Putting the “AI” in ThAInksgiving

It’s true that AI and machine learning are changing the world, and in a few years, it will be embedded in all of the technology in our lives.

So maybe it makes sense to help folks at home better understand machine learning. After all, without deep knowledge of current tech, autonomous vehicles seem dangerous, Skynet is coming, and the (spoiler warning!) AI-controlled human heat farms of The Matrix are a real possibility.

This stems from a conflation of the very real and exciting concept of machine learning and the very not real concept of “general artificial intelligence,” which is basically as far off today as it was when science fiction writers first explored the idea a hundred years ago.

That said, you may find yourself in a discussion on this topic during the holidays this year, either of your own volition or by accident. And you’ll want to be prepared to argue for AI, against it, or simply inject facts as you moderate the inevitably heated conversation.

But before you dive headlong into argument mode, it’s important that you both know what AI is (which, of course, you do!) and that you know how to explain it.

Understanding Onboard Flash Programming

Firmware often is preprogrammed into flash memory devices prior to the printed-circuit board’s (PCB) manufacture to maintain high throughput and avoid slowing the manufacturing beat rate. Yet there are advantages to programming the flash memory after it has been soldered to the PCB. In-circuit test (ICT), the Joint Test Action Group (JTAG) interface, and external connectors all can be used to program flash devices without impacting manufacturing beat rates. Image size, existing manufacturing infrastructure, system capability, and required programming methods also should be considered in choosing an optimal preprogramming solution.

Onboard Programming Advantages

One of the most significant advantages of onboard programming is that it enables design and manufacturing engineers to combine IC testing and programming into a single manufacturing process, which eliminates the need to perform offboard programming of an IC prior to surface-mounting the device.

Flash memory utilization typically depends on the development stage of a product. In many applications, designers use flash memory to develop, store, and eventually execute firmware and application code. Onboard programming is often used during the development of new application code or firmware. Code is written, compiled, and downloaded onto the flash device on the development board and then tested on the target PCB.

When the hardware and software nears production readiness, it is common practice to preprogram flash memory devices prior to starting high-volume PCB manufacturing flows for two principal reasons. First, firmware loaded onto the device can be used to perform basic booting and testing of the PCB during manufacturing to check system/module functionality. Second, loading the final firmware, operating system (OS), and application code on the flash device prior to manufacturing maintains a high-volume manufacturing beat rate. To support these usage models, multiple vendors provide systems for loading firmware and data into flash memory devices prior to the PCB solder flow process

Firmware often is preprogrammed into flash memory devices prior to the printed-circuit board’s (PCB) manufacture to maintain high throughput and avoid slowing the manufacturing beat rate. Yet there are advantages to programming the flash memory after it has been soldered to the PCB. In-circuit test (ICT), the Joint Test Action Group (JTAG) interface, and external connectors all can be used to program flash devices without impacting manufacturing beat rates. Image size, existing manufacturing infrastructure, system capability, and required programming methods also should be considered in choosing an optimal preprogramming solution. 

•  Onboard Programming Advantages
•  Onboard Programming Techniques
•  Conclusion

Onboard Programming Advantages

One of the most significant advantages of onboard programming is that it enables design and manufacturing engineers to combine IC testing and programming into a single manufacturing process, which eliminates the need to perform offboard programming of an IC prior to surface-mounting the device.

---

Related Articles

•  Interview: Spansion’s CTO Talks About Embedded Charge Trap NOR Flash
•  Design FAQ: Path To High-Performance NAND Flash
•  Design FAQ: Reliability Challenges Emerge With NAND Flash Migration

---

Flash memory utilization typically depends on the development stage of a product. In many applications, designers use flash memory to develop, store, and eventually execute firmware and application code. Onboard programming is often used during the development of new application code or firmware. Code is written, compiled, and downloaded onto the flash device on the development board and then tested on the target PCB.

When the hardware and software nears production readiness, it is common practice to preprogram flash memory devices prior to starting high-volume PCB manufacturing flows for two principal reasons. First, firmware loaded onto the device can be used to perform basic booting and testing of the PCB during manufacturing to check system/module functionality. Second, loading the final firmware, operating system (OS), and application code on the flash device prior to manufacturing maintains a high-volume manufacturing beat rate. To support these usage models, multiple vendors provide systems for loading firmware and data into flash memory devices prior to the PCB solder flow process. 

The ability to leverage existing manufacturing systems for testing, coupled with the fast program speeds of flash memory, make onboard programming a viable mainstream solution for programming flash memory. In addition to having no impact on the manufacturing line, other advantages of onboard programming include:  

•  Faster time-to-market: If a change is made to the flash memory image, the change can be sent directly to the manufacturing line, speeding the delivery time into production. 
•  Improved quality: Removing the preprogrammed inventory reduces the risk of sending out the wrong version of firmware, OS, or application code as well as the time and resources needed to track units that must be reworked.  
•  Supply chain simplification: Traditionally, manufacturing operations track both programmed and unprogrammed units, whereas with onboard programming, only unprogrammed flash units need to be monitored.

Onboard Programming Techniques

Several onboard programming (or in-system programming [ISP]) techniques are used to load firmware and data onto a flash device after solder reflow. These techniques include ICT, JTAG, and external connections (Table 1).

ICT is a test methodology that checks PCBs for assembly defects such as shorts, opens, resistance, and capacitance directly after devices are mounted in the solder reflow oven. After undergoing a successful test sequence, flash memory devices can be programmed directly by the ICT equipment. The program time must be very short (approximately 1 to 4 seconds) to avoid negatively impacting the manufacturing beat rate. In addition, programming a memory device with ICT typically requires a low-pin-count interface, which limits the number of nails/pins on the test fixture.

JTAG is an IEEE standard (IEEE 1149.1) that uses boundary scan architecture to test for structural integrity between devices on a PCB. The JTAG interface also can perform ISP of flash memory devices. During the boundary scan check, the interface enables direct control over the memory signals, enabling an image to be programmed into a flash device. JTAG programmers typically write software and data to the flash memory using data bus access similar to the access used to update CPU firmware.

ICT and JTAG are typically used for programming small data images (less
than 16 Mbytes) on flash memory devices like NOR due to the low bandwidth required by these processes (low-pin-count bus or slow clock frequencies). Table 2 compares the theoretical write performance of NOR flash lithography nodes and interfaces. 

External connection and new ICT models are the best techniques for transferring larger data images that require a higher bandwidth. These two techniques provide an opportunity for manufacturing tool and support vendors to enable faster transfers of high-density firmware and data to flash memory devices. ICT testers have recently reached program speeds of 20 Mbytes/s.

External-edge connections accomplish onboard programming using two basic methods. With a microprocessor, data is transferred into the flash device with an existing connector (e.g., USB) using the main microprocessor on the PCB for control. Without a microprocessor, data is transferred into the flash device with an existing connector (e.g., UART) using specific control logic on the PCB dedicated to the manufacturing environment. In this case, the onboard microprocessor is not used.

External connection techniques and new ICT models can transfer data at much higher rates. Table 3 shows the bandwidth that can be achieved using single-level cell (SLC) NAND and e∙MMC memory for a given lithography node. In each case, the device rather than the interface limits the programming bandwidth.

Critical Factors For Onboard ProgrammingWhen choosing an onboard programming method to program images on flash devices, memory type, image size, board design, and existing manufacturing capabilities are all critical factors to consider. 

Firmware code, operating systems, and data files can range from tens of kilobytes to hundreds of megabytes. For a smaller image size (less than 8 Mbytes), ICT and JTAG are ideal because of their low bandwidth. Today, many manufacturing lines use JTAG and ICT to test board-level functionality, so additional tools are not needed. With existing hardware in place and NOR flash products that can program between 1 Mbyte/s and 2 Mbytes/s, onboard programming is an ideal solution when the program time does not exceed 4 seconds and does not impact the manufacturing beat rate. 

The program performance of high-density (32 Mbytes to 1 Tbyte) NAND flash enables the use of optimized onboard programming techniques. SLC NAND is the fastest (5 to 60 Mbytes/s) programming flash memory on the market. However, the flash market is transitioning to managed NAND solutions such as e∙MMC embedded memory that have programming speeds ranging from 10 to 35 Mbytes/s. 

Memory devices with high-bandwidth capabilities use two different techniques for transferring large data images: new ICT models and external connections. Recent ICT models can support fast program speeds for high-density memory devices. External-edge connectors can either use the onboard processor or skip the processor and program the flash memory directly. Both of these external connection techniques require either a PCB design to route the data through the processor or logic onboard to handle bus isolation to program the flash memory.

High-speed quantum encryption could stop hackers: Study

WASHINGTON: To fight back against the common security attacks, scientists have created a high-speed encryption system to stop hackers.

The system is capable of distributing encryption codes at megabit-per-second rates, five to 10 times faster than existing methods and on par with current internet speeds when running several systems in parallel.

In a study, published in the journal Science Advances, the researchers demonstrate that the technique is secure from common attacks, even in the face of equipment flaws that could open up leaks.

"We are now likely to have a functioning quantum computer that might be able to start breaking the existing cryptographic codes in the near future," said Daniel Gauthier, from The Ohio State University in the US.

"We really need to be thinking hard now of different techniques that we could use for trying to secure the internet," he said.

To a hacker, our online purchases, bank transactions and medical records all look like gibberish due to ciphers called encryption keys.

Personal information sent over the web is first scrambled using one of these keys, and then unscrambled by the receiver using the same key.

For this system to work, both parties must have access to the same key, and it must be kept secret.

Quantum key distribution (QKD) takes advantage of one of the fundamental properties of quantum mechanics - measuring tiny bits of matter like electrons or photons automatically changes their properties - to exchange keys in a way that immediately alerts both parties to the existence of a security breach.

Though QKD was first theorised in 1984 and implemented shortly thereafter, the technologies to support its wide-scale use are only now coming online.

The problem with many of these systems, said Nurul Taimur Islam, from the Duke University in the US, is that they can only transmit keys at relatively low rates - between tens to hundreds of kilobits per second - which are too slow for most practical uses on the internet.

Like many QKD systems, Islam's key transmitter uses a weakened laser to encode information on individual photons of light. But they found a way to pack more information onto each photon, making their technique faster.

By adjusting the time at which the photon is released, and a property of the photon called the phase, their system can encode two bits of information per photon instead of one.

This trick, paired with high-speed detectors developed by Clinton Cahall, from the Duke University, powers their system to transmit keys five to 10 times faster than other methods.

"It was changing these additional properties of the photon that allowed us to almost double the secure key rate that we were able to obtain if we hadn't done that," said Gauthier.

BlackBerry Expands Embedded Software Design and Delivery with New Partners in Japan

Fujisoft and Hitachi Industry & Control join BlackBerry Partner Program to deliver safety-critical and secure software solutions for Japanese industry

TOKYO, JAPAN and WATERLOO, ONTARIO--(Marketwired - Nov. 21, 2017) - BlackBerry Limited (NYSE:BB)(TSX:BB) today announced it is expanding its network of embedded technology experts with two new partners in Japan - Fujisoft Incorporated and Hitachi Industry & Control Solutions, Ltd. The companies have joined a new, specialized Value-Added Integrator (VAI) program announced by BlackBerry in March 2017, which aims to build a worldwide network of experts trained on BlackBerry QNX and Certicom technologies. The VAI program allows partners to deliver integration services and build upon BlackBerry's embedded technologies to design and develop secure, mission-critical solutions - ultimately accelerating product time to market.

Kaivan Karimi, senior vice president and head of sales for BlackBerry Technology Solutions says, "Japan is at the cutting edge of the Internet of Things and embedded software systems, which presents a significant opportunity for companies delivering security and software solutions in industries such as automotive, manufacturing and healthcare. By using trusted BlackBerry software and cryptography-based solutions, companies around the world can develop safety-certified embedded systems and devices that are not just secure, but BlackBerry Secure. We are very pleased to grow BlackBerry's VAI partner program with Fujisoft and Hitachi Industry & Control, and look forward to helping their customers to further accelerate the design, development, integration and testing of mission-critical, next-generation systems in Japan and around the world."

VAI partners in the program provide support for technologies and services including BlackBerry's QNX Neutrino Realtime OS, QNX Momentics Tool Suite, QNX Hypervisor, QNX SDK for Apps & Media, QNX Wireless Framework, QNX OS for Safety, QNX OS for Medical, QNX CAR Platform for Infotainment, QNX Platform for Acoustics, and QNX Platform for ADAS, Certicom Toolkits, Certicom Managed Public Key Infrastructure and Certicom Asset Management System. Applications include automotive systems, medical surgical robots, smart grids, train control systems and industrial automation.

"Fujisoft provides one stop solutions from hardware to middleware and to application development, taking advantage of over 45 years of development experience in the automotive, medical equipment, industrial equipment and consumer markets. With the participation in the VAI program at this time, we further strengthen our technical cooperation with BlackBerry QNX. Fujisoft with more than 6,000 engineers, offers complete technologies and services with reliable expertise for customer system development that requires safety and high reliability," said Masaki Shibuya, Director & Executive Operating Officer of Fujisoft.

"Hitachi Industry & Control is pleased to be able to participate in BlackBerry's global VAI program. Hitachi Industry & Control is a mainstay company supporting the Hitachi group's industrial solution business, providing solutions to a wide range of industrial, security and embedded fields. Collaborating our advanced embedded technologies such as camera application, image processing, robotics, network, functional safety, design platform utilization with the highly secure and safe BlackBerry QNX embedded platform, Hitachi Industry & Control will contribute to customers' development and business expansion," said Shin Nakano, General Manager, Embedded Systems Engineering Group of Hitachi Industry & Control.

Other partners participating in the VAI program include: Archermind Technology, Mcloudware, Micon Global, Mission Embedded, ThunderSoft, Witekio Corporation and Tata Elxsi, all of whom are working with BlackBerry to accelerate innovation in sophisticated and secure mission critical embedded systems.

To learn about VAI program, please visit: https://ca.blackberry.com/partners.

About BlackBerry

BlackBerry is a cybersecurity software and services company dedicated to securing the Enterprise of Things. Based in Waterloo, Ontario, the company was founded in 1984 and operates in North America, Europe, Asia, Australia, Middle East, Latin America and Africa. The Company trades under the ticker symbol "BB" on the Toronto Stock Exchange and New York Stock Exchange. For more information, visit www.BlackBerry.com.

BlackBerry and related trademarks, names and logos are the property of BlackBerry Limited and are registered and/or used in the U.S. and countries around the world. All other marks are the property of their respective owners. BlackBerry is not responsible for any third-party products or services.

Cyber Security Firms Turn To Artificial Intelligence As Hacking Threats Rise

Machine learning can be used to detect suspicious behavior and minimise threats to cyber networks.

Helsinki: Cyber security companies are turning to artificial intelligence and machine learning tools to ward off growing number of attacks on networks, Finland-based internet security firm F-Secure said.

As the world is fast moving towards Internet of Things and connected devices, deployment of artificial intelligence (AI) has become inevitable for cyber security firms to analyse huge amount of data to save networks from infiltration attempts, F-Secure's Security Advisor Sean Sullivan said. Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks.

On a daily basis, F-Secure Labs on an average receives sample data of 500,000 files from its customers that include 10,000 malware variants and 60,000 malicious URLs for analysis and protection, Sullivan said. 

For humans, it is a big task to go through such huge amount of data and machine learning tools and AI are lending a helping hand at this stage, he said.

Machine learning can be used to train logic designed to detect suspiciousness based on the structure of a file or its behaviour or both, another Security Advisor Andy Patel said.

Sullivan said any abnormal behaviour of a file is flagged by AI which helps in detecting threats at an early stage without much damage being done to the network.

Patel claimed behaviour models enable them to take preemptive steps to save their customers from ransomware attacks like 'Locky'.

When asked if machine tools and AI can make people's jobs in cyber security redundant, Patel said it is unlikely as attacks through malwares are designed by humans who think creatively to bypass automated security solutions. So, there is need of humans who can think creatively to defend networks from such attacks.

He also said AI and machine learning are at an evolving stage and there is a long way to go for widespread adoption of such tools in cyber security as only big players at present can afford building such systems and improving them every day.

History of artificial intelligence

The history of Artificial Intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with "an ancient wish to forge the gods."

The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The field of AI research was founded at a workshop held on the campus of Dartmouth College during the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true.

Eventually it became obvious that they had grossly underestimated the difficulty of the project due to computer hardware limitations. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence, and the difficult years that followed would later be known as an "AI winter". Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned by the absence of the needed computer power (hardware) and withdrew funding again.

Investment and interest in AI boomed in the first decades of the 21st century, when machine learning was successfully applied to many problems in academia and industry due to the presence of powerful computer hardware. As in previous "AI summers", some observers (such as Ray Kurzweil) predicted the imminent arrival of artificial general intelligence: a machine with intellectual capabilities that exceed the abilities of human beings.

Precursors 

McCorduck (2004) writes "artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized," expressed in humanity's myths, legends, stories, speculation and clockwork automatons.

AI in myth, fiction and speculation

Main article: Artificial intelligence in fiction

Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and Pygmalion's Galatea.In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as Jābir ibn Hayyān's Takwin, Paracelsus' homunculus and Rabbi Judah Loew's Golem.By the 19th century, ideas about artificial men and thinking machines were developed in fiction, as in Mary Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots), and speculation, such as Samuel Butler's "Darwin among the Machines." AI has continued to be an important element of science fiction into the present.

Artificial intelligence (AI)

Artificial intelligence (AI, also machine intelligence, MI) is Intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip "AI is whatever hasn't been done yet."For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Capabilities generally classified as AI as of 2017 include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data, including images and videos.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,followed by disappointment and the loss of funding (known as an "AI winter"),followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other.

The traditional problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects.General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, neural networks and methods based on statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology and many others.

The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI a danger to humanity if it progresses unabatedly.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.