Technologies to transform business operations during the pandemic (Part 2)

Besides 5G, IoT and automation, key technologies such as AI and data alternation will play a crucial role in business operations.

Within a short period, companies have embraced remote working for their workers, pushing organizational improvements that would have taken years to prepare and carry out. This changed the attention of the company to the importance of adopting technologies in its operations across industries.

The growth of the open data movement and the realization that public sharing of data provides social and economic benefits might contribute to the opening up of many of these knowledge sources for corporations to innovate. Data exchanges like Terbine and Dawex are building the infrastructure required to inject liquidity into this evolving market.

key technologies such as AI and data alternation will play a crucial role in business operations (Photo: Neuromation)

AI and alternative data

Small enterprises have been lagging behind in harnessing the economic advantages of AI due to the shortage of sufficient data inputs for their algorithms. Only big companies were able to leverage these technologies since they had internal databases resulting from their activities or could afford to purchase data as needed. However, data ownership is not necessarily meant to create data-driven goods and services, as some creative uses of data indicate.

A common approach to the use of AI is data synthesis when developers search for rich datasets to train their programs. Continental, a car component supplier, is estimated to create an AI-based program that can produce 5,000 miles of vehicle test data per hour. Since collecting data with an actual vehicle would have taken as long as 20 days, Intel Labs and Darmstadt University in Germany have utilized the video game Grand Theft Auto to do such thing, helping help train autonomous vehicles.

To evaluate the effects of COVID-19 on the economy, specifically decide which stocks to purchase or sell, many major banks have been trying new approaches regarding data sources. For example, it is better to buy and leverage travel websites, insurance companies and job sites than those from the monthly government statistics and companies’ financial reports. 

In response to challenges posed by the current lockdowns, companies are realizing the vital role of evaluating and predicting the future of the economy post COVID. Enterprises now are putting more time and effort into utilizing and assessing the data to make better predictions as businesses will not be back to the normal before the pandemic. 

Source: CIO ASEAN

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Technologies to transform business operations during the pandemic (Part 1)

The pandemic is pushing organizations to drive innovations in their existing operations in response to the impact of COVID-19.

Drive to automation

The adoption of robots in factories and warehouse has long been a norm in the industry, only now do the organizations realize the crucial role of automation in transforming their operation during COVID-19. However, the need for social distance and safe workspaces has contributed to a major rise in the market for robotic systems and helpers. Firms like Brain Corp, Xenex and Simbe Robotics are hiring extra personnel to create and manage robotic stores and hospitals that now need to sanitize buildings and supermarket shelves.

As these systems help keep employees distant from each other, they also produce data that is used to streamline workflows and business planning. For instants, Simbe's Tally robots gather real-time data on where items are stored on shelves, helping consumers find what they're searching for less amount of time. Not only do these robotic solutions help boost customer experience, but they also keep on collecting new data, minimize operational costs.

Technologies to transform business operations (Photo: Raconteur)

5G and the IoT

The emerging preference towards remote work is expected to accelerate the introduction of 5G networks and facilities as soon as immediate restrictions on travel are removed. The importance of high speed and low latency connectivity has been evident to home staff who are dealing with their difficulties in video conferencing and remote communication. 2.6 billion 5G subscriptions by 2025 and up from 13 million at the end of 2019 are expected, according to Ericsson.  This will encourage businesses to deliver more immersive consumer service across VR and AR in terms of creativity and product growth. Across B2B and B2C verticals, the data created by these value-added technologies can expand consumer interactions as user desires and actions are recorded in real-time and returned to optimized offerings.

IoT initiatives such as remote data collection on environmental conditions in buildings and communities are becoming increasingly crucial as policymakers and decision-makers pursue information on the activity and actions of people under lockdown.

Source: CIO ASEAN

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Review cybersecurity threats and solutions in 2020 (Part 2)

The past decade has seen the proliferation of technology providing many advantages, but also with distinct disadvantages. This part will mention some of the solutions helping your organization defense before cyberattacks.

As threats change over time, security solutions change too. These tools enable security teams to adapt to new attack methods and tools and ideally enable organizations to identify vulnerabilities before attackers can exploit them.

Endpoint Management

As an organization’s systems grow, the expansion of endpoints often occurs in the following categories: cloud solutions, bring your own device (BYOD) policies, Internet of things (IoT) devices. These things mentioned above presents a possibly vulnerable endpoint.

XDR is a trending solution that can help with endpoint management. It provides proactive protection for threat detection and response and offers visibility across networks, endpoints, and clouds. This will increase the productivity of security teams and track threats throughout the organization.

The Growth of Alternative Authentication

Privacy and password strength is a security concern that has persisted since the beginning of the Internet. However, another authentication method is beginning to replace passwords —  alternative or passwordless authentication. 

Alternative authentication solutions, such as hardware tokens, knowledge-based authentication, biometric authentication, and unique password generators can offer a more secure option. It is predicted that by 2022 90% of midsize businesses will adopt passwordless authentication for more than 50% of their use cases.

Companies need to begin investing more to improve in-house cybersecurity expertise. (Photo: 10BestOnline)

Cyber Insurance

The total losses associated with cyberattacks and extortion were estimated at around $11 million in 2019. Due to this, it’s not surprising that organizations are looking for damage reduction measures and buying insurance. 

Despite the need for coverage, insurance providers have struggled to adapt to the changing cyber risk landscape. This includes the increasingly important role that IoT is playing and the emergence of more connected devices. This lag creates a lot of opportunity for new insurance models and coverage. 

Cloud-Based Security Expansion

As the world moves a large amount of technology infrastructure to the cloud, cloud-based security platforms and services continue to grow. Cloud-based security can offer several advantages over traditional approaches, including economies of scale, reduced costs, better protection performance, increased threat intelligence, faster compliance with industry, and government standards.

Advances in Encryption

As cyberattacks have become more sophisticated, numerous advances in data encryption have followed suit, however, a particular encryption strategy is no longer effective. To address this security gap, many advancements in encryption are emerging. These include distributed ledgers, zero-knowledge technologies, ring signing, and privacy technologies. When used in combination, these technologies can provide complete or partial data anonymity and identity and data verification can be automated.

In 2020, cybersecurity trends are turning into a necessity for business continuity, as organizations face attacks from a staggering number of directions.

Source: Dzone

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3 types of AI: a guide to narrow, general, and super artificial intelligence (part 2)

Part 2 of this article will cover the other two types of Artificial Intelligence - general AI and artificial superintelligence.

AI grows to be so close to human thoughts and perceptions. (Photo: Apro Software)

Artificial General Intelligence (AGI) / Strong AI / Deep AI

The idea of Artificial general intelligence (AGI), also known as strong AI or deep AI, is the system with general intelligence that simulates human intellect and actions. Under particular situations, AGI can think, comprehend and behave in a manner that is indistinguishable from that of a human being.

Finding a way to make machines conscious and programming a full set of cognitive abilities are the main barriers preventing scientists from achieving Deep AI. Apart from enhancing performance on singular tasks, machines will have to push experiential learning to the next level to apply the knowledge to a broader variety of challenges. Though knowing Deep AI follows the theory of mind AI framework training machines to understand humans, the lack of knowledge on the functionality of the human brain has refrained researchers from doing so. 

It is more likely to see an improvement in the ability to learn of a machine than achieve a complete Artificial General Intelligence (AGI). For example, the effort to achieve AGI of one of the fastest supercomputers, Fujitsu-built K, has failed to yield any significant results given that it took 40 minutes to replicate a single second of neural activity.

Artificial Superintelligence (ASI)

Artificial Super-Intelligence (ASI) is a conceptual AI that not only mimics or recognizes human intellect and behavior but also becomes self-conscious and transcends human intelligence and capacity. 

In ASI, artificial intelligence grows to be so close to human thoughts and perceptions that it not only recognizes them but evokes feelings, wishes, values and desires of its own. besides the ability to emulate everything human beings can do including math, science, sports, art, medicine, hobbies, emotional relationships, ASI will have a better memory and quicker capacity to interpret and evaluate data and stimuli. 

Source: Codebots

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3 types of AI: a guide to narrow, general, and super artificial intelligence (part 1)

Part 1 of this article will go through a brief overview of Artificial Intelligence and the first type of AI.

Narrow or weak AI, general or strong AI and artificial superintelligence are the three types of artificial intelligence (AI).

Right now, we have only achieved narrow AI. As machine learning capabilities continue to develop, and scientists are getting closer to achieve general AI in the near future.

3 types of AI: a guide to narrow, general, and super artificial intelligence (Photo: accilium)

Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI

The only successful form of AI that has been implemented today is Artificial narrow intelligence (ANI), also known as weak AI or narrow AI. Though Narrow AI is excellent at completing specific tasks such as facial recognition, speech recognition/voice assistants, driving a car, or searching the internet, it is meant to perform singular tasks.

Intelligent as these machines appear, they only simulate simple human behaviors and function under a limited range of constraints. The fact that Narrow AI does not imitate or replicate human intelligence and work on a restricted set of parameters and contexts makes them a weak type of AI. The applications of this type can be seen at the speech and language recognition of the Siri on iPhones,  vision recognition of self-driving cars, and recommendation engines that make suggestions based on purchase history.

In the last decade, Narrow AI has undergone several breakthroughs, propelled by advancements in machine learning and deep learning. AI systems are now used in medicine to diagnose cancer and other diseases with a high degree of precision by emulating human cognition and reasoning.

Natural language processing (NLP) emerges in the Narrow AI's machine intelligence which recognizes speech and text in chatbots. Hence, AI is designed to communicate with humans in a natural, personalized way.

Narrow AI consists of reactive AI which has no memory or data storage to simulate human responses to things and limited memory AI which can use historical data to make decisions with equipped learning capabilities. The vast majority of AI nowadays is limited memory that utilizes deep learning technology to personalize users’ experience.

Source: Codebots

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Software prevents accidents from autonomous vehicles (Part 1)

Before participating in road traffic, autonomous vehicles must demonstrate that they do not harm the others on the road. New software prevents accidents by predicting different variants of a traffic situation every millisecond. One of these methodologies called algorithm is mentioned in part 1. 

A vehicle moves toward a convergence. Another vehicle streams out of the go across the road; however, it isn't yet certain whether it will turn right or left. Simultaneously, a passerby ventures into the path legitimately before the vehicle, and there is a cyclist on the opposite side of the road. Individuals with street traffic experience will when all is said in the done survey, the developments of other traffic members virtually.

"These sorts of circumstances present a gigantic test for self-ruling vehicles constrained by PC programs," clarifies Matthias Althoff, Professor of Cyber-Physical Systems at TUM. "However, independent driving will possibly pick up the acknowledgement of the overall population on the off chance that you can guarantee that the vehicles won't jeopardize other street clients - regardless of how confounding the traffic circumstance."

Using software to prevent accidents from autonomous vehicles (Photo: IIoT World)

Algorithms that peer into the future

A definitive objective when creating programming for self-governing vehicles is to guarantee that they won't cause mishaps. Althoff, who is an individual from the Munich School of Robotics and Machine Intelligence, and his group have now built up a product module that for all time investigates and predicts occasions while driving. Vehicle sensor information is recorded and assessed each millisecond. The product can compute all potential developments for each traffic member - if they hold fast to the street traffic guidelines - permitting the framework to look three to six seconds into what's to come.

In light of these future situations, the framework decides an assortment of development choices for the vehicle. Simultaneously, the program ascertains potential crisis moves in which the vehicle can be moved out of damage's way by quickening or slowing down without imperiling others. The self-ruling vehicle may follow courses that are liberated from predictable impacts and for which a crisis move alternative has been recognized.

Source: Science Daily.

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Review cybersecurity threats and solutions in 2020 (Part 1)

Part 1 of this article will present a brief overview of critical cybersecurity threats to watch out for during 2020. 

To keep your systems and data secure, you need first to understand what the most common threats are as they continue developing to exploit vulnerabilities in security protections. 

Insecure Mobile Devices

Adding company emails to your smartphone and connecting from public Wi-Fi networks can make sensitive data available to everyone. Besides, the use of instant messaging services and file-sharing can enable criminals to easily compromise smartphones, granting access to credentials and data. Therefore, protecting mobile devices has become one of the main concerns in cybersecurity. 

Lack of Device Controls and Shared Logins

When employees leave their desks or workstations, access needs to be restricted with passwords or other authentication measures. Systems should lock automatically after a certain period of activity if users don’t log out. This prevents others from using the device while the employee is away. 

The proliferation of technology provides not only many advantages, but also with distinct disadvantages. 

(Photo: Channel Futures)

Mismanagement of Passwords and Permissions

Permissions for access to certain documents or for downloading programs should not be given to everyone or used without restrictions. Companies need to establish access control mechanisms for employees and administrators. Security responsibility is shared between employers and employees.

State-Sponsored Attacks

Modern cybercrime goes beyond criminals seeking to make a profit through the theft or ransom of individual and corporate data. Many of these attacks target government infrastructure and systems, but private organizations are also vulnerable. Specifically, organizations in business sectors related to geopolitical disputes are especially at risk. 

Advances in Computing Threaten Cryptography

Since quantum can perform significantly more powerful computations than current machines, it creates a threat, allowing attackers to decipher cryptographic codes that would take traditional computers far longer to crack. 

To be continued

Source Dzone 

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Data Visualization trends in 2021

Analysts at research and advisory firm Gartner forecast that, by 2025, data stories (which are explained using visuals) will be the most common way of consuming analytics.

Data visualization (DataViz) has gained its popularity. During the current global health crisis, data visualization tools have provided visuals of the new infections, fatalities, recoveries, and more. Besides, DataViz enables users to visualize large amounts of complex data, helping businesses in their decision-making and forecast future trends and patterns.

Data Visualization Through AR/VR

Dataviz in Augmented Reality (AR) and Virtual Reality (VR) might be the next major use case, with salient examples of 3D data visualizations, which supports building models, developing games, and virtual events, indicating big changes in the way we interact with data.

Experts anticipate the market size of augmented VR to be worth around US$209bn globally by 2022. It’s worth pointing out that VR enables us to touch information as if it’s real, resulting in accurate data analysis and faster decisions.

Data Storytelling Here to Stay

For long, publishers have recognized the power of data in storytelling. Two major US publications will testify to that. Their journalistic stories, enhanced by data visualizations, went viral and are the most-viewed and most-popular articles on their websites. One is an illustration of how social distancing helps reduce the spreading of infection. The other visualizes the fatality rate in Latin America, North America, Europe, Asia, and other geographical regions.  

A Wharton School of Business study finds that effective use of data visualizations shortens meetings by 24% (Photo: Nordic Coder)

Smart Devices

Smart traffic systems, for example, help foresee traffic bottlenecks a few hours before the commute. Also, they can forecast public transport shortages while guiding commuters to handle possible congestions in their routes. AR in autonomous vehicles would improve safety by overlaying key information for the driver, making them aware of possible risks in their routes, providing emergency alerts. With the rising users’ expectation of the effectiveness of data, DataViz will help detect abnormalities, trends, and spikes in data.

Real-Time Visualization

Visualizing data in real-time accelerates the entire decision-making process for both authorities and enterprises. The Johns Hopkins University dashboard, which has visualized the spread of the ongoing pandemic across the world, is a popular example of the impact of real-time visualization. Accessing real-time data has helped officials identify the emerging hotspots and utilize the available resources to reduce the spread of the virus.

Data visualization helps organize data into a comprehensible format and convey it as a story that highlights essential information. With the volume of data increasing rapidly, visualizations are essential to understanding the trillions of rows of data being generated daily.

Source: DZone

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The brain-inspired computing system could decrease the carbon footprint of Artificial Intelligence

Extremely energy-efficient artificial intelligence is now closer to reality after a study by UCL researchers found a way to improve the accuracy of a brain-inspired computing system.

Brain-inspired electronic system (Photo: Sci Tech Daily)

The system creating artificial neural networks by memristors is said to be at least 1,000 times more energy-efficient than conventional transistor-based AI hardware. However, it still has been more prone to error until now.

Existing AI is extremely energy-intensive due to the fact that training one AI model can emerge 284 tonnes of carbon dioxide, equivalent to the lifetime emissions of five cars. Supplanting the semiconductors that make up all advanced gadgets with memristors, a novel electronic gadget previously underlying 2008, could decrease this to a small amount of a huge amount of carbon dioxide which is equal to emanations produced in an afternoon’s drive.

Since memristors are a lot more vitality effective than existing figuring frameworks, they can conceivably pack colossal measures of registering power into hand-held gadgets, eliminating the need to be associated with the Internet.

This is particularly significant as over-dependence on the Internet is relied upon to get dangerous in future due to ever-expanding information requests and the troubles of expanding information bandwidth past a specific point.

In the new investigation, distributed in Nature Correspondences, engineers at UCL found that exactness could be enormously improved by getting memristors to cooperate in a few sub-gatherings of neural systems and averaging their figurines, implying that imperfections in every one of the systems could be counteracted.

Memristors, portrayed as "resistors with memory," as they recollect the measure of electric charge that moved through them even subsequent to being killed, were viewed as progressive when they were first worked longer than 10 years back, a "missing connection" in hardware to enhance the resistor, capacitor and inductor. They have since been made economically in memory gadgets, however the examination group say they could be utilized to create man-made intelligence frameworks inside the following three years.

Memristors offer immensely improved effectiveness since they work not simply in a paired code of ones and zeros, however at numerous levels somewhere in the range of zero and one simultaneously, which means more data can be stuffed into each piece.

Besides, memristors are regularly portrayed as a neuromorphic (mind propelled) type of registering in light of the fact that, as in the cerebrum, handling and memory are executed in a similar versatile structure obstructs, rather than current PC frameworks that squander a great deal of vitality in information development.

In the investigation, Dr Adnan Mehonic, PhD understudy Dovydas Joksas (both UCL Electronic and Electrical Designing), and associates from the UK and the US tried the new methodology in a few distinct sorts of memristors and found that it improved the precision of every one of them, paying little mind to material or specific memristor innovation. It likewise worked for various issues that may influence memristors' exactness.

Specialists found that their methodology expanded the exactness of the neural systems for run of the mill man-made intelligence assignments to a tantamount level to programming devices run on traditional advanced equipment.

Dr Mehonic, head of the investigation, stated: "We trusted that there may be more conventional methodologies that improve not the gadget level, however the framework level conducts, and we accept we discovered one. Our methodology shows that, with regards to memristors, a few heads are superior to one. Orchestrating the neural system into a few littler systems as opposed to one major system prompted more noteworthy exactness in general."

Dovydas Joksas further clarified that they acquired a well known procedure from software engineering and applied it with regards to memristors. Furthermore, it worked! Utilizing starter reenactments, they found that even basic averaging could essentially build the precision of memristive neural systems.

Educator Tony Kenyon (UCL Electronic and Electrical Designing), a co-creator on the examination, included: "We accept right now is an ideal opportunity for memristors, on which we have been laboring for quite a while, to play a main part in a more vitality feasible time of IoT gadgets and edge figuring."

Source: Science Daily.

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How to make AI trustworthy

One of the biggest bottlenecks when adopting new technologies is trust in AI. Now, a new tool generates automatic indicators if data and predictions generated by AI algorithms are trustworthy.

Neural networks are a type of artificial intelligence that is modeled after the brain and generate predictions. But how we can tell these predictions are trusted? One of the key barriers to the adoption of self-driving cars is that the vehicles need to act as independent decision-makers in the road. Can we trust the computer software within the vehicles to make sound decisions within fractions of a second - especially when conflicting information is coming from computer vision from cameras or data from lidar? Even humans can be indecisive in certain cases involving conflicting information.

Knowing which systems to trust and is most accurate would be helpful to determine what decisions 

the autopilot should make (Photo: aitrends)

A new tool developed by USC Viterbi Engineering researchers named DeepTrust can quantify the amount of uncertainty, should human intervention is necessary. It took the research team almost two years to employ what is known as subjective logic to assess the architecture of the neural networks. On one of their test cases, DeepTrust found that the prediction pointing towards Clinton winning in 2016  Presidential election had a greater margin for error.

The other significance of this research is that it provides insights on how to test the reliability of AI algorithms that are normally trained on thousands to millions of data points. In other way, how to make sure the architecture of these neural network systems has greater accuracy. This work could also serve as a guidepost as to how much "noise" can be in testing samples if computer scientists want to maximize accuracy and trust.

This model is the first of its kind. To our knowledge, there is no trust quantification model or tool for deep learning, artificial intelligence, and machine learning. DeepTrust has the potential to make AI aware and adaptive.

Source: ScienceDaily

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