real-time analytics with business operations. It measures recorded and current data to give decision-making automation or decision-making support. Continuous intelligence uses several technologies such as optimization, business rule management, event stream processing, augmented analytics, and machine learning. It suggests activities dependent on both historical and real-time data.
Gartner predicts more than 50% of new business systems will utilize continuous intelligence by 2022. This move has begun, and numerous companies will fuse continuous intelligence during 2020 to pick up or keep up a serious edge.
Machine Learning will Continue to be in Focus
Being a significant innovation in big data trends 2020, machine learning (ML) is another development expected to affect our future fundamentally. ML is a rapidly developing advancement that used to expand regular activities and business processes
ML projects have gotten the most investments in 2019, stood out from all other AI systems joined. Automated ML tools help in making pieces of knowledge that would be difficult to separate by various methods, even by expert analysts. This big data innovation stack gives faster results and lifts both general productivity and response times.
Abandon Hadoop for Spark and Databricks
Since showing up in the market, Hadoop has been criticized by numerous individuals in the network for its multifaceted nature. Spark and managed Spark solutions like Databricks are the “new and glossy” player and have accordingly been picking up a foothold as data science workers consider them to be as an answer to all that they disdain about Hadoop.
However, running a Spark or Databricks work in data science sandbox and then promoting it into full production will keep on facing challenges. Data engineers will keep on requiring more fit and finish for Spark with regards to enterprise-class data operations and orchestration. Most importantly there are a ton of options to consider between the two platforms, and companies will benefit themselves from that decision for favored abilities and economic worth.
In-memory computing has the additional advantage of helping business clients (counting banks, retailers, and utilities) to identify patterns rapidly and break down huge amounts of data without any problem. The dropping of costs for memory is a major factor in the growing enthusiasm for in-memory computing innovation.
In-memory innovation is utilized to perform complex data analyses in real time. It permits its clients to work with huge data sets with a lot more prominent agility. In 2020, in-memory computing will pick up fame because of the decreases in expenses of memory.
IoT and Big Data
There are such enormous numbers of advancements that expect to change the current business situations in 2020. It is hard to be aware of all that, however, IoT and digital gadgets are required to get a balance in big data trends 2020.
The function of IoT in healthcare can be seen today, likewise, the innovation joining with gig data is pushing companies to get better outcomes. It is expected that 42% of companies that have IoT solutions in progress or IoT creation in progress are expecting to use digitized portables within the following three years.
Digital Transformation Will Be a Key Component
Digital transformation goes together with the Internet of Things (IoT), artificial intelligence (AI), machine learning and big data. With IoT connected devices expected to arrive at a stunning 75 billion devices in 2025 from 26.7 billion presently, it’s easy to see where that big data is originating from. Digital transformation as IoT, IaaS, AI and machine learning is taking care of big data and pushing it to regions inconceivable in mankind’s history.
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