curious case of AI in the cloud

Data is not the new oil as some have claimed, and Machine Learning is not the new electricity. The shift is so gigantic that itâ??s impossible to come up with a fair analogy

We're living through the most significant technological shift in human history. Our smartphones are connected to thousands of computers and access terabytes of data, and whether we realize it or not, our lives are heavily impacted by algorithms- our news feeds, the products we buy, our food, our transportation, etc.

The shift is significant, not so much because we have access to unlimited computational power and data, but rather, because a larger number of things is now measurable.

The confluence of unlimited computational power and unlimited data, in conjunction with continuous advances in algorithms and hardware, mean that Machine Learning (ML) is the driving force of this major technological shift - and that is significant because computers will play an increasingly important role in our decision-making and ML in how the technology works.

On one hand, measuring at scale allows Machine Learning algorithms to make better predictions which help individuals and businesses make decisions. On the other hand, it feeds the improvement of algorithms that perform tasks that are better suited to computers.

Given the quickly evolving landscape, how can developers and businesses, old and new, best capitalize on this shift to have an impact and stay ahead of the game?

First, it's important to separate hype from reality. We won't have human-like robots walking our streets and performing the very hard jobs that require "soft skills" anytime soon, but AI and Machine Learning are here to stay and they are the new reality of computing.

Data is not the new oil as some have claimed, and Machine Learning is not the new electricity. The shift is so gigantic that it's impossible to come up with a fair analogy. What is clear, is that every business in the very near future will use ML and every developer will work on ML as part of a standard set of computing tools. That's a reality today for those that have embraced it.

Second, understanding the ecosystem is critical. The Cloud plays a significant role because it gives developers and businesses access and flexibility in storing as much data as needed, and in instantly scaling as needed, at low prices - size doesn't matter.

Individual developers, small teams, mid-size, and large enterprises can all leverage the Cloud and AI. And it also means that consumers, in many verticals, will continue to expect competitive performance and free or near free functionalities. So, we have the technology (cloud, data), businesses (every business becomes a tech business), and consumers (the "end point" for collection of data and impact).

Third, identifying internal opportunities and modifying processes is pivotal in driving that shift. For developers, it means getting up to speed on ML and understanding the subtleties of what different algorithms offer. For medium-sized companies, that's having a strategy to collect the right data, and execute on it, step by step. For large companies, it's ensuring that data pipelines and processes align with experimentation and leverage AI. In all cases, it's having clarity on what needs to be measured so that the right metrics are in place.

It's also important to avoid pitfalls, and there are many. On one extreme sit the skeptics that think AI does not apply to them because they're very far from being able to apply it. This is usually a misguided notion, as the Cloud and many open source tools allow fairly quick starts. Additionally, in most cases, data is already in existence in one form or another. On the other extreme sit the dreamers who believe that AI and the Cloud will magically solve everything.

In practice, the biggest challenge most companies face is not having the right expertise. For developers, learning ML has become increasingly accessible, but a common issue is a disconnect between those with the technical skills and those with the business experience. The reality is that no matter what stage you are in - as a developer, or as a company - the time to embrace AI is now.

AI can be applied anywhere where you can collect data, measure, and make predictions. Identify those opportunities and tie them to a specific customer or business needs and start ensuring the quality of the data is good and apply basic methods to start with as a proof of concept. This includes asking the right questions. Then, look to the Cloud because it gives you the flexibility to easily, quickly, and cheaply try things out, and identify the right open source and learning resources available.

In this process, keep in mind what three factors above: separate hype from reality and set reasonable expectations; have a clear understanding of the technology (cloud, data), the business, and the "customers" for the task, and have clarity on what is to be measured and what the goals are.

If you manage to put these things together and get started, even with a small project, you'll already be participating in that gigantic shift. Just be sure to have clear goals, iterate, and experiment.

Source: Economic Times

Top IoT applications for industrial

We round up some of the most innovative and trailblazing industrial companies across the landscape of the industrial Internet of Things.

The term “Industrie 4.0” heralds the coming of a new industrial revolution through smart manufacturing. The term “industrial Internet of Things” has a more muted-sounding promise of driving operational efficiencies through automation, connectivity and analytics. But the focus of IIoT — on industry at large — is broader.

Here, we take a comprehensive view, rounding up 20 IIoT leaders and pioneers, drawing on the feedback from industry analysts and consultants. The focus here is not on vendors offering, say, a cloud-based platform for monitoring industrial machines but on the companies that themselves are using IIoT technology to drive their business forward.

For the sake of this feature, we focus on organizations that use connected technology in tandem with cloud-based analytics to drive efficiencies and launch new business models. We concentrate on organizations that focus on logistics, agriculture and traditional “hard-hat” undertakings such as construction, manufacturing, mining, energy productionand supply. We leave out healthcare, and smart city and smart building applications, which occasionally get lumped into the IIoT domain. 

The companies on this list, presented alphabetically, are not idly boasting about the promise of IIoT to transform their business; they have already begun the transformation.

List of Application

Robotic Process Automation Market

Popular Trends & Technological advancements to Watch Out for Near Future 2023

As per the findings of the research, rule based operations have been the largest revenue generators in the global robotic process automation market, as compared to knowledge based operations. Further, among various processes, automation solutions segment is expected to continue its highest revenue contribution to the market, during the forecast period. Among various industries, retail and consumer goods witnessed the highest growth in demand of robotic process automation, during 2014 - 2016. However, banking, financial services and insurance (BFSI) is expected to hold the largest market during the forecast period. 

Geographically, North America has been the largest market for robotic process automation, whereas Asia-Pacific is expected to witness the fastest growth among all regions, during the forecast period. The anticipated growth in the market can be attributed to factors such as advancement in new technologies, growing digitalization, growth in automation software industry, and increasing adoption of business process automation solutions by small and medium scale enterprises in the region..
Some of the key players operating in the robotic process automation ecosystem are Nice Systems Ltd., Pegasystems Inc., Automation Anywhere, Blue Prism PLC, Ipsoft, Inc., Celaton Ltd., Redwood Software, UiPath SRL, Verint System Inc., Xerox Corporation, and IBM Corporation.GLOBAL ROBOTIC PROCESS AUTOMATION MARKET SEGMENTATION

By Process

• Automated Solution

• Decision Support & Management

• Interaction Solution

By Operation

• Rule Based

• Knowledge Based

By Service

• Professional

• Training

By Enterprise Size

• Small and Medium Enterprise

• Large Enterprise

By Industry

• BFSI

• Telecom & IT

• Retail and Consumer Goods

• Manufacturing

• Healthcare and Pharmaceuticals

• Others

By Geography

• North America

o The U.S.

o Rest of North America

• Europe

o The U.K.

o Germany

o France

o Rest of Europe

• Asia-Pacific

o China

o Japan

Artificial Intelligence Market in Agriculture

The most significant factor driving the demand for artificial intelligence in agriculture sector is the continuous surge in demand for agriculture robots, globally. This growth in demand is attributed to relative reduction in the average available agricultural workforce. Agriculture robots are expected to replace human labour and help overcome the scarcity of physical labour in near future. The trend toward digital agriculture and new farming technologies has opened up new growth opportunities. 

According to a report by the United Nations (UN), the global population is expected to reach nearly nine billion people by 2050. This indicates a significant increase in agricultural production to meet the growing demand for food, globally. Also, it has led to development of new technologies, such as artificial intelligence, in agriculture. Agriculture industry demands new and innovative technologies to face and overcome the growing challenge of meet the increasing demand for food. Artificial intelligence is one of the promising technologies of recent times, which is capable of catering to the ongoing food demand from the agriculture sector through increased production. 

Some of the major players in artificial intelligence market in agriculture are IBM Corporation, Microsoft Corporation, Google Inc., NVIDIA Corporation, Intel Corporation, Sentient Technologies, and Numenta Inc.

Global Artificial Intelligence Market in Agriculture 

By Solution

• Crop Monitoring
• Automated Irrigation
• Ai-Guided Drone

By Type

• Product

o Hardware
o Software

• By Service

o Installation
o Training
o Support and Maintenance

By Geography

• North America

o The U.S.
o Canada

• Europe

o The U.K.
o Germany
o France
o Rest of Europe

• Asia-Pacific

o China
o Japan
o India
o South Korea
o Rest of Asia-Pacific

• Rest of the World (RoW)

o Mexico
o Brazil
o Rest of RoW

Quick Bootstrapping Tips for Entrepreneurs

Bootstrapping your business can be a smart and effective way to go. It gives you an added level of control and independence, as you can follow your own vision (within your financial limits) and not be bound to the opinions of investors.

It will likely require a great deal of patience. Here are a few tips to get the ball rolling.

1. Get acquainted with the term "burn rate."

Know your budget better than the back of your hand. Many burgeoning entrepreneurs are too optimistic about expenses, often underestimating how much they’re actually spending and finding themselves dealing with severe budget issues.

As you engage in the budget-building process, err on the side of caution, increasing your burn rate estimate by 15 to 20 percent of your initial number. From this point forward, you can monitor your monthly spending until you can gain a clearer picture of your actual burn rate.

Additionally, you can look to cut expenses to line up with your projections. Do this in all aspects of your life, not just in business -- as a business owner, your outside financial decisions still impact your overall success.

For example, if you’re paying off a car, look into refinancing your car loan. According to Auto. Loan, there’s a good chance you can lower your monthly payments and interest rates as long as you’ve been on time with previous payments.

2. Become a C corporation.

While becoming a C corporation won’t give you the sexy LLC label, it minimizes the personal risk involved in bootstrapping a startup. As a C corp, you won’t be taxed in conjunction with your business, or rather, you and your business are separate entities.

This way, you aren’t held responsible to pay taxes on everything your startup makes, which, if business is good, can end up being quite a bit. Operating as a C corp gives you a bit more safety and motivates you to keep good track of both individual and business finances.

3. Stay away from credit card debt.

This is where patience really becomes a virtue. Many business owners form an unhealthy reliance on credit card debt as they try to grow. Do whatever you can to avoid this. This debt will stack up, and eventually you’ll have to reckon with it.

As difficult as it can be, you’ll have to learn to operate within the bounds of the money you actually have. This is what makes bootstrapped startups rare, but it is also what makes them special.

4. Focus on becoming well-rounded.

Bootstrapping a business is not for the faint of heart nor is it for the one-dimensional businessperson. In order to successfully bootstrap your startup, you’ll have to wear many hats.

Get ready to become a financial wiz. Be prepared to earn your keep by coming up with a marketing strategy for your product. At least for the first while, it’s going to be your responsibility to fill in wherever there’s a gap.

5. Make connections.

Making a connection doesn’t necessarily mean financially getting involved with an outside party. In this case, making connections can help you find new customers and, more importantly, facilitate the learning process as you associate with established successes.

Remember: Just because you’re funding yourself doesn’t mean you can’t look for help. As you connect with experienced entrepreneurs, pick up as much knowledge from them as possible. Learn what works and what doesn’t. Take their advice on how to apply these lessons to your business. These connections will often prove just as valuable as a financial investment.

Ultimately, you’re responsible for your own success. Bootstrapping your business makes this doubly true. But, if you can patiently navigate the waters of building a startup, it will be worth all the time and effort you put into it.

Written by Nathan Resnick, Guest Writer / CEO of Sourcify

Find "The Right Money" for Your Business"

How to Secure your IoT Solution From Edge to Cloud?

Maintain supply chain integrity: Enterprise companies need to ensure that their vendors and suppliers have defined Supply Chain Management (SCM) procedures that include baseline testing of components and specifications for parts used in IoT projects. In addition, they should be able to provide information on the entire manufacturing process. They should also share any changes in the system or any technical vulnerabilities in components with the IoT system owner. Any updates of the system such as changes in configuration, software changes and so forth should also be shared with the system owner or operator. Supply chain management systems should be able to consult a dashboard where they can easily access vendors’ and suppliers’ details, and any changes in the specifications of the components or parts.

Establish a chain of trust: Ensuring a high degree of security for an IoT implementation requires that devices, gateways and applications that are part of an IoT value chain. A trustworthy system enables the “chain of trust,” and this level of confidence should be maintained in the entire lifecycle of the system and adapt to new changes.

The basic categories for building a chain of trust, according to the Industrial Internet Consortium’s security framework include:

1. Security, which is the assurance of a system that it will remain secure from any outside threats, and attempts to harm the system. It also includes confidentiality of the information that it will not be disclosed to any unauthorized entity, the integrity of the system to avoid inappropriate changes and destruction of the information, and availability of the system to provide instantaneous information to an authorized user.
2. Safety, which is the condition at which a system runs without posing a threat of danger includes safeguarding people and physical OT assets.
3. Reliability is the ability of a system or component to perform its required functions under stated conditions for a specified time. Reliability and availability are correlated. Reliability can be thought of like a fraction: it is the amount of actual availability over scheduled availability, as affected by things like scheduled maintenance, updates, repairs and backups. Hence, when the scheduling is done properly, it is possible to get the actual availability (reliability) closer/equal to the scheduled availability.
4. Resilience is achieved by designing the system so that, when a failure occurs, the system can find an alternative way to accomplish the task. Failure in a single component should not affect other parts of the system. The system should be able to deal with failed or faulty processes automatically.
5. Privacy is the ability of personnel or an organization to have control of the information flow. It includes matters such as the confidentiality of processing and transferring data and who has access to that data.

When a system has all of these characteristics, it should be able to stand up to risks predicted for the system.

Communication and network security

An important aspect of any connected device or IoT system involves peer-to-peer communication between gateways and devices as well as communication to the cloud.

Data security

Securing data at endpoints involves data-at-rest (DAR) and data-in-use (DIU). The communication security is required for data-in-motion (DIM). For DAR, TPM (Trusted Platform Module) storage key can be used to secure the data. For DIU, runtime integrity techniques can be used to monitor memory access, and detect & protect against memory attacks. For DIM, data tokenization (a type of cryptography) can be used to protect sensitive data with encryption that can be decoded by authorized parties. See the example below showing a hospital’s patients database

There are three main techniques for cryptography: shared key, certificate-based authentication, and token-based authentication.

Cyber theft prevention

From a theft perspective, the most common type of targets are IP addresses, Fully Qualified Domain Names (FQDNs), and malicious URLs. There are many frameworks that can identify the cyber threats and mitigate them, including the Collective Intelligence Framework (CIF),

Trusted Automated eXchange of Indicator Information (TAXII) and Structured Threat Information Expression (STIX). Such technological frameworks continuously analyzes data, creating a chain of messages. In the STIX framework, for instance, whenever a user asks for specific data, the system provides information on cyber risks, threat actors, a recommended course of action and other information. For building a chain of trust, it is important for IoT devices to share threats and other pertinent information with the nearby devices that are on the same network.

Hardware security

Hardware security can be achieved in an IoT solution with Trusted Platform Modules (TPMs) and Trusted Execution Environment (TEE). TPM is essentially a chip that is installed on an IoT device near the CPU. It is used for mainly cryptographic operations, which creates a security key, saves it, stores the data and other related operations. They can use to ensure the integrity of a platform, for disk encryption and password protection.

TEE is a separate execution platform that differentiates the operational capability from the security functionality. It consists of APIs, kernel and a trusted OS that runs security checks, parallel to the standard OS. TEE consists root of trust (RoT), which includes a trusted boot platform, a measured boot process and an attestation process. TEEs also help ensure the integrity of applications and data storage. A trusted boot platform enables a secure boot, avoiding problems with malware that self-installs during the boot process. A measured boot process provides data on every process of the boot sequence before executing it on the standard OS. The attestation process allows the process to share its trustworthiness and security parameters with other trusted sources, securely. TEEs also help ensure the integrity of applications and data storage.

Blockchain-based security

While blockchain is best known for its use in cryptocurrencies like Bitcoin, the technology can be used for authentication in IoT networks as it uses a “micro-ledger” as evidence for peer-to-peer communications. Blockchain can record the communication history of two IoT gateways or devices. Once an action (or “transaction”) get stored in a micro-ledger, then it cannot be altered in the future. While certificate-based encryption technologies can be forged, Blockchain has the advantage of being distributed, and thus supports the security concept of non-repudiation, meaning a person who triggers an action on an IoT network cannot deny doing so.

Various attacks for IoT applications

A handful of IoT-related attacks seem to receive the most attention in the popular press. There is, of course, the Mirai botnet that brought down a chunk of the internet last year. There’s BrickerBot, which renders insecure IoT devices unusable. On the industrial side, Stuxnet is famous for causing physical damage to nuclear centrifuges in Iran. And then there is BlackEnergy — a malware variant that shut down a portion of Ukraine’s power grid.

Pure software attacks: 

This category includes malware variants such as viruses and trojans and worms. Also in this category is fuzzing, in which random data is thrown at software to see how it reacts. Distributed Denial of Service (DDoS) attacks can be software-based as well, although they can also occur at lower levels of the OSI Model. One potential example of an IoT-related DDoS risk would be safety-critical information such as warnings of a broken gas line that can go unnoticed through a DDoS attack of IoT sensor networks.

Network attacks: 

One of the biggest vulnerabilities of IoT devices is their wireless connectivity, which can make them remotely exploitable. Here, there are a variety of possible attacks that are possible on the devices, or “nodes,” connected to the network.

In an enterprise Internet of Things context, those nodes typically communicate with the gateway that is the core of that implementation. The node connects all of the IoT devices to the cloud.

Let’s assume that we have an industrial IoT application with interconnected gateways linked to each other in a mesh network. If a hacker jams the functionality of a gateway with denial of service requests, they can bring down the whole IoT project. Thus, a single attacker can stop the IT and OT elements of a system from interacting, as we discussed in the article “IoT gateway architecture: Clustering ensures reliability.” 

Attacks with a physical component: 

IoT attacks at the physical layer of the OSI Model require unauthorized access to physical sensing, actuation and control systems. Consider how electronic car theft works as an example. Since cars are essentially computers on wheels, hackers have a variety of options at their disposal. They can clone the radio signals from a key fob to open a locked vehicle. A hacker with physical access to a vehicle’s Controller Area Network (CAN) bus underneath the steering wheel can cause all sorts of mischief: They can unlock the car’s immobilizer that stops a thief from driving away and reprogram a new key for the vehicle. Access to the CAN bus could also enable them to hack the speedometer, door locks and other components.

The similar threat applies to industrial control systems, which have a decades-long history. Many industrial machines make use of supervisory control and data acquisition (SCADA), a technology that was created decades ago without much thought about security. As a result, an attacker with physical access to a SCADA system can cause significant damage to industrial facilities and critical infrastructure.

Similar threats could apply to medical devices. An attacker could gain access to an implantable device such as a cardioverter defibrillator or an external medical device such as an insulin pump to install malware.

Side-channel attack: 

A side-channel attack is the IT equivalent to spotting a liar by their nervous behavior while fibbing rather than what they say. In other words, the attacker can infer which encryption is used without having access to either plain or ciphertext. There are myriad ways this might work. An attacker might study a device’s power use or optical or radio emanations. A hacker could even observe the sounds coming from the electronic components within a device and use that information to crack its encryption key.

Side-channel attacks are a threat to IoT devices as well as traditional IT infrastructure. There is, however, a big difference between IT and IoT security. IoT systems typically use weaker authentications and have less-effective layers of security than conventional IT infrastructure.

Cryptanalysis attack: 

In this type of exploit, a hacker tries to recover an encrypted message without access to an encryption key. Examples include brute-force attacks when a hacker tries every possible password combination to gain access to a system. The known-plaintext attack, with roots stretching back to WWII, is another example, in which a hacker has access to unencrypted text as well as its encrypted counterpart. Another possible exploit in this category is a so-called “man-in-the-middle-attack” where hackers position themselves in between two network nodes to gain access to the communication between them.

Why it’s time to batten down the IoT hatches

After hearing countless predictions about billions of connected devices and trillions in market value, it’s easy to understand why Internet of Things devices are proliferating. But the landscape certainly poses a challenge to cybersecurity professionals. Many of the IoT devices out in the field now have poor security. The complexity of the IoT landscape makes it hard to tick all of the security boxes.

In theory, it should be relatively straightforward to answer questions like these: Is the cloud architecture of your IoT application configured correctly? How many IoT devices are on your network? Are any of them are using hard-coded passwords? How would you react if your IoT implementation was hacked?

Security problems that can besiege IoT applications include:

• Lack of mature technologies and business processes: There is a proliferation of diverse standards. This complexity can, in turn, help enable the introduction of vulnerabilities and provides attackers with a way to infiltrate the enterprise.
•  Limited guidance for lifecycle maintenance and management of IoT devices
• Physical security concerns
• Lack of agreement on how to approach authentication and authorization for IoT edge devices
• Lack of best practices for IoT-based incident response activities
• Audit and logging standards are not defined for IoT components

Supply chain vulnerabilities

Enterprises with IoT applications can achieve trustworthiness at each level of the supply chain, including people, process, design, manufacturing and delivery levels. If there is a lack of information transfer at any link in the supply chain, it can enable security vulnerabilities and possibly open it up to a breach. Enterprise companies should have a policy in place to prevent unauthorized access to important systems while weeding out rogue vendors who could leverage technical loopholes to obtain sensitive data.