You’d think that the 8086 microprocessor, a 40-year-old chip with a mere 29,000 transistors on board that kicked off the 16-bit PC revolution, would have no more tales left to tell. But as [Ken Shirriff] discovered, reverse engineering the chip from die photos reveals some hidden depths.
The focus of [Ken]’s exploration of the venerable chip is the charge pump, a circuit that he explains was used to provide a bias voltage across the substrate of the chip. Early chips generally took this -5 volt bias voltage from a pin, which meant designers had to provide a bipolar power supply. To reduce the engineering effort needed to incorporate the 8086 into designs, Intel opted for an on-board charge pump to generate the bias voltage. The circuit consists of a ring oscillator made from a trio of inverters, a pair of transistors, and some diodes to act as check valves. By alternately charging a capacitor and switching its polarity relative to the substrate, the needed -5 volt bias is created.
Given the circuit required, it was pretty easy for [Ken] to locate it on the die. The charge pump takes up a relatively huge amount of die space, which speaks to the engineering decisions Intel made when deciding to include it. [Ken] drills down to a very low level on the circuit, with fascinating details on how the MOSFETs were constructed, and why eight transistors were used instead of two diodes. As usual, his die photos are top quality, as are his explanations of what’s going on down inside the silicon.
If you’re somehow just stumbling upon [Ken]’s body of work, you’re in for a real treat. To get you started, you’ll want to check out how he found pi baked into the silicon of the 8087 coprocessor, or perhaps his die-level exploration of different Game Boy audio chips.
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.
Sberbank’s Pay with QR and Take&Go offerings recognized among world’s best
Sberbank has won Future Digital Awards in two categories, securing gold accolades for the innovativeness and user-friendliness of its Pay with QR (Best QR Code Payment Offering) and Take&Go (Best In-Store Technology) services.
Pay with QR is an innovative cashless method of accepting payments for products or services, which is available to Sberbank customers, as well as to clients of other banks who use Sberbank’s QR Platform. To make a payment, you only need to show your personal QR code generated by the mobile application or scan the code posted at the outlet. For businesses, this type of payment is much more affordable than merchant acquiring, with the fee being only 0.6% to 1.5%. On top of that, outlets don’t need any special equipment to use the service.
Take&Go is a revolutionary computer vision technology to make purchases at stores. The first store without checkouts in Russia was opened at Azbuka Vkusa. To make a purchase, you just need to scan a QR code using the Take&Go mobile app, take the groceries you need off the shelf and leave. The money will be debitedautomatically.
Dmitry Malykh, Acquiring and Bank Cards Director, Sberbank:
“Sberbank constantly develops and introduces innovative payment solutions that become an integral part of our customers’ lives over time. They pay for their purchases every day in the most convenient and secure ways. The Pay with QR and Take&Go offerings are already very popular among our individual customers while opening up new opportunities for businesses of any size. The international experts’ recognition once again proves that we are moving in the right direction.”
Future Digital Awards is an international award, founded in 2008. Its participants are companies that operate in fintech and payment, digital service provision, technology and innovation such as smart devices, cities and houses, IoT, etc. Juniper Research analytical group assesses applications, draws up short lists of potential winners based on criteria such as product innovation, its peculiar features and advantages, product partnerships, certification and compliance, and future business outlooks. Amazon, Visa, Google, Microsoft, Apple, Mastercard, IBM and other globally recognized tech companies won the award in 2016–2019.
How Artificial Intelligence is Changing the eLearning Environment
Artificial intelligence (AI) is everywhere these days, making inanimate objects increasingly smart. It’s designed by humans for humans, to enrich and facilitate our everyday lives. As a matter of fact, AI is now the brain behind your smartphone, car, music streaming service, banking app, freezer, and travel agency. It’s not only omnipresent, it’s omniscient as well.
If the main keyword for AI is “smart”, then how come we don’t talk more about this burgeoning technology in the context of knowledge, learning, and education? AI has everything we need to revolutionize the industry, enrich and facilitate the learning experience of students and adult learners, and boost the knowledge retention rates across the board.
It’s a whole new chapter in the history of the classroom. The change has already begun, with AI reshaping eLearning as we know it. Find out how.
AI Makes Personalized Learning a Child’s Play
Every student is different. Educators have known this for a long time, but it wasn’t until LMS software solutions that they were able to actually accommodate the various needs of different types of learners.
The introduction of technology to the traditional classroom established a framework for blended learning, which is now the dominant model for educating modern-day students. Essentially, blended learning allows students to choose how they access educational content, and how they acquire knowledge in terms of learning styles and speed. But AI brings personalization to a whole new level.
Unlike teachers, artificial intelligence can recognize students’ learning needs, interests, preferences, habits, and capacities automatically and in real-time, by analyzing their performance. This powerful insight helps create highly personalized learning environments and paths where every aspect – from content type to delivery method – is adjusted to the students’ individual needs.
The result? Optimum performance of every learner.
AI is at Learners’ Disposal Anytime, Anywhere
In combination with personalized learning paths, unlimited connectivity makes it possible for students to access educational content from anywhere and at any given moment.
Adaptive learning environments enable and encourage self-paced learning, thus allowing students to make the best out of their peak productivity hours. But what happens if one learner’s productivity peaks in the middle of the night, when there’s no one around to help solve a dilemma or address points of confusion?
Since blended learning is a combination of instructor-led training and online courses, tutors should remain available 24/7, whenever students may need their guidance. This may not be possible with human tutors, but it is with digital assistants.
Thanks to machine learning and natural language processing, AI assistants can learn to provide necessary guidance to students, answer their questions, and solve their problems. And since machines never sleep, this digital mentorship is available 24/7.
Digital Mentorship Can Encourage Engagement
Usually, AI assistants work with students individually. Extending the concept of student-centered education, smart technology caters not only to different learning styles but also to different personalities and temperaments. This is significant to introverted students, who usually don’t feel comfortable expressing themselves in front of their peers in the traditional classroom environment.
Faceless AI is a saving grace for such learners as it allows them to voice their dilemmas without the fear of being judged, while the eLearning environment itself encourages them to participate in social learning. Of course, AI has the potential to motivate and engage all students, be they introverts or extroverts, visual learners or auditory learners.
Higher engagement is a natural consequence of personalization – by facilitating the learning path, AI makes knowledge acquisition easier, thus eliminating all frustration from the process. All students thrive in such a learning and development (L&D) environment when they are motivated by their own achievements.
AI Allows Continual Real-Time Improvement
Thanks to cutting-edge software technology and authoring tools, everyone can learn to create an online course that encourages engagement and equips students for success.
It’s maintaining this eLearning environment that’s hard, especially in today’s fast-paced world. Technology is a powerful knowledge acquisition tool – the more potent it becomes, the more we understand about the world we live in. The human race is learning on a daily basis.
The road from tacit to explicit knowledge was a long one in the pre-digital age. At times, students had to wait years before new findings were added to their old textbooks. Today, we have the Internet – the biggest and fastest knowledge base in history.
With AI, we can now comb through this knowledge base in matters of minutes, update our online courses with cutting-edge information in real time, and keep educational content relevant. Having said that, up-to-date content is not enough to ensure a rich learning experience. Educators and online course creators must learn from their students as there are always challenges that need to be addressed. Because of this, student feedback is precious.
Real-Life Examples of AI in Learning
Let’s now see a few companies who are working behind the scenes to bring the benefits of AI-powered technologies to people.
Massachusetts-based software technology company, Nuance builds speech recognition software. The technology can transcribe a maximum of 160 words per minute. It is useful especially to learners with mobility challenges, including writing. Instructors too can rely on it to dictate classroom lectures and expedite repetitive tasks such as emails.
As a provider of quality math education, Carnegie Learning brings together artificial intelligence and machine learning to its platform. It puts emphasis on personalized learning experiences to turn learners into critical thinkers, creative problem solvers, and lifelong learners. By combining cognitive and learning science with research and practical instructions, CL helps students gain a deeper understanding of what they are learning.
Century is a provider of an AI-based teaching and learning platform located in London. It provides adaptive learning solutions for learners according to their strengths and weaknesses, while helping instructors simplify their routine tasks such as grading. For this, it combines AI, neuroscience, and data analytics.
Artificial intelligence is already making waves in the eLearning industry by unlocking exciting new opportunities for educators and students alike. But deep personalization, 24/7 availability, increased engagement, and real-time improvement are only a fragment of what AI may mean for the future of education. Experts predict that the use of this smart technology will grow by 47.5% in the next two years. We can only imagine the possibilities.
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