Monthly Archives: March 2022

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Fetch.ai is West Ham Uniteds exclusive official artificial intelligence partner and the premier leagues giant non-exclusive Official Global Partner. Under the deal, Fetch.ai has also been designated as West Ham United Womens football clubs non-exclusive official partner. Through this partnership, Fetch.ai and West Ham United will leverage and promote the impact of artificial intelligence in enhancing businesses and daily lives. Fetch.ai Brand to be Displayed in West Ham United LEDs Subsequently, West Ham United will promote the Fetch.ai brand and its products in their mega London Stadium on their LED perimeter advertising boards and displays, marketing Fetch.ais smart parking concept, upcoming social media platform, and future smart solutions. West Ham Uniteds London Stadium at the Queen Elizabeth Olympic Park has a capacity of 67,000 fans. It is larger than Tottenham Hotspurs 1 billion stadium. In London, the West Ham Uniteds mega stadium is only second after Wembley and Twickenham stadiums. Nathan Thompson, the Commercial Director of West Ham United, said he was delighted with the partnership. We are delighted to announce our first Official Artificial Intelligence Partner and welcome Fetch.ai to the Club at an exciting time for the business, and the industry. Were looking forward to working with Fetch.ai on their smart parking concept, social media platform, and upcoming projects that will provide smart solutions for fans. Using Artificial Intelligence to Drive Crypto Solutions The developers of Fetch.ai are firm believers that smart contracts can, as their name implies, be smart. Fetch.ai integrates artificial intelligence and machine learning for the building and deployment of smart code to deliver enhanced service delivery for users, businesses, and organizations. Through their secure and decentralized blockchain, Fetch.ai can securely launch their Autonomous Economic Agents (AEA)representing connected devices, users, or organizationsand act on their behalf on the Fetch.ai network. These agents depend on artificial intelligence and are created as digital citizens. They are tasked with securely and instantaneously connecting to vast data sources and hardware environments, effectively eliminating the need for aggregators. Therefore, by using artificial intelligence solutions in creative ways, the founder of Fetch.ai, Humayun Sheikh, believes it will power the future of world-class Premier League football for fans in the U.K. and worldwide.

Fetch.ai is West Ham Uniteds exclusive official artificial intelligence partner and the premier leagues giant non-exclusive Official Global Partner. Under the deal, Fetch.ai has also been designated as West Ham United Womens football clubs non-exclusive official partner. Through this partnership, Fetch.ai and West Ham United will leverage and promote the impact of artificial intelligence in enhancing businesses and daily lives.

Fetch.ai Brand to be Displayed in West Ham United LEDs

Subsequently, West Ham United will promote the Fetch.ai brand and its products in their mega London Stadium on their LED perimeter advertising boards and displays, marketing Fetch.ais smart parking concept, upcoming social media platform, and future smart solutions.

West Ham Uniteds London Stadium at the Queen Elizabeth Olympic Park has a capacity of 67,000 fans. It is larger than Tottenham Hotspurs 1 billion stadium. In London, the West Ham Uniteds mega stadium is only second after Wembley and Twickenham stadiums.

Nathan Thompson, the Commercial Director of West Ham United, said he was delighted with the partnership.

We are delighted to announce our first Official Artificial Intelligence Partner and welcome Fetch.ai to the Club at an exciting time for the business, and the industry. Were looking forward to working with Fetch.ai on their smart parking concept, social media platform, and upcoming projects that will provide smart solutions for fans.

Using Artificial Intelligence to Drive Crypto Solutions

The developers of Fetch.ai are firm believers that smart contracts can, as their name implies, be smart. Fetch.ai integrates artificial intelligence and machine learning for the building and deployment of smart code to deliver enhanced service delivery for users, businesses, and organizations.

Through their secure and decentralized blockchain, Fetch.ai can securely launch their Autonomous Economic Agents (AEA)representing connected devices, users, or organizationsand act on their behalf on the Fetch.ai network. These agents depend on artificial intelligence and are created as digital citizens.

They are tasked with securely and instantaneously connecting to vast data sources and hardware environments, effectively eliminating the need for aggregators. Therefore, by using artificial intelligence solutions in creative ways, the founder of Fetch.ai, Humayun Sheikh, believes it will power the future of world-class Premier League football for fans in the U.K. and worldwide.

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West Ham United Announces Fetch.ai as their Official Artificial Intelligence Partner - Geeks World Wide

A biodiversity simulation framework

We have developed a simulation framework modelling biodiversity loss to optimize and validate conservation policies (in this context, decisions about data gathering and area protection across a landscape) using an RL algorithm. We implemented a spatially explicit individual-based simulation to assess future biodiversity changes based on natural processes of mortality, replacement and dispersal. Our framework also incorporates anthropogenic processes such as habitat modifications, selective removal of a species, rapid climate change and existing conservation efforts. The simulation can include thousands of species and millions of individuals and track population sizes and species distributions and how they are affected by anthropogenic activity and climate change (for a detailed description of the model and its parameters see Supplementary Methods and Supplementary Table 1).

In our model, anthropogenic disturbance has the effect of altering the natural mortality rates on a species-specific level, which depends on the sensitivity of the species. It also affects the total number of individuals (the carrying capacity) of any species that can inhabit a spatial unit. Because sensitivity to disturbance differs among species, the relative abundance of species in each cell changes after adding disturbance and upon reaching the new equilibrium. The effect of climate change is modelled as locally affecting the mortality of individuals based on species-specific climatic tolerances. As a result, more tolerant or warmer-adapted species will tend to replace sensitive species in a warming environment, thus inducing range shifts, contraction or expansion across species depending on their climatic tolerance and dispersal ability.

We use time-forward simulations of biodiversity in time and space, with increasing anthropogenic disturbance through time, to optimize conservation policies and assess their performance. Along with a representation of the natural and anthropogenic evolution of the system, our framework includes an agent (that is, the policy maker) taking two types of actions: (1) monitoring, which provides information about the current state of biodiversity of the system, and (2) protecting, which uses that information to select areas for protection from anthropogenic disturbance. The monitoring policy defines the level of detail and temporal resolution of biodiversity surveys. At a minimal level, these include species lists for each cell, whereas more detailed surveys provide counts of population size for each species. The protection policy is informed by the results of monitoring and selects protected areas in which further anthropogenic disturbance is maintained at an arbitrarily low value (Fig. 1). Because the total number of areas that can be protected is limited by a finite budget, we use an RL algorithm42 to optimize how to perform the protecting actions based on the information provided by monitoring, such that it minimizes species loss or other criteria depending on the policy.

We provide a full description of the simulation system in the Supplementary Methods. In the sections below we present the optimization algorithm, describe the experiments carried out to validate our framework and demonstrate its use with an empirical dataset.

In our model we use RL to optimize a conservation policy under a predefined policy objective (for example, to minimize the loss of biodiversity or maximize the extent of protected area). The CAPTAIN framework includes a space of actions, namely monitoring and protecting, that are optimized to maximize a reward R. The reward defines the optimality criterion of the simulation and can be quantified as the cumulative value of species that do not go extinct throughout the timeframe evaluated in the simulation. If the value is set equal across all species, the RL algorithm will minimize overall species extinctions. However, different definitions of value can be used to minimize loss based on evolutionary distinctiveness of species (for example, minimizing phylogenetic diversity loss), or their ecosystem or economic value. Alternatively, the reward can be set equal to the amount of protected area, in which case the RL algorithm maximizes the number of cells protected from disturbance, regardless of which species occur there. The amount of area that can be protected through the protecting action is determined by a budget Bt and by the cost of protection ({C}_{t}^{c}), which can vary across cells c and through time t.

The granularity of monitoring and protecting actions is based on spatial units that may include one or more cells and which we define as the protection units. In our system, protection units are adjacent, non-overlapping areas of equal size (Fig. 1) that can be protected at a cost that cumulates the costs of all cells included in the unit.

The monitoring action collects information within each protection unit about the state of the system St, which includes species abundances and geographic distribution:

$${S}_{t}={{{{H}}}_{{{t}}},{{{D}}}_{{{t}}},{{{F}}}_{{{t}}},{{{T}}}_{{{t}}},{{{C}}}_{{{t}}},{{{P}}}_{{{t}}},{B}_{t}}$$

(1)

where Ht is the matrix with the number of individuals across species and cells, Dt and Ft are matrices describing anthropogenic disturbance on the system, Tt is a matrix quantifying climate, Ct is the cost matrix, Pt is the current protection matrix and Bt is the available budget (for more details see Supplementary Methods and Supplementary Table 1). We define as feature extraction the result of a function X(St), which returns for each protection unit a set of features summarizing the state of the system in the unit. The number and selection of features (Supplementary Methods and Supplementary Table 2) depends on the monitoring policy X, which is decided a priori in the simulation. A predefined monitoring policy also determines the temporal frequency of this action throughout the simulation, for example, only at the first time step or repeated at each time step. The features extracted for each unit represent the input upon which a protecting action can take place, if the budget allows for it, following a protection policy Y. These features (listed in Supplementary Table 2) include the number of species that are not already protected in other units, the number of rare species and the cost of the unit relative to the remaining budget. Different subsets of these features are used depending on the monitoring policy and on the optimality criterion of the protection policy Y.

We do not assume species-specific sensitivities to disturbance (parameters ds, fs in Supplementary Table 1 and Supplementary Methods) to be known features, because a precise estimation of these parameters in an empirical case would require targeted experiments, which we consider unfeasible across a large number of species. Instead, species-specific sensitivities can be learned from the system through the observation of changes in the relative abundances of species (x3 in Supplementary Table 2). The features tested across different policies are specified in the subsection Experiments below and in the Supplementary Methods.

The protecting action selects a protection unit and resets the disturbance in the included cells to an arbitrarily low level. A protected unit is also immune from future anthropogenic disturbance increases, but protection does not prevent climate change in the unit. The model can include a buffer area along the perimeter of a protected unit, in which the level of protection is lower than in the centre, to mimic the generally negative edge effects in protected areas (for example, higher vulnerability to extreme weather). Although protecting a disturbed area theoretically allows it to return to its initial biodiversity levels, population growth and species composition of the protected area will still be controlled by the deathreplacementdispersal processes described above, as well as by the state of neighbouring areas. Thus, protecting an area that has already undergone biodiversity loss may not result in the restoration of its original biodiversity levels.

The protecting action has a cost determined by the cumulative cost of all cells in the selected protection unit. The cost of protection can be set equal across all cells and constant through time. Alternatively, it can be defined as a function of the current level of anthropogenic disturbance in the cell. The cost of each protecting action is taken from a predetermined finite budget and a unit can be protected only if the remaining budget allows it.

We frame the optimization problem as a stochastic control problem where the state of the system St evolves through time as described in the section above (see also Supplementary Methods), but it is also influenced by a set of discrete actions determined by the protection policy Y. The protection policy is a probabilistic policy: for a given set of policy parameters and an input state, the policy outputs an array of probabilities associated with all possible protecting actions. While optimizing the model, we extract actions according to the probabilities produced by the policy to make sure that we explore the space of actions. When we run experiments with a fixed policy instead, we choose the action with highest probability. The input state is transformed by the feature extraction function X(St) defined by the monitoring policy, and the features are mapped to a probability through a neural network with the architecture described below.

In our simulations, we fix monitoring policy X, thus predefining the frequency of monitoring (for example, at each time step or only at the first time step) and the amount of information produced by X(St), and we optimize Y, which determines how to best use the available budget to maximize the reward. Each action A has a cost, defined by the function Cost(A, St), which here we set to zero for the monitoring action (X) across all monitoring policies. The cost of the protecting action (Y) is instead set to the cumulative cost of all cells in the selected protection unit. In the simulations presented here, unless otherwise specified, the protection policy can only add one protected unit at each time step, if the budget allows, that is if Cost(Y, St)

The protection policy is parametrized as a feed-forward neural network with a hidden layer using a rectified linear unit (ReLU) activation function (Eq. (3)) and an output layer using a softmax function (Eq. (5)). The input of the neural network is a matrix x of J features extracted through the most recent monitoring across U protection units. The output, of size U, is a vector of probabilities, which provides the basis to select a unit for protection. Given a number of nodes L, the hidden layer h(1) is a matrix UL:

$${h}_{u{l}}^{(1)}=gleft(mathop{sum}limits_{j =1}^{J}{x}_{uj}{W}_{j{l}}^{(1)}right)$$

(2)

where u {1, , U} identifies the protection unit, l {1, , L} indicates the hidden nodes and j {1, , J} the features and where

is the ReLU activation function. We indicate with W(1) the matrix of J L coefficients (shared among all protection units) that we are optimizing. Additional hidden layers can be added to the model between the input and the output layer. The output layer takes h(1) as input and gives an output vector of U variables:

$${h}_{u}^{(2)}=sigma left(mathop{sum}limits_{{l=1}}^{L}{h}_{u{l}}^{(1)}{W}_{{l}}^{(2)}right)$$

(4)

where is a softmax function:

$$sigma(x_i) = frac{exp(x_i)}{sum_u{exp(x_u)}}$$

(5)

We interpret the output vector of U variables as the probability of protecting the unit u.

This architecture implements parameter sharing across all protection units when connecting the input nodes to the hidden layer; this reduces the dimensionality of the problem at the cost of losing some spatial information, which we encode in the feature extraction function. The natural next step would be to use a convolutional layer to discover relevant shape and space features instead of using a feature extraction function. To define a baseline for comparisons in the experiments described below, we also define a random protection policy ({hat{pi }}), which sets a uniform probability to protect units that have not yet been protected. This policy does not include any trainable parameter and relies on feature x6 (an indicator variable for protected units; Supplementary Table 2) to randomly select the proposed unit for protection.

The optimization algorithm implemented in CAPTAIN optimizes the parameters of a neural network such that they maximize the expected reward resulting from the protecting actions. With this aim, we implemented a combination of standard algorithms using a genetic strategies algorithm43 and incorporating aspects of classical policy gradient methods such as an advantage function44. Specifically, our algorithm is an implementation of the Parallelized Evolution Strategies43, in which two phases are repeated across several iterations (hereafter, epochs) until convergence. In the first phase, the policy parameters are randomly perturbed and then evaluated by running one full episode of the environment, that is, a full simulation with the system evolving for a predefined number of steps. In the second phase, the results from different runs are combined and the parameters updated following a stochastic gradient estimate43. We performed several runs in parallel on different workers (for example, processing units) and aggregated the results before updating the parameters. To improve the convergence we followed the standard approach used in policy optimization algorithms44, where the parameter update is linked to an advantage function A as opposed to the return alone (Eq. (6)). Our advantage function measures the improvement of the running reward (weighted average of rewards across different epochs) with respect to the last reward. Thus, our algorithm optimizes a policy without the need to compute gradients and allowing for easy parallelization. Each epoch in our algorithm works as:

for every worker p do

({epsilon }_{p}leftarrow {{{mathcal{N}}}}(0,sigma )), with diagonal covariance and dimension W+M

for t=1,...,T do

RtRt1+rt(+p)

end for

end for

Raverage of RT across workers

ReR+(1)Re1

for every coefficient in W+M do

+A(Re, RT, )

end for

where ({mathcal{N}}) is a normal distribution and W + M is the number of parameters in the model (following the notation in Supplementary Table 1). We indicate with rt the reward at time t, with R the cumulative reward over T time steps. Re is the running average reward calculated as an exponential moving average where = 0.25 represents the degree of weighting decrease and Re1 is the running average reward at the previous epoch. =0.1 is a learning rate and A is an advantage function defined as the average of final reward increments with respect to the running average reward Re on every worker p weighted by the corresponding noise p:

$$A({R}_{e},{R}_{T},epsilon )=frac{1}{P}mathop{sum}limits_{p}({R}_{e}-{R}_{T}^{p}){epsilon }_{p}.$$

(6)

We used our CAPTAIN framework to explore the properties of our model and the effect of different policies through simulations. Specifically, we ran three sets of experiments. The first set aimed at assessing the effectiveness of different policies optimized to minimize species loss based on different monitoring strategies. We ran a second set of simulations to determine how policies optimized to minimize value loss or maximize the amount of protected area may impact species loss. Finally, we compared the performance of the CAPTAIN models against the state-of-the-art method for conservation planning (Marxan25). A detailed description of the settings we used in our experiments is provided in the Supplementary Methods. Additionally, all scripts used to run CAPTAIN and Marxan analyses are provided as Supplementary Information.

We analysed a recently published33 dataset of 1,517 tree species endemic to Madagascar, for which presence/absence data had been approximated through species distribution models across 22,394 units of 55km spanning the entire country (Supplementary Fig. 5a). Their analyses included a spatial quantification of threats affecting the local conservation of species and assumed the cost of each protection unit as proportional to its level of threat (Supplementary Fig. 5b), similarly to how our CAPTAIN framework models protection costs as proportional to anthropogenic disturbance.

We re-analysed these data within a limited budget, allowing for a maximum of 10% of the units with the lowest cost to be protected (that is, 2,239 units). This figure can actually be lower if the optimized solution includes units with higher cost. We did not include temporal dynamics in our analysis, instead choosing to simply monitor the system once to generate the features used by CAPTAIN and Marxan to place the protected units. Because the dataset did not include abundance data, the features only included species presence/absence information in each unit and the cost of the unit.

Because the presence of a species in the input data represents a theoretical expectation based on species distribution modelling, it does not consider the fact that strong anthropogenic pressure on a unit (for example, clearing a forest) might result in the local disappearance of some of the species. We therefore considered the potential effect of disturbance in the monitoring step. Specifically, in the absence of more detailed data about the actual presence or absence of species, we initialized the sensitivity of each species to anthropogenic disturbance as a random draw from a uniform distribution ({d}_{s} sim {{{mathcal{U}}}}(0,1)) and we modelled the presence of a species s in a unit c as a random draw from a binomial distribution with a parameter set equal to ({p}_{s}^{c}=1-{d}_{s}times {D}^{c}), where Dc[0, 1] is the disturbance (or threat sensu Carrasco et al.33) in the unit. Under this approach, most of the species expected to live in a unit are considered to be present if the unit is undisturbed. Conversely, many (especially sensitive) species are assumed to be absent from units with high anthropogenic disturbance. This resampled diversity was used for feature extraction in the monitoring steps (Fig. 1c). While this approach is an approximation of how species might respond to anthropogenic pressure, the use of additional empirical data on species-specific sensitivity to disturbance can provide a more realistic input in the CAPTAIN analysis.

We repeated this random resampling 50 times and analysed the resulting biodiversity data in CAPTAIN using the one-time protection model, trained through simulations in the experiments described in the previous section and in the Supplementary Methods. We note that it is possible, and perhaps desirable, in principle to train a new model specifically for this empirical dataset or at least fine-tune a model pretrained through simulations (a technique known as transfer learning), for instance, using historical time series and future projections of land use and climate change. Yet, our experiment shows that even a model trained solely using simulated datasets can be successfully applied to empirical data. Following Carrasco et al.33, we set as the target of our policy the protection of at least 10% of each species range. To achieve this in CAPTAIN, we modified the monitoring action such that a species is counted as protected only when at least 10% of its range falls within already protected units. We ran the CAPTAIN analysis for a single step, in which all protection units are established.

We analysed the same resampled datasets using Marxan with the initial budget used in the CAPTAIN analyses and under two configurations. First, we used a BLM (BLM=0.1) to penalize the establishment of non-adjacent protected units following the settings used in Carrasco et al.33. After some testing, as suggested in Marxans manual45, we set penalties on exceeding the budget, such that the cost of the optimized results indeed does not exceed the total budget (THRESHPEN1=500, THRESHPEN2=10). For each resampled dataset we ran 100 optimizations (with Marxan settings NUMITNS=1,000,000, STARTTEMP=1 and NUMTEMP=10,000 (ref. 45) and used the best of them as the final result. Second, because the BLM adds a constraint that does not have a direct equivalent in the CAPTAIN model, we also repeated the analyses without it (BLM=0) for comparison.

To assess the performance of CAPTAIN and compare it with that of Marxan, we computed the fraction of replicates in which the target was met for all species, the average number of species for which the target was missed and the number of protected units (Supplementary Table 4). We also calculated the fraction of each species range included in protected units to compare it with the target of 10% (Fig. 6c,d and Supplementary Fig. 6c,d). Finally, we calculated the frequency at which each unit was selected for protection across the 50 resampled datasets as a measure of its relative importance (priority) in the conservation plan.

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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Improving biodiversity protection through artificial intelligence - Nature.com

Artificial intelligence (AI) will add up to $10tn to the global economy in the next decade, Arvind Krishna, chairman and CEO of IBM has said.

Greater adoption of AI in the UAE could also add up to $200bn in productivity gains by 2030, Krishna told Omar bin Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy, and Teleworking Applications, at the World Government Summit 2022, official news agency WAM reported.

The leader of the US tech giant tipped AI to transform the world economy after warning that the planet lacks skilled people to keep up with the pandemic-induced disruption caused to workplaces everywhere.

I fundamentally believe that AI offers over $10tn of productivity to the world. If you think about GDP increase, this could be anywhere between 10, 20, or 30 per cent. But we have to do this carefully, we have to harness the skills and deploy it in the right manner, Krishna said, speaking during a one-on-one panel The Next Big Merger: Governments and Technology.

Omar bin Sultan said that the UAEs talent pool will be boosted by Indias decision to set up the first Indian Institute of Technology (IIT) in Abu Dhabi.

Last year, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a graduate-level research university focusing on artificial intelligence (AI), launched an executive programme, designed to assist the UAEs government and business elite in unlocking the potential of AI to ensure smart management, increased efficiencies, and enhanced productivity.

Read:Abu Dhabis AI university launches executive programme for UAE govt and business leaders

Located in Masdar City, MBZUAI offers Master of Science, Msc, and PhD level programmes in key areas of AI such as machine learning, computer vision, and natural language processing.

Read:Video: Abu Dhabi launches worlds first AI university

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Artificial intelligence will add $10tn to global economy in next decade - IBM CEO - Gulf Business

Artificial Intelligence in the Education Sector Market Research Report audits the development drivers and the momentum and future trends. The Artificial Intelligence in the Education Sector Market Size consists of various players. The organization profiling of the above Artificial Intelligence in the Education Sector Market players has been done in the report comprising of their business outline, financial overview and the business techniques took on by the organizations with Forecast Period 2022-2028.

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Major Highlights of TOC:

Major Points from Table of Contents:

Global Artificial Intelligence in the Education Sector Market Research Report 2022-2028, by Manufacturers, Regions, Types and Applications

1 Study Coverage

1.1 Artificial Intelligence in the Education Sector Product Introduction

1.2 Market by Type

1.2.1 Global Artificial Intelligence in the Education Sector Market Size Growth Rate by Type

1.3 Market by Application

1.3.1 Global Artificial Intelligence in the Education Sector Market Size Growth Rate by Application

1.4 Study Objectives

1.5 Years Considered

2 Global Artificial Intelligence in the Education Sector Production

2.1 Global Artificial Intelligence in the Education Sector Production Capacity (2016-2028)

2.2 Global Artificial Intelligence in the Education Sector Production by Region: 2016 VS 2022 VS 2028

2.3 Global Artificial Intelligence in the Education Sector Production by Region

2.3.1 Global Artificial Intelligence in the Education Sector Historic Production by Region (2016-2022)

2.3.2 Global Artificial Intelligence in the Education Sector Forecasted Production by Region (2022-2028)

3 Global Artificial Intelligence in the Education Sector Sales in Volume and Value Estimates and Forecasts

3.1 Global Artificial Intelligence in the Education Sector Sales Estimates and Forecasts 2016-2028

3.2 Global Artificial Intelligence in the Education Sector Revenue Estimates and Forecasts 2016-2028

3.3 Global Artificial Intelligence in the Education Sector Revenue by Region: 2016 VS 2022 VS 2028

3.4 Global Top Artificial Intelligence in the Education Sector Regions by Sales

3.4.1 Global Top Artificial Intelligence in the Education Sector Regions by Sales (2016-2022)

3.4.2 Global Top Artificial Intelligence in the Education Sector Regions by Sales (2022-2028)

3.5 Global Top Artificial Intelligence in the Education Sector Regions by Revenue

3.5.1 Global Top Artificial Intelligence in the Education Sector Regions by Revenue (2016-2022)

3.5.2 Global Top Artificial Intelligence in the Education Sector Regions by Revenue (2022-2028)

3.6 North America

3.7 Europe

3.8 Asia-Pacific

3.9 Latin America

3.10 Middle East and Africa

4 Competition by Manufactures

4.1 Global Artificial Intelligence in the Education Sector Supply by Manufacturers

4.1.1 Global Top Artificial Intelligence in the Education Sector Manufacturers by Production Capacity (2022 VS 2022)

4.1.2 Global Top Artificial Intelligence in the Education Sector Manufacturers by Production (2016-2022)

4.2 Global Artificial Intelligence in the Education Sector Sales by Manufacturers

4.2.1 Global Top Artificial Intelligence in the Education Sector Manufacturers by Sales (2016-2022)

4.2.2 Global Top Artificial Intelligence in the Education Sector Manufacturers Market Share by Sales (2016-2022)

4.2.3 Global Top 10 and Top 5 Companies by Artificial Intelligence in the Education Sector Sales in 2022

4.3 Global Artificial Intelligence in the Education Sector Revenue by Manufacturers

4.3.1 Global Top Artificial Intelligence in the Education Sector Manufacturers by Revenue (2016-2022)

4.3.2 Global Top Artificial Intelligence in the Education Sector Manufacturers Market Share by Revenue (2016-2022)

4.3.3 Global Top 10 and Top 5 Companies by Artificial Intelligence in the Education Sector Revenue in 2022

4.4 Global Artificial Intelligence in the Education Sector Sales Price by Manufacturers

4.5 Analysis of Competitive Landscape

4.5.1 Manufacturers Market Concentration Ratio (CR5 and HHI)

4.5.2 Global Artificial Intelligence in the Education Sector Market Share by Company Type (Tier 1, Tier 2, and Tier 3)

4.5.3 Global Artificial Intelligence in the Education Sector Manufacturers Geographical Distribution

4.6 Mergers and Acquisitions, Expansion Plans

5 Market Size by Type

5.1 Global Artificial Intelligence in the Education Sector Sales by Type

5.1.1 Global Artificial Intelligence in the Education Sector Historical Sales by Type (2016-2022)

5.1.2 Global Artificial Intelligence in the Education Sector Forecasted Sales by Type (2022-2028)

5.1.3 Global Artificial Intelligence in the Education Sector Sales Market Share by Type (2016-2028)

5.2 Global Artificial Intelligence in the Education Sector Revenue by Type

5.2.1 Global Artificial Intelligence in the Education Sector Historical Revenue by Type (2016-2022)

5.2.2 Global Artificial Intelligence in the Education Sector Forecasted Revenue by Type (2022-2028)

5.2.3 Global Artificial Intelligence in the Education Sector Revenue Market Share by Type (2016-2028)

5.3 Global Artificial Intelligence in the Education Sector Price by Type

5.3.1 Global Artificial Intelligence in the Education Sector Price by Type (2016-2022)

5.3.2 Global Artificial Intelligence in the Education Sector Price Forecast by Type (2022-2028)

6 Market Size by Application

6.1 Global Artificial Intelligence in the Education Sector Sales by Application

6.1.1 Global Artificial Intelligence in the Education Sector Historical Sales by Application (2016-2022)

6.1.2 Global Artificial Intelligence in the Education Sector Forecasted Sales by Application (2022-2028)

6.1.3 Global Artificial Intelligence in the Education Sector Sales Market Share by Application (2016-2028)

6.2 Global Artificial Intelligence in the Education Sector Revenue by Application

6.2.1 Global Artificial Intelligence in the Education Sector Historical Revenue by Application (2016-2022)

6.2.2 Global Artificial Intelligence in the Education Sector Forecasted Revenue by Application (2022-2028)

6.2.3 Global Artificial Intelligence in the Education Sector Revenue Market Share by Application (2016-2028)

6.3 Global Artificial Intelligence in the Education Sector Price by Application

6.3.1 Global Artificial Intelligence in the Education Sector Price by Application (2016-2022)

6.3.2 Global Artificial Intelligence in the Education Sector Price Forecast by Application (2022-2028)

7 Artificial Intelligence in the Education Sector Consumption by Regions

7.1 Global Artificial Intelligence in the Education Sector Consumption by Regions

7.1.1 Global Artificial Intelligence in the Education Sector Consumption by Regions

7.1.2 Global Artificial Intelligence in the Education Sector Consumption Market Share by Regions

7.2 North America

7.2.1 North America Artificial Intelligence in the Education Sector Consumption by Application

7.2.2 North America Artificial Intelligence in the Education Sector Consumption by Countries

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Artificial Intelligence in the Education Sector Market Size 2022 : Share and Trend, Growth Strategies with Revenue, future Scope, Analytical Overview...

The World Economic Forum (WEF) published a report titled Artificial Intelligence for Children a toolkit to enable various stakeholders to develop trustworthy artificial intelligence for children and youth.

Children and youth are surrounded by AI in many of the products they use in their daily lives, from social media to education technology, video games, smart toys and speakers. AI determines the videos children watch online, their curriculum as they learn, it says in the reports introduction.

The WEF toolkit was created by a team of academics, business leaders, technologists, and youth leaders. Its purpose is to enable the business sector to create ethical, responsible, and trustworthy AI to support parents, guardians and youth to navigate the AI environment safely.

The toolkit includes a tool for business called the C-suite. It provides actionable frameworks with real-world support to help companies design innovative and responsible AI for young people.

Many companies use AI to differentiate their brands and their products by incorporating it into toys, interactive games, extended reality applications, social media, streaming platforms and educational products. With little more than a patchwork of regulations to guide them, organizations must navigate a sea of privacy and ethics concerns related to data capture and the training and use of AI models. Executive leaders must strike a balance between realizing the potential of AI and helping reduce the risk of harm to children and youth and, ultimately, their brand, the report says.

The C-suite checklist guides business on topics such as full disclosure, systemic bias, age-sensitive user validation, and privacy. These areas are where businesses often fall short in a fast-evolving field where regulatory frameworks struggle to keep up.

The WEF report calls for an AI labelling system that would put a QR code on the box of every product incorporating AI potentially used by children. It would inform consumers, especially parents and guardians, the nature of the contents and possible considerations.

The QR code would share information about the product such as the age-appropriateness; whether it uses a microphone, or camera; whether the product can connect with other users on the internet; if it employs facial or voice recognition or gathers data.

The topic of AI and children is not a subject that has been taken lightly since the advent of AI. In 2017, MIT published a discourse on AIs impact on children and childhood called Hey Google, is it OK if I eat you?

Autonomous technology is becoming more prevalent in our daily lives. We investigated how children perceive this technology by studying how 26 participants (3-10 years old) interact with Amazon Alexa, Google Home, Cozmo, and Julie Chatbot. [The] children answered questions about trust, intelligence, social entity, personality, and engagement. We identify four themes in child-agent interaction: perceived intelligence, identity attribution, playfulness and understanding. Our findings show how different modalities of interaction may change the way children perceive their intelligence in comparison to the agents. We also propose a series of design considerations for future child-agent interaction around voice and prosody, interactive engagement and facilitating understanding, the MIT reports abstract said.

MIT raised concerns at the time that unregulated toys already on the market using AI and an internet connection were and continue to be a serious concern in terms of invasiveness and privacy.

Already, the Internet of Toys is raising privacy and security concerns. Take Mattels Aristotle, for instance. This bot, which is like an Amazon Echo for kids, can record childrens video and audio and has an uninterrupted connection to the internet. Despite the intimate link Aristotle has with young children, Mattel has said that it will not conduct research into how the device is affecting kids development. Another smart toy, the interactive Cayla doll was taken off the market in Germany because its Bluetooth connection made it vulnerable to hacking MIT said.

The WEF report concludes by warning stakeholders that AI products come with risks and benefits and that the risks are especially concerning in relation to their use by children.

Amanda Brown is a reporter with the Western Standardabrown@westernstandardonline.comTwitter: @WS_JournoAmanda

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WEF publishes toolkit on artificial intelligence and kids - Western Standard

VANCOUVER, BC, March 28, 2022 /PRNewswire/ -- Zoomd Technologies Ltd.(TSXV: ZOMD) (OTC: ZMDTF) and its wholly-owned subsidiary Zoomd Ltd. (collectively, "Zoomd" or the "Company"), the marketing tech (MarTech) user-acquisition and engagement platform, today announced its acquisition (the "Transaction") of Albert Technologies Ltd. ("Albert") on March 27, 2022. Albert is a U.S.-based artificial intelligence marketing platform for advertisers, driving fully autonomous digital campaigns for some of the world's leading brands. The consideration for the Transaction payable by Zoomd is a combination of cash and shares paid on March 27, 2022, being the closing date, and a future share-based earn-out payment, based on meeting certain criteria.

Albert processes and analyzes audience and tactical data at scale, thereby autonomously allocating budgets and optimizing creative and evolving campaigns across paid search and social media. Albert's value proposition to its clients is to ease the complexities of scaling, primarily using the Google and Facebook platforms, by executing campaigns at a pace and scale that were generally not previously possible. By autonomously combing through mass amounts of data, converting this data into insights, and autonomously acting on these insights, across channels, devices, and formats, Albert eliminates the manual and time-consuming tasks that generally limit the effectiveness and results of modern digital advertising and marketing.

"While we are also releasing some of our products onto a Self-Service and SaaS business model, Albert enhances our efforts immediately, with additional solid offerings that cover branding and awareness needs. Furthermore, we view Albert as complementary for mobile apps, particularly with regards to our future plans relating to Web3." said Ofer Eitan, Zoomd CEO, adding "we view M&A activity, which includes industry professionals, supplementary technology and solid customer base, as a part of Zoomd's growth objective. This acquisition shows our ambition to provide our partners a SaaS platform for scaling with minor efforts. Albert's team is a group of extremely talented veterans that fit Zoomd's culture. They have a number of Fortune 500 customers that will now be able to use our products and services. We are happy and excited to have the team come on board."

Or Shani, Founder and CEO of Albert commented: "We are excited to join Zoomd, a fast growing company in the marketing technology space. We believe that our business, based on our unique, patented and proven technology, will further accelerate given the great scale and financial strength of Zoomd."

For the purposes of the Transaction, the share component of the consideration will be valued at the higher of (i) the closing price of the shares on the date prior to their issuance and (ii) US$1.00 per share. Zoomd did not assume any of Albert's debt and no finder's fees were paid or are payable in connection with the Transaction. All shares to be issued pursuant to the Transaction are subject to the prior approval of the TSX-V.

About Zoomd:

Zoomd (TSXV: ZOMD, OTC: ZMDTF), founded in 2012 and began trading on the TSX Venture Exchange inSeptember 2019, offers a site search engine to publishers, and a mobile app user-acquisition platform, integrated with a majorityof global digital media, to advertisers. The platform unifies more than 600 media sources into one unified dashboard. Offering advertisers, a user acquisition control center for managing all new customer acquisition campaigns using a single platform. By unifying all these media sources onto a single platform, Zoomd saves advertisers significant resources that would otherwise be spent consolidating data sources, thereby maximizing data collection and data insights while minimizing the resources spent on the exercise. Further, Zoomd is a performance-based platform that allows advertisers to advertise to the relevant target audiences using a key performance indicator-algorithm that is focused on achieving the advertisers' goals and targets.

Neither TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the Exchange) accepts responsibility for the adequacy or accuracy of this release.

DISCLAIMER IN REGARD TO FORWARD-LOOKING STATEMENTS

This news release includes certain "forward-looking statements" under applicable Canadian securities legislation. Forward-looking statements include, but are not limited to, statements with respect the successful closing of the Transaction and the future success of Albert, Zoomd's future ability to successfully continue its growth, its ability to continue to deliver products and services largely unimpacted by the privacy updates undertaken (or will be undertaken in the future) by Google and Apple as well as its ability to continue expanding into new geographies and industries. Forward-looking statements are based on our current assumptions, estimates, expectations and projections that, while considered reasonable, are subject to known and unknown risks, uncertainties, and other factors that may cause the actual results and future events to differ materially from those expressed or implied by such forward-looking statements. Such factors include, but are not limited to: general business, economic, competitive, technological, legal, privacy matters, political and social uncertainties (including the impacts of the COVID-19 pandemic and the current war in Ukraine), the extent and duration of which are uncertain at this time on Zoomd's business and general economic and business conditions and markets. There can be no assurance that any of the forward-looking statements will prove to be accurate, as actual results and future events could differ materially from those anticipated in such statements. Accordingly, readers should not place undue reliance on forward-looking statements. The Company disclaims any intention or obligation to update or revise any forward-looking statements, whether because of new information, future events or otherwise, except as required by law.

The reader should not place undue importance on forward-looking information and should not rely upon this information as of any other date. All forward-looking information contained in this press release is expressly qualified in its entirety by this cautionary statement.

FOR FURTHER INFORMATION PLEASE CONTACT:

Company Media Contacts:Amit BohenskyChairmanZoomd[emailprotected]

Website: http://www.zoomd.com

Investor relations:Lytham Partners, LLCBen ShamsianNew York | Phoenix[emailprotected]

SOURCE Zoomd Technologies Ltd.

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Zoomd Announces the Acquisition of Artificial Intelligence Marketing Platform "Albert" - PR Newswire

When students in suburban Atlanta returned to school for in-person classes amid the pandemic, they were required to mask up, like in many places across the US. Yet in this 95,000-student district, officials took mask compliance a step further than most.

Through a network of security cameras, officials harnessed artificial intelligence to identify students whose masks drooped below their noses.

If they say a picture is worth a thousand words, if I send you a piece of video its probably worth a million, said Paul Hildreth, the districts emergency operations coordinator. You really cant deny, Oh yeah, thats me, I took my mask off.

The school district in Fulton county had installed the surveillance network, by Motorola-owned Avigilon, years before the pandemic shuttered schools nationwide in 2020. Out of fear of mass school shootings, districts in recent years have increasingly deployed controversial surveillance networks like cameras with facial recognition and gun detection.

With the pandemic, security vendors switched directions and began marketing their wares as a solution to stop the latest threat. In Fulton county, the district used Avigilons no face mask detection technology to identify students with their faces exposed.

Remote learning during the pandemic ushered in a new era of digital student surveillance as schools turned to AI-powered services like remote proctoring and digital tools that sift through billions of students emails and classroom assignments in search of threats and mental health warning signs. Back on campus, districts have rolled out tools like badges that track students every move.

But one of the most significant developments has been in AI-enabled cameras. Twenty years ago, security cameras were present in 19% of schools, according to the National Center for Education Statistics. Today, that number exceeds 80%. Powering those cameras with artificial intelligence makes automated surveillance possible, enabling things like temperature checks and the collection of other biometric data.

Districts across the country have said they had bought AI-powered cameras to fight the pandemic. But as pandemic-era protocols like mask mandates end, experts said the technology will remain. Some educators have stated plans to leverage pandemic-era surveillance tech for student discipline while others hope AI cameras will help them identify youth carrying guns.

The cameras have faced sharp resistance from civil rights advocates who question their effectiveness and argue they trample students privacy rights.

Noa Young, a 16-year-old high school junior in Fulton county, said she knew that cameras monitored her school but wasnt aware of their hi-tech features like mask detection. She agreed with the districts now-expired mask mandate but felt that educators should have been more transparent about the technology.

I think its helpful for Covid stuff but it seems a little intrusive, Young said in an interview. I think its strange that we were not aware of that.

Outside Fulton county, educators have used AI cameras to fight Covid on multiple fronts.

In Rockland regional school unit 13 in Maine, officials used federal pandemic relief money to procure a network of cameras with face match technology for contact tracing. Through advanced surveillance, the cameras, made by California-based security company Verkada, allow the 1,600-student district to identify students who came in close contact with classmates who tested positive for Covid-19.

At a district in suburban Houston, officials spent nearly $75,000 on AI-enabled cameras from Hikvision, a surveillance company owned in part by the Chinese government, and deployed thermal imaging and facial detection to identify students with elevated temperatures and those without masks.

The cameras can screen as many as 30 people at a time and are therefore less intrusive than slower processes, said Ty Morrow, the Brazosport independent school districts head of security. The checkpoints have helped the district identify students who later tested positive for Covid-19, Morrow said, although a surveillance testing company has argued Hikvisions claim of accurately scanning 30 people at once is not possible.

That was just one more tool that we had in the toolbox to show parents that we were doing our due diligence to make sure that we werent allowing kids or staff with Covid into the facilities, he said.

Yet its this mentality that worries consultant Kenneth Trump, the president of Cleveland-based National School Safety and Security Services. Security hardware for the sake of public perception, the industry expert said, is simply smoke and mirrors.

Its creating a facade, he said. Parents think that all the bells and whistles are going to keep their kids safer and thats not necessarily the case. With cameras, in the vast majority of schools, nobody is monitoring them.

When the Fulton county district upgraded its surveillance camera network in 2018, officials were wooed by Avigilons AI-powered appearance search, which allows security officials to sift through a mountain of video footage and identify students based on characteristics like their hairstyle or the color of their shirt. When the pandemic hit, the companys mask detection became an attractive add-on, Hildreth said.

He said the district didnt actively advertise the technology to students but they probably became aware of it quickly after students got called out for breaking the rules. He doesnt know students opinions about the cameras or seem to care.

I wasnt probably as much interested in their reaction as much as their compliance, Hildreth said. You dont have to like something thats good for you, but you still need to do it.

A Fulton county district spokesperson said they were unaware of any instances where students were disciplined because the cameras caught them without masks.

Among the school security industrys staunchest critics is Sneha Revanur, a 17-year-old high school student from San Jose, California, who founded the youth-led group Encode Justice to highlight the dangers of artificial intelligence on civil liberties.

Revanur said she was concerned by districts decisions to implement surveillance cameras as a public health strategy and that the technology in schools could result in harsher discipline for students, particularly youth of color.

Verkada offers a cautionary tale. Last year, the company suffered a massive data breach when a hack exposed the live feeds of 150,000 surveillance cameras, including those inside Tesla factories, jails and at Sandy Hook elementary school in Newtown, Connecticut. The Newtown district, which suffered a mass school shooting in 2012, said the breach didnt expose compromising information about students. The vulnerability hasnt deterred some educators from contracting with the California-based company.

After a back-and-forth with the Verkada spokesperson, the company would not grant an interview or respond to a list of written questions.

Revanur called the Verkada hack at Sandy Hook elementary a staggering indictment of educators rush for dragnet surveillance systems that treat everyone as a constant suspect at the expense of student privacy. Constant monitoring, she argued, creates this culture of fear and paranoia that truly isnt the most proactive response to gun violence and safety concerns.

In Fayette county, Georgia, the district spent about $500,000 to buy 70 Hikvision cameras with thermal imaging to detect students with fevers. But it ultimately backtracked and disabled them after community uproar over their efficacy and Hikvisions ties to the Chinese government. In 2019, the US government imposed a trade blacklist on Hikvision, alleging the company was implicated in Chinas campaign of repression, mass arbitrary detention and high-technology surveillance against Muslim ethnic minorities.

The school district declined to comment. In a statement, a Hikvision spokesperson said the company takes all reports regarding human rights very seriously and has engaged governments globally to clarify misunderstandings about the company. The company is committed to upholding the right to privacy, the spokesperson said.

Meanwhile, regional school unit 13s decision to use Verkada security cameras as a contact tracing tool could run afoul of a 2021 law that bans the use of facial recognition in Maine schools. The district didnt respond to requests for comment.

Michael Kebede, the ACLU of Maines policy counsel, cited recent studies on facial recognitions flaws in identifying children and people of color and called on the district to reconsider its approach.

We fundamentally disagree that using a tool of mass surveillance is a way to promote the health and safety of students, Kobede said in a statement. It is a civil liberties nightmare for everyone, and it perpetuates the surveillance of already marginalized communities.

In Fulton county, school officials wound up disabling the face mask detection feature in cafeterias because it was triggered by people eating lunch. Other times, it identified students who pulled their masks down briefly to take a drink of water.

In suburban Houston, Morrow ran into similar hurdles. When white students wore light-colored masks, for example, the face detection sounded alarms. And if students rode bikes to school, the cameras flagged their elevated temperatures.

Weve got some false positives but it was not a failure of the technology, Hildreth said. We just had to take a look and adapt what we were looking at to match our needs.

With those lessons learned, Hildreth said he hoped to soon equip Fulton county campuses with AI-enabled cameras that identify students who bring guns to school.

In a post-pandemic world, Albert Fox Cahn, founder of the non-profit Surveillance Technology Oversight Project, worries the entire school security industry will take a similar approach.

With the pandemic hopefully waning, well see a lot of security vendors pivoting back to school shooting rhetoric as justification for the camera systems, he said. Due to the potential for errors, Cahn called the embrace of AI surveillance in schools really alarming.

This report was published in partnership with the 74, a non-profit, non-partisan news site covering education in America

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Really alarming: the rise of smart cameras used to catch maskless students in US schools - The Guardian