Artificial Intelligence - What Are Mobile Recommendation Assistants?

 

Mobile Recommendation Assistants, also known as Virtual Assistants, Intelligent Agents, or Virtual Personal Assistants, are a collection of software features that combine a conversational user interface with artificial intelligence to act on behalf of a user.

They may deliver what seems to a user as an agent when they work together.

In this sense, an agent differs from a tool in that it has the ability to act autonomously and make choices with some degree of autonomy.

Many qualities may be included into the design of mobile suggestion helpers to improve the user's impression of agency.

Using visual avatars to represent technology, incorporating features of personality such as humor or informal/colloquial language, giving a voice and a legitimate name, constructing a consistent method of behaving, and so on are examples of such tactics.

A human user can use a mobile recommendation assistant to help them with a wide range of tasks, such as opening software applications, answering questions, performing tasks (operating other software/hardware), or engaging in conversational commerce or entertainment (telling stories, telling jokes, playing games, etc.).

Apple's Siri, Baidu's Xiaodu, Amazon's Alexa, Microsoft's Cortana, Google's Google Assistant, and Xiaomi's Xiao AI are among the mobile voice assistants now in development, each designed for certain companies, use cases, and user experiences.

A range of user interface modali ties are used by mobile recommendation aides.

Some are completely text-based, and they are referred regarded as chatbots.

Business to consumer (B2C) communication is the most common use case for a chatbot, and notable applications include online retail communication, insurance, banking, transportation, and restaurants.

Chatbots are increasingly being employed in medical and psychological applications, such as assisting users with behavior modification.

Similar apps are becoming more popular in educational settings to help students with language learning, studying, and exam preparation.

Facebook Messenger is a prominent example of a chatbot on social media.

While not all mobile recommendation assistants need voice-enabled interaction as an input modality (some, such web site chatbots, may depend entirely on text input), many contemporary examples do.

A Mobile Recommendation Assistant uses a number similar predecessor technologies, including a voice-enabled user interface.

Early voice-enabled user interfaces were made feasible by a command syntax that was hand-coded as a collection of rules or heuristics in advance.

These rule-based systems allowed users to operate devices without using their hands by delivering voice directions.

IBM produced the first voice recognition program, which was exhibited during the 1962 World's Fair in Seattle.

The IBM Shoebox has a limited vocabulary of sixteen words and nine numbers.

By the 1990s, IBM and Microsoft's personal computers and software had basic speech recognition; Apple's Siri, which debuted on the iPhone 4s in 2011, was the first mobile application of a mobile assistant.

These early voice recognition systems were disadvantaged in comparison to conversational mobile agents in terms of user experience since they required a user to learn and adhere to a preset command language.

The consequence of rule-based voice interaction might seem mechanical when it comes to contributing to real humanlike conversation with computers, which is a feature of current mobile recommendation assistants.

Instead, natural language processing (NLP) uses machine learning and statistical inference to learn rules from enormous amounts of linguistic data (corpora).

Decision trees and statistical modeling are used in natural language processing machine learning to understand requests made in a variety of ways that are typical of how people regularly communicate with one another.

Advanced agents may have the capacity to infer a user's purpose in light of explicit preferences expressed via settings or other inputs, such as calendar entries.

Google's Voice Assistant uses a mix of probabilistic reasoning and natural language processing to construct a natural-sounding dialogue, which includes conversational components such as paralanguage ("uh", "uh-huh", "ummm").

To convey knowledge and attention, modern digital assistants use multimodal communication.

Paralanguage refers to communication components that don't have semantic content but are nonetheless important for conveying meaning in context.

These may be used to show purpose, collaboration in a dialogue, or emotion.

The aspects of paralanguage utilized in Google's voice assistant employing Duplex technology are termed vocal segre gates or speech disfluencies; they are intended to not only make the assistant appear more human, but also to help the dialogue "flow" by filling gaps or making the listener feel heard.

Another key aspect of engagement is kinesics, which makes an assistant feel more like an engaged conversation partner.

Kinesics is the use of gestures, movements, facial expressions, and emotion to aid in the flow of communication.

The car firm NIO's virtual robot helper, Nome, is one recent example of the application of face expression.

Nome is a digital voice assistant that sits above the central dashboard of NIO's ES8 in a spherical shell with an LCD screen.

It can swivel its "head" automatically to attend to various speakers and display emotions using facial expressions.

Another example is Dr. Cynthia Breazeal's commercial Jibo home robot from MIT, which uses anthropomorphism using paralinguistic approaches.

Motion graphics or lighting animations are used to communicate states of communication such as listening, thinking, speaking, or waiting in less anthropomorphic uses of kinesics, such as the graphical user interface elements on Apple's Siri or illumination arrays on Amazon Alexa's physical interface Echo or in Xiami's Xiao AI.

The rising intelligence and anthropomorphism (or, in some circumstances, zoomorphism or mechanomorphism) that comes with it might pose some ethical issues about user experience.

The need for more anthropomorphic systems derives from the positive user experience of humanlike agentic systems whose communicative behaviors are more closely aligned with familiar interactions like conversation, which are made feasible by natural language and paralinguistics.

Natural conversation systems have the fundamental advantage of not requiring the user to learn new syntax or semantics in order to successfully convey orders and wants.

These more humanistic human machine interfaces may employ a user's familiar mental model of communication, which they gained through interacting with other people.

Transparency and security become difficulties when a user's judgments about a machine's behavior are influenced by human-to-human communication as machine systems become closer to human-to-human contact.

The establishment of comfort and rapport may obscure the differences between virtual assistant cognition and assumed motivation.

Many systems may be outfitted with motion sensors, proximity sensors, cameras, tiny phones, and other devices that resemble, replicate, or even surpass human capabilities in terms of cognition (the assistant's intellect and perceptive capacity).

While these can be used to facilitate some humanlike interaction by improving perception of the environment, they can also be used to record, document, analyze, and share information that is opaque to a user when their mental model and the machine's interface do not communicate the machine's operation at a functional level.

After a user interaction, a digital assistant visual avatar may shut his eyes or vanish, but there is no need to associate such behavior with the microphone's and camera's capabilities to continue recording.

As digital assistants become more incorporated into human users' daily lives, data privacy issues are becoming more prominent.

Transparency becomes a significant problem to solve when specifications, manufacturer data collecting aims, and machine actions are potentially mismatched with user expectations.

Finally, when it comes to data storage, personal information, and sharing methods, security becomes a concern, as hacking, disinformation, and other types of abuse threaten to undermine faith in technology systems and organizations.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Chatbots and Loebner Prize; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

References & Further Reading:

Lee, Gary G., Hong Kook Kim, Minwoo Jeong, and Ji-Hwan Kim, eds. 2015. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer.

Leviathan, Yaniv, and Yossi Matias. 2018. “Google Duplex: An AI System for Accomplishing Real-world Tasks Over the Phone.” Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

Viken, Alexander. 2009. “The History of Personal Digital Assistants, 1980–2000.” Agile Mobility, April 10, 2009.

Waddell, Kaveh. 2016. “The Privacy Problem with Digital Assistants.” The Atlantic, May 24, 2016. https://www.theatlantic.com/technology/archive/2016/05/the-privacy-problem-with-digital-assistants/483950/.

Post Quantum Computing Encryption - Future-Proofing Encryption

Encryption in the post-quantum era. 

Many popular media depictions of quantum computing claim that the creation of dependable large-scale quantum computers will bring cryptography to an end and that quantum computers are just around the corner. 

The latter point of view may turn out to be overly optimistic or pessimistic, if you happen to rely on quantum-computing-proof security. 

While quantum computers have made significant progress in recent years, there's no certainty that they'll ever advance beyond laboratory proof-of-concept devices to become a realistic daily technology. (For a more thorough explanation, see a recent ASPI study.) 

Nonetheless, if quantum computing becomes a viable technology, several of the most extensively used encryption systems would be vulnerable to quantum computer cryptography assaults because quantum algorithms may drastically shorten the time it takes to crack them. 

For example, the RSA encryption scheme for the secure exchange of encryption keys, which underlies most web-based commerce, is based on the practical difficulty of finding prime factors of very big integers using classical (non-quantum) computers.

However, there is an extremely efficient quantum technique for prime factorization (known as ‘Shor's algorithm') that would make RSA encryption vulnerable to attack, jeopardizing the security of the vast quantity of economic activity that relies on the ability to safeguard moving data. 

Other commonly used encryption protocols, such as the Digital Signature Algorithm (DSA) and Elliptic Curve DSA, rely on mathematical procedures that are difficult to reverse conventionally but may be vulnerable to quantum computing assaults. 

Moving to secure quantum communication channels is one technique to secure communications. 

However, while point-to-point quantum channels are conceivable (and immune to quantum computer assaults), they have large administration overheads, and constructing a quantum ‘web' configuration is challenging. 

A traditional approach is likely to be favored for some time to come for applications such as networking military force units, creating secure communications between intelligence agencies, and putting up a secure wide-area network. 

Non-quantum (classical) techniques to data security, fortunately, are expected to remain safe even in the face of quantum computer threats. 

Quantum assaults have been found to be resistant to the 256-bit Advanced Encryption Standard (AES-256), which is routinely employed to safeguard sensitive information at rest. 

Protecting data at rest addresses only half of the problem; a secure mechanism for transferring encryption keys between the start and end locations for data in motion is still required. 

As a result, there's a lot of work being done to construct so-called "post-quantum" encryption systems that rely on mathematical processes for which no quantum algorithms exist. 

IBM has already detailed a quantum-resistant technology for safely transporting data across networks.  If the necessity arises, such a system might possibly replace RSA and other quantum-vulnerable encryption systems.

If everything else fails, there's always encryption technologies for the twenty-first century. 

One technique to improve communication security is to be able to ‘narrowcast' in such a way that eavesdropping is physically difficult, if not impossible. 

However, this is not always practicable, and there will always be messages that must pass over channels that are sensitive to eavesdropping. 

Even so-called "secure" channels can be breached at any time. 

The actual tapping of a subsea cable run to a Soviet naval facility on the Kamchatka Peninsula by the US Navy in the 1970s is a good example. The cable was deemed safe since it ran wholly within Russian territorial seas and was covered by underwater listening posts. 

As a result, it transmitted unencrypted messages. The gathered signals, though not of high intelligence value in and of themselves, gave cleartext ‘cribs' of Soviet naval communications that could be matched with encrypted data obtained elsewhere, substantially simplifying the cryptanalytic work. 

Even some of the LPI/LPD technology systems discussed in earlier sections may be subject to new techniques. 

For example, the Pentagon has funded research on devices that gather single photons reflected off air particles to identify laser signals from outside the beam, with the goal of extracting meaningful information about the beam direction, data speeds, and modulation type. The ultimate objective is to be able to intercept laser signals in the future.  

A prudent communications security approach is to expect that an opponent will find a method to access communications, notwithstanding best attempts to make it as difficult as possible. 

Highly sensitive information must be safeguarded from interception, and certain data must be kept safe for years, if not decades. Cryptographic procedures that render an intercepted transmission unintelligible are required. 

As we saw in the section on the PRC's capabilities, a significant amount of processing power is currently available to target Australian and ally military communications, and the situation is only going to become worse. 

On the horizon are technical dangers, the most well-known of which is the potential for effective quantum computing. Encryption needs to be ‘future proofed.'

As secure intermediates, space-based interconnections are used. 

If the connection can be made un-interceptable, space-based communications might provide a secure communication route for terrestrial organizations. Information and control signals between spacecraft and the Earth have been sent by radio waves to and from ground stations until now. 

Interception is achievable when collection systems are close enough to the uplink transmitter to collect energy from either the unavoidable side lobes of the main beam or when the collection system is able to be positioned inside the same downlink footprint as the receiver. 

The use of laser signals of various wavelengths to replace such RF lines has the potential to boost data speeds while also securing the communications against eavesdropping. 

Using laser communication connection between spacecraft has a number of advantages as well. 

Transmission losses over long distances restrict the efficiency with which spacecraft with low power budgets can exchange vast amounts of data, and RF connections inevitably restrict bandwidth. 

The imposts on space, weight, and power on spacecraft would be reduced if such linkages were replaced by laser communications. 

The benefits might include being able to carry larger sensor and processing payloads, spending more time on mission (owing to reduced downtime to recharge batteries), or a combination of the two. 

In the United States, the Trump administration's Space Force and anticipated NASA operations (including a presence on the moon and deep space missions) have sparked a slew of new space-based communications research initiatives. 

NASA has a ten-year project road map (dubbed the "decade of light") aiming at creating infrared and optical frequency laser communication systems, combining them with RF systems, and connecting many facilities and spacecraft into a reliable, damage-resistant network. 

As part of that effort, it is developing various technology demonstrations. 

Its Laser Communications Relay Demonstration, which is set to be live in June, will utilize lasers to encode and send data at speeds 10 to 100 times faster than radio systems.  

NASA uses the example of transmitting a map of Mars' surface back to Earth, which may take nine years with present radio technology but just nine weeks using laser communications. T

he practicality of laser communications has been demonstrated in laboratory prototype systems, and NASA plans to launch space-based versions later this year. The Pentagon's Space Development Agency (SDA) and the Defense Advanced Research Projects Agency (DARPA) are both working on comparable technologies, but with military and intelligence purposes in mind. 

The SDA envisions hundreds of satellites linked by infrared and optical laser communication connections. 

Sensor data will be sent between spacecraft until it reaches a satellite in touch with a ground station, according to the plan. Information from an orbiting sensor grid may therefore be sent to Earth in subsecond time frames, rather than the tens of minutes it can take for a low-Earth-orbiting satellite to pass within line of sight of a ground station. 

Furthermore, because to the narrow beams created by lasers, an eavesdropper has very limited chance of intercepting the message. Because of the increased communication efficiency, ‘traffic jams' in the considerably more extensively utilized radio spectrum are significantly less likely to occur. 

This year, the SDA plans to conduct a test with a small number of "cubesats." Moving to even higher frequencies, X-ray beams may theoretically transport very high data-rate messages. In terrestrial applications, ionization of air gases would soon attenuate signals, but this isn't an issue in space, and NASA is presently working on gigabit-per-second X-ray communication lines between spacecraft.  

Although NASA is primarily interested in applications for deep space missions (current methods can take many hours to transmit a single high-resolution photograph of a distant object such as an asteroid after a flyby), the technology has the potential to link future constellations of intelligence-gathering and communications satellites with extremely high data-rate channels. On board the International Space Station, NASA has placed a technology demonstration.

Communications with a low chance of being detected. 

One technique to keep communications safe from an enemy is to never send them over routes that can be detected or intercepted. For mobile force units, this isn't always practicable, but when it is, communications security may be quite effective. 

The German army curtailed its radio transmissions in the run-up to its Ardennes operation in December 1944, depending instead on couriers and landlines operating within the region it held (which was contiguous with Germany, so that command and control traffic could mostly be kept off the airwaves).

 The build-up of considerable German forces was overlooked by Allied intelligence, which had been lulled into complacency by having routinely forewarned of German moves via intercepted radio communications. 

Even today, when fibre-optic connections can transmit data at far greater rates than copper connections, the option to go "off air" when circumstances allow is still valuable. Of course, mobile troops will not always have the luxury of transferring all traffic onto cables, especially in high-speed scenarios, but there are still techniques to substantially minimize the footprint of communication signals and, in some cases, render them effectively undetectable. 

Frequency-hopping and spread-spectrum radios were two previous methods for making signals less visible to an eavesdropper. 

Although these approaches lower the RF footprint of transmissions, they are now vulnerable to detection, interception, and exploitation using wideband receivers and computer spectral analysis tools. Emerging technologies provide a variety of innovative approaches to achieve the same aim while improving security. 

The first is to use extremely directed ‘line of sight' signals that may be focused directly at the intended receiver, limiting an adversary's ability to even detect the broadcast. This might be accomplished, for example, by using tightly concentrated laser signals of various wavelengths that may be precisely directed at the desired recipient's antenna when geography allow. 

A space-based relay, in which two or more force components are linked by laser communication channels with a constellation of satellites, which are connected by secure links (see the following section for examples of ongoing work in that field), offers a difficult-to-intercept communications path. 

As a consequence, data might be sent with far less chance of being intercepted than RF signals. The distances between connecting parties are virtually unlimited for a satellite system with a worldwide footprint for its uplinks and downlinks. Moving radio signals to wavelengths that do not travel over long distances due to atmospheric absorption, but still give effective communications capabilities at small ranges, is a second strategy that is better suited to force elements in close proximity. 

The US Army, for example, is doing research on deep ultraviolet communications (UVC). 5 UVC has the following benefits over radio frequencies such as UHF and VHF: 

• the higher frequency enables for faster data transfer

• very low-powered signals can still be received over short distances

• signal strength rapidly drops off over a critical distance 

Communications with a low chance of being detected. One technique to keep communications safe from an enemy is to never send them over routes that can be detected or intercepted. 

For mobile force units, this isn't always practicable, but when it is, communications security may be quite effective. The German army curtailed its radio transmissions in the run-up to its Ardennes operation in December 1944, depending instead on couriers and landlines operating within the region it held (which was contiguous with Germany, so that command and control traffic could mostly be kept off the airwaves). 

The build-up of considerable German forces was overlooked by Allied intelligence, which had been lulled into complacency by having routinely forewarned of German moves via intercepted radio communications. 

Even today, when fiber-optic connections can transmit data at far greater rates than copper connections, the option to go "off air" when circumstances allow is still valuable. Of course, mobile troops will not always have the luxury of transferring all traffic onto cables, especially in high-speed scenarios, but there are still techniques to substantially minimize the footprint of communication signals and, in some cases, render them effectively undetectable. 

Frequency-hopping and spread-spectrum radios were two previous methods for making signals less visible to an eavesdropper. 

Although these approaches lower the RF footprint of transmissions, they are now vulnerable to detection, interception, and exploitation using wideband receivers and computer spectral analysis tools. Emerging technologies provide a variety of innovative approaches to achieve the same aim while improving security. 

The first is to use extremely directed ‘line of sight' signals that may be focused directly at the intended receiver, limiting an adversary's ability to even detect the broadcast. 

This might be accomplished, for example, by using tightly concentrated laser signals of various wavelengths that may be precisely directed at the desired recipient's antenna when geography allow. 

A space-based relay, in which two or more force components are linked by laser communication channels with a constellation of satellites, which are connected by secure links (see the following section for examples of ongoing work in that field), offers a difficult-to-intercept communications path. 

As a consequence, data might be sent with far less chance of being intercepted than RF signals. The distances between connecting parties are virtually unlimited for a satellite system with a worldwide footprint for its uplinks and downlinks. 

Moving radio signals to wavelengths that do not travel over long distances due to atmospheric absorption, but still give effective communications capabilities at small ranges, is a second strategy that is better suited to force elements in close proximity. 

The US Army, for example, is doing research on deep ultraviolet communications (UVC). 5 UVC has the following benefits over radio frequencies such as UHF and VHF: 

• the higher frequency allows for faster data transfer 

• very low-powered signals can still be heard over short distances 

• there is a quick drop-off in signal strength at a critical distance

Artificial Intelligence - What Is Swarm Intelligence and Distributed Intelligence?

From developing single autonomous agents to building groups of distributed autonomous agents that coordinate themselves, distributed intelligence is the obvious next step.

A multi-agent system is made up of many agents.

Communication is a prerequisite for cooperation.

The fundamental concept is to allow for distributed problem-solving rather than employing a collection of agents as a simple parallelization of the single-agent technique.

Agents effectively cooperate, exchange information, and assign duties to one another.

Sensor data, for example, is exchanged to learn about the current condition of the environment, and an agent is given a task based on who is in the best position to complete that job at the time.

Agents might be software or embodied agents in the form of robots, resulting in a multi-robot system.

RoboCup Soccer (Kitano et al.1997) is an example of this, in which two teams of robots compete in soccer.

Typical challenges include detecting the ball cooperatively and sharing that knowledge, as well as assigning tasks, such as who will go after the ball next.

Agents may have a complete global perspective or simply a partial picture of the surroundings.

The agent's and the entire approach's complexity may be reduced by restricting information to the local area.

Regardless of their local perspective, agents may communicate, disseminate, and transmit information across the agent group, resulting in a distributed collective vision of global situations.

A scalable decentralized system, a non-scalable decentralized system, and a decentralized system are three separate concepts of distributed intelligence that may be used to construct distributed intelligence.

Without a master-slave hierarchy or a central control element, all agents in scalable decentralized systems function in equal roles.

Because the system only allows for local agent-to-agent communication, there is no need for all agents to coordinate with each other.

This allows for potentially huge system sizes.

All-to-all communication is an important aspect of the coordination mechanism in non-scalable decentralized systems, but it may become a bottleneck in systems with too many agents.

A typical RoboCup-Soccer system, for example, requires all robots to cooperate with all other robots at all times.

Finally, in decentralized systems with central components, the agents may interact with one another through a central server (e.g., cloud) or be coordinated by a central control.

It is feasible to mix the decentralized and central approaches by delegating basic tasks to the agents, who will complete them independently and locally, while more difficult activities will be managed centrally.

Vehicle ad hoc networks are an example of a use case (Liang et al.2015).

Each agent is self-contained, yet collaboration aids in traffic coordination.

For example, intelligent automobiles may build dynamic multi-hop networks to notify others about an accident that is still hidden from view.

For a safer and more efficient traffic flow, cars may coordinate passing moves.

All of this may be accomplished by worldwide communication with a central server or, depending on the stability of the connection, through local car-to-car communication.

Natural swarm systems and artificial, designed distributed systems are combined in swarm intelligence research.

Extracting fundamental principles from decentralized biological systems and translating them into design principles for decentralized engineering systems is a core notion in swarm intelligence (scalable decentralized systems as defined above).

Swarm intelligence was inspired by flocks, swarms, and herds' collective activities.

Social insects such as ants, honeybees, wasps, and termites are a good example.

These swarm systems are built on self-organization and work in a fundamentally decentralized manner.

Crystallization, pattern creation in embryology, and synchronization in swarms are examples of self-organization, which is a complex interaction of positive (deviations are encouraged) and negative feedback (deviations are damped).

In swarm intelligence, four key features of systems are investigated: • The system is made up of a large number of autonomous agents that are homogeneous in terms of their capabilities and behaviors.

• Each agent follows a set of relatively simple rules compared to the task's complexity.

• The resulting system behavior is heavily reliant on agent interaction and collaboration.

Reynolds (1987) produced a seminal paper detailing flocking behavior in birds based on three basic local rules: alignment (align direction of movement with neighbors), cohesiveness (remain near to your neighbors), and separation (stay away from your neighbors) (keep a minimal distance to any agent).

As a consequence, a real-life mimicked self-organizing flocking behavior emerges.

By depending only on local interactions between agents, a high level of resilience may be achieved.

Any agent, at any moment, has only a limited understanding of the system's global state (swarm-level state) and relies on communication with nearby agents to complete its duty.

Because the swarm's knowledge is spread, a single point of failure is rare.

An perfectly homogenous swarm has a high degree of redundancy; that is, all agents have the same capabilities and can therefore be replaced by any other.

By depending only on local interactions between agents, a high level of scalability may be obtained.

Due to the dispersed data storage architecture, there is less requirement to synchronize or maintain data coherent.

Because the communication and coordination overhead for each agent is dictated by the size of its neighborhood, the same algorithms may be employed for systems of nearly any scale.

Ant Colony Optimization (ACO) and Particle Swarm Optimization are two well-known examples of swarm intelligence in engineered systems from the optimization discipline (PSO).

Both are metaheuristics, which means they may be used to solve a wide range of optimization problems.

Ants and their use of pheromones to locate the shortest pathways inspired ACO.

A graph must be used to depict the optimization issue.

A swarm of virtual ants travels from node to node, choosing which edge to use next based on the likelihood of how many other ants have used it before (through pheromone, implementing positive feedback) and a heuristic parameter, such as journey length (greedy search).

Evaporation of pheromones balances the exploration-exploitation trade-off (negative feedback).

The traveling salesman dilemma, automobile routing, and network routing are all examples of ACO applications.

Flocking is a source of inspiration for PSO.

Agents navigate search space using average velocity vectors that are impacted by global and local best-known solutions (positive feedback), the agent's past path, and a random direction.

While both ACO and PSO conceptually function in a completely distributed manner, they do not need parallel computing to be deployed.

They may, however, be parallelized with ease.

Swarm robotics is the application of swarm intelligence to embodied systems, while ACO and PSO are software-based methods.

Swarm robotics applies the concept of self-organizing systems based on local information to multi-robot systems with a high degree of resilience and scalability.

Following the example of social insects, the goal is to make each individual robot relatively basic in comparison to the task complexity while yet allowing them to collaborate to perform complicated problems.

A swarm robot can only communicate with other swarm robots since it can only function on local information.

Given a fixed swarm density, the applied control algorithms are meant to allow maximum scalability (i.e., constant number of robots per area).

The same control methods should perform effectively regardless of the system size whether the swarm size is grown or lowered by adding or deleting robots.

A super-linear performance improvement is often found, meaning that doubling the size of the swarm improves the swarm's performance by more than two.

As a result, each robot is more productive than previously.

Swarm robotics systems have been demonstrated to be effective for a wide range of activities, including aggregation and dispersion behaviors, as well as more complicated tasks like item sorting, foraging, collective transport, and collective decision-making.

Rubenstein et al. (2014) conducted the biggest scientific experiment using swarm robots to date, using 1024 miniature mobile robots to mimic self-assembly behavior by arranging the robots in predefined designs.

The majority of the tests were conducted in the lab, but new research has taken swarm robots to the field.

Duarte et al. (2016), for example, built a swarm of autonomous surface watercraft that cruise the ocean together.

Modeling the relationship between individual behavior and swarm behavior, creating advanced design principles, and deriving assurances of system attributes are all major issues in swarm intelligence.

The micro-macro issue is defined as the challenge of identifying the ensuing swarm behavior based on a given individual behavior and vice versa.

It has shown to be a difficult challenge that manifests itself in both mathematical modeling and the robot controller design process as an engineering difficulty.

The creation of complex tactics to design swarm behavior is not only crucial to swarm intelligence research, but it has also proved to be very difficult.

Similarly, due to the combinatorial explosion of action-to-agent assignments, multi-agent learning and evolutionary swarm robotics (i.e., application of evolutionary computation techniques to swarm robotics) do not scale well with task complexity.

Despite the benefits of robustness and scalability, obtaining strong guarantees for swarm intelligence systems is challenging.

Swarm systems' availability and reliability can only be assessed experimentally in general. 

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

See also: 

AI and Embodiment.

Further Reading:

Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. 1999. Swarm Intelligence: From Natural to Artificial System. New York: Oxford University Press.

Duarte, Miguel, Vasco Costa, Jorge Gomes, Tiago Rodrigues, Fernando Silva, Sancho Moura Oliveira, Anders Lyhne Christensen. 2016. “Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots.” PloS One 11, no. 3: e0151834.

Hamann, Heiko. 2018. Swarm Robotics: A Formal Approach. New York: Springer.

Kitano, Hiroaki, Minoru Asada, Yasuo Kuniyoshi, Itsuki Noda, Eiichi Osawa, Hitoshi Matsubara. 1997. “RoboCup: A Challenge Problem for AI.” AI Magazine 18, no. 1: 73–85.

Liang, Wenshuang, Zhuorong Li, Hongyang Zhang, Shenling Wang, Rongfang Bie. 2015. “Vehicular Ad Hoc Networks: Architectures, Research Issues, Methodologies, Challenges, and Trends.” International Journal of Distributed Sensor Networks 11, no. 8: 1–11.

Reynolds, Craig W. 1987. “Flocks, Herds, and Schools: A Distributed Behavioral Model.” Computer Graphics 21, no. 4 (July): 25–34.

Rubenstein, Michael, Alejandro Cornejo, and Radhika Nagpal. 2014. “Programmable Self-Assembly in a Thousand-Robot Swarm.” Science 345, no. 6198: 795–99.

Quantum Cryptography

The Holy Grail of Data Security 

Let's take a closer look at the second item on the list: quantum cryptography. In today's society, data security is a problem that has grown more crucial. 

How can we be sure that no one else has access to our personal digital information? 

Or that third parties don't listen in on our discussions without our knowledge? 

Traditional encryption encrypts a communication with a key code in such a way that decrypting it without knowing the key would demand unreasonably large processing power. But it's like a never-ending competition to build ever-more sophisticated encryption methods that can't be cracked by ever-more powerful computers. 

At least for the dilemma of the unidentified eavesdropper, quantum cryptography offers a solution.

  Quantum key distribution is a critical component of quantum-secure communication: by conveying the key using entangled quantum states of light, any interference in the transmission, such as an eavesdropper in the communication channel, is immediately observable by the user. 

• Assume A makes a “secure” phone call to B. (in quantum cryptography, A and B are always taken to stand for Alice and Bob). 
• Both Alice's and Bob's equipment are capable of measuring entangled particles. 
• When the line is intercepted, Alice and Bob quickly recognize that an undesirable third party (commonly referred to as Eve) is present, because Eve would irreversibly disrupt the entanglement of the particles while listening in, i.e., measuring it for that reason. 
• She also can't just copy them and transfer the information, the qubit, to the intended recipient without being caught, because it's impossible to duplicate any (yet-to-be-measured) quantum state exactly. 
• As soon as Alice and Bob observe any changes to their key, or that the entanglement of their particles has been broken, they alter the method of communication and, at least temporarily, prevent the eavesdropper. 

Cryptography relies on a fundamental fact of quantum mechanics: quantum states may never be replicated without affecting the matching state or original information. 

Engineers are currently striving to utilize the odd qualities of the micro universe, which caused so much consternation among physicists in the early part of the twentieth century. 

Physicists went back to the theoretical drawing board during the creation of the first generation of quantum technologies to achieve a proper understanding of the principles that govern the micro universe. Meanwhile, they have made great progress in their efforts. 

Quantum physics and all of its main aspects may now be applied in a technology environment. The fascinating aspect of this approach is that scientists and engineers are working on a whole new universe of possibilities that have never been conceived before, rather than just attempting to make current and familiar things quicker or more exact. 

“The nineteenth century was known as the machine era, the twentieth century will go down in history as the information era,” wrote physicist Paul Davies in 1997. The quantum age, I believe, will begin in the twenty-first century.”

You may also want to read more about Quantum Computing here.

Quantum Cryptography - What Is Quantum Cryptography? How Does It Work?

Quantum cryptography makes use of unique quantum characteristics of nature to complete a cryptographic job. 

Most quantum cryptography algorithms are information theoretically safe (at least in theory), which is a very strong concept of security since it is derived only from information theory. 

Early attempts to utilize quantum characteristics for security reasons may be traced back to the 1970s, when Wiesner attempted to produce unfalsifiable bank notes. 

However, these concepts seemed to be impractical, since they required the storage of a single polarized photon for days without loss (at the time, photon polarization was the only conceived carrier of quantum information). 

Bennett and Brassard made the breakthrough in 1983, when they discovered that photons are better utilized to convey quantum information rather than to store it. 

• They might, for example, be used to convey a random secret key from a sender to a recipient, who would then be able to encrypt and decode sensitive communications using the key. 

• Bennett and Brassard released the first quantum key distribution (QKD) protocol, dubbed the BB84 protocol, shortly after. 

A QKD protocol allows two parties to create a shared secret key using an unsecured quantum channel and a public classical channel that has been authenticated. 

• Since then, a slew of new protocols have been suggested – and implemented – propelling QKD to the forefront of quantum cryptography and one of the most important applications of quantum information science. 

• Furthermore, driven by growing concerns about data security and the possibility of commercialization, quantum cryptography research has drawn the interest of a number of businesses, private organizations, and governments.

 

In reality, quantum cryptography solutions are being offered by an increasing number of businesses and startups across the globe. 

• In the long run, scientists want to build large-scale quantum networks that will allow safe communication between any subset of users in the network due to quantum entanglement. 

• In a wider sense, similar networks may be connected together to form a quantum internet, which could be used for much more than secure communication, such as safe access to distant quantum computers. 

Quantum cryptography elegantly integrates concepts and contributions from a variety of disciplines, including quantum information and quantum communication, as well as computer science and conventional encryption. 

• The interaction of these disparate disciplines leads to theoretical breakthroughs that are of wide interest and transferable to other areas of study. 

• However, since quantum cryptography, and in particular QKD, has a considerable economic appeal, ongoing research is also driven by more practical goals. 

For example, combined theoretical and practical efforts are continuously dedicated to: improving the key-generation rates, simplifying the experimental setups, and so on by focusing on an unique QKD protocol that has lately garnered a lot of attention from the scientific community and is widely regarded as the new standard for long-distance QKD in fiber. 

Twinfield (TF) QKD is a technique that enables two parties to create a secret key across vast distances using single-photon interferometric measurements in an intermediary relay. 

• In this context, we use current theoretical findings and simulations to examine practical TF-QKD implementations in depth. 

• With bipartite QKD connections becoming the norm at many research institutions and field deployments across the globe, the next major step would be to join these isolated links into quantum networks to conduct more complex multi-user activities. 

• The extension of QKD to many users using multipartite QKD, also known as quantum conference key agreement (CKA), is undoubtedly a logical application of future quantum networks. 

When a confidential communication has to be securely broadcast among a group of users, the CKA protocol is used. 

• The users share a shared secret key—the conference key—with which they may encrypt and decode the secret message when they utilize the CKA protocol. 

In this section, CKA plays a significant part. 

• We provide an understandable description of CKA's evolution from current QKD protocols to expose the reader to it. 

• We extend QKD's security architecture to incorporate CKA and concentrate on a multipartite variant of the widely used BB84 protocol. 

• We also go through some of the most recent experimental implementations of CKA protocols, with a focus on the multipartite BB84 protocol. 

• We describe a new CKA technique based on the TF-QKD operating principle, in which several users distil a conference key via single-photon interference events. 

• We demonstrate that the protocol outperforms prior CKA schemes over long distances thanks to this feature, since it uses a W-class state as its entanglement resource instead of the traditional GHZ state.

~ Jai Krishna Ponnappan

You may also want to read more about Quantum Computing here.

Artificial Intelligence - Who Is Sherry Turkle?

 

 

 

Sherry Turkle(1948–) has a background in sociology and psychology, and her work focuses on the human-technology interaction.

While her study in the 1980s focused on how technology affects people's thinking, her work in the 2000s has become more critical of how technology is utilized at the expense of building and maintaining meaningful interpersonal connections.

She has employed artificial intelligence in products like children's toys and pets for the elderly to highlight what people lose out on when interacting with such things.

Turkle has been at the vanguard of AI breakthroughs as a professor at the Massachusetts Institute of Technology (MIT) and the creator of the MIT Initiative on Technology and the Self.

She highlights a conceptual change in the understanding of AI that occurs between the 1960s and 1980s in Life on the Screen: Identity inthe Age of the Internet (1995), substantially changing the way humans connect to and interact with AI.

She claims that early AI paradigms depended on extensive preprogramming and employed a rule-based concept of intelligence.

However, this viewpoint has given place to one that considers intelligence to be emergent.

This emergent paradigm, which became the recognized mainstream view by 1990, claims that AI arises from a much simpler set of learning algorithms.

The emergent method, according to Turkle, aims to emulate the way the human brain functions, assisting in the breaking down of barriers between computers and nature, and more generally between the natural and the artificial.

In summary, an emergent approach to AI allows people to connect to the technology more easily, even thinking of AI-based programs and gadgets as children.

Not just for the area of AI, but also for Turkle's study and writing on the subject, the rising acceptance of the emerging paradigm of AI and the enhanced relatability it heralds represents a significant turning point.

Turkle started to employ ethnographic research techniques to study the relationship between humans and their gadgets in two edited collections, Evocative Objects: Things We Think With (2007) and The Inner History of Devices (2008).

She emphasized in her book The Inner History of Devices that her intimate ethnography, or the ability to "listen with a third ear," is required to go past the advertising-based clichés that are often employed when addressing technology.

This method comprises setting up time for silent meditation so that participants may think thoroughly about their interactions with their equipment.

Turkle used similar intimate ethnographic approaches in her second major book, Alone Together: 

Why We Expect More from Technology and Less from Each Other (2011), to argue that the increasing connection between people and the technology they use is harmful.

These issues are connected to the increased usage of social media as a form of communication, as well as the continuous degree of familiarity and relatability with technology gadgets, which stems from the emerging AI paradigm that has become practically omnipresent.

She traced the origins of the dilemma back to early pioneers in the field of cybernetics, citing, for example, Norbert Weiner's speculations on the idea of transmitting a human person across a telegraph line in his book God & Golem, Inc.(1964).

Because it reduces both people and technology to information, this approach to cybernetic thinking blurs the barriers between them.

In terms of AI, this implies that it doesn't matter whether the machines with which we interact are really intelligent.

Turkle claims that by engaging with and caring for these technologies, we may deceive ourselves into feeling we are in a relationship, causing us to treat them as if they were sentient.

In a 2006 presentation titled "Artificial Intelligence at 50: From Building Intelligence to Nurturing Sociabilities" at the Dartmouth Artificial Intelligence Conference, she recognized this trend.

She identified the 1997 Tamagotchi, 1998 Furby, and 2000 MyReal Baby as early versions of what she refers to as relational artifacts, which are more broadly referred to as social machines in the literature.

The main difference between these devices and previous children's toys is that these devices come pre-animated and ready for a relationship, whereas previous children's toys required children to project a relationship onto them.

Turkle argues that this change is about our human weaknesses as much as it is about computer capabilities.

In other words, just caring for an item increases the likelihood of not only seeing it as intelligent but also feeling a connection to it.

This sense of connection is more relevant to the typical person engaging with these technologies than abstract philosophic considerations concerning the nature of their intelligence.

Turkle delves more into the ramifications of people engaging with AI-based technologies in both Alone Together and Reclaiming Conversation: The Power of Talk in a Digital Age (2015).

She provides the example of Adam in Alone Together, who appreciates the appreciation of the AI bots he controls over in the game Civilization.

Adam appreciates the fact that he is able to create something fresh when playing.

Turkle, on the other hand, is skeptical of this interaction, stating that Adam's playing isn't actual creation, but rather the sensation of creation, and that it's problematic since it lacks meaningful pressure or danger.

In Reclaiming Conversation, she expands on this point, suggesting that social partners simply provide a perception of camaraderie.

This is important because of the value of human connection and what may be lost in relationships that simply provide a sensation or perception of friendship rather than true friendship.

Turkle believes that this transition is critical.

She claims that although connections with AI-enabledtechnologies may have certain advantages, they pale in contrast to what is missing: 

• the complete complexity and inherent contradictions that define what it is to be human.

A person's connection with an AI-enabled technology is not as intricate as one's interaction with other individuals.

Turkle claims that as individuals have become more used to and dependent on technology gadgets, the definition of friendship has evolved.

• People's expectations for companionship have been simplified as a result of this transformation, and the advantages that one wants to obtain from partnerships have been reduced.

• People now tend to associate friendship only with the concept of interaction, ignoring the more nuanced sentiments and arguments that are typical in partnerships.

• By engaging with gadgets, one may form a relationship with them.

• Conversations between humans have become merely transactional as human communication has shifted away from face-to-face conversation and toward interaction mediated by devices. 

In other words, the most that can be anticipated is engagement.

Turkle, who has a background in psychoanalysis, claims that this kind of transactional communication allows users to spend less time learning to view the world through the eyes of another person, which is a crucial ability for empathy.

Turkle argues we are in a robotic period in which people yearn for, and in some circumstances prefer, AI-based robotic companionship over that of other humans, drawing together these numerous streams of argument.

For example, some people enjoy conversing with their iPhone's Siri virtual assistant because they aren't afraid of being judged by it, as evidenced by a series of Siri commercials featuring celebrities talking to their phones.

Turkle has a problem with this because these devices can only respond as if they understand what is being said.

AI-based gadgets, on the other hand, are confined to comprehending the literal meanings of data stored on the device.

They can decipher the contents of phone calendars and emails, but they have no idea what any of this data means to the user.

There is no discernible difference between a calendar appointment for car maintenance and one for chemotherapy for an AI-based device.

A person may lose sight of what it is to have an authentic dialogue with another human when entangled in a variety of these robotic connections with a growing number of technologies.

While Reclaiming Communication documents deteriorating conversation skills and decreasing empathy, it ultimately ends on a positive note.

Because people are becoming increasingly dissatisfied with their relationships, there may be a chance for face-to-face human communication to reclaim its vital role.

Turkle's ideas focus on reducing the amount of time people spend on their phones, but AI's involvement in this interaction is equally critical.

• Users must accept that their virtual assistant connections will never be able to replace face-to-face interactions.

• This will necessitate being more deliberate in how one uses devices, prioritizing in-person interactions over the faster and easier interactions provided by AI-enabled devices.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Blade Runner; Chatbots and Loebner Prize; ELIZA; General and Narrow AI; Moral Turing Test; PARRY; Turing, Alan; 2001: A Space Odyssey.

References And Further Reading

• Haugeland, John. 1997. “What Is Mind Design?” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 1–28. Cambridge, MA: MIT Press.

• Searle, John R. 1997. “Minds, Brains, and Programs.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 183–204. Cambridge, MA: MIT Press.

• Turing, A. M. 1997. “Computing Machinery and Intelligence.” Mind Design II: Philosophy, Psychology, Artificial Intelligence, edited by John Haugeland, 29–56. Cambridge, MA: MIT Press.