3 Questions: Kalyan Veeramachaneni on hurdles preventing fully ... While the government did not specify when Ramon Magsaysay would be conferred with the country’s highest honour, the President recently revealed that it would be bestowed on the foreign investor, who has committed large scale investments and is actively involved in social and educational reforms. While the government did not specify when Ramon Magsaysay would be conferred with the country’s highest honour, the President recently revealed that it would be bestowed on the foreign investor, who has committed large scale investments and is actively involved in social and educational reforms. The Philippines will be conferred the Ramon Magsaysay Award, Philippines’ highest honour, on the investor that will be announced later this week, the foreign policy committee of Magsaysay award organizers, the late President Ramon Magsaysay Award Foundation said on Tuesday. The award, which is given in the name of Magsaysay, will be conferred posthumously on those who meet four criteria established by the foundation. On May 10, Magsaysay was shot and killed by an unknown attacker who approached the famous presidential vehicle from the back. President Fidel Ramos decided, at the outset of his term to honor Magsaysay with a highest honour that will be bestowed on the investor who will be announced later this week as the recipient of the award. The Magsaysay award is conferred by the late President Ramon Magsaysay Award Foundation, a non-government organization that is headed by former President Fidel V. Ramos, the longest-serving President in the country’s history. “This is indeed the best way to commemorate his legacy, as this award is indeed a significant honor, a big responsibility for any deserving winner. At the same time, however, this may be an opportunity for the awardees to work together towards his vision,” the late President said in a radio call-in programme in his last year in office. He said the Magsaysay award would honor individuals who have done outstanding things in their chosen field, whether civic or political, education and social or environmental. He said the Ramon Magsaysay award committee would recommend someone who has made a significant contribution in their chosen field to be conferred with the Magsaysay award. “We always strive to create an atmosphere where the best work gets recognized.” President Ramos had said Magsaysay would receive the award at midnight of April 7, 2000, in a ceremony which would be held in the Cultural Center of The Philippines in the presence of a select audience. He said his father had set aside the date to honor Magsaysay, the former foreign minister who was shot dead in a drive-by shooting on May 10, 1960, when he was returning to Malacañang from his home in Pasig City. A year of mourning Ramos said he was looking forward to conferring the highest honor on Magsaysay, the first Filipino President to be killed in the line of duty. “It could never be an adequate compensation for what had happened, the loss and the pain, for what everyone will go through for the next one year of mourning, but nonetheless, I hope that those who will be given the opportunity will do their best to live up to what Magsaysay taught.” He said this recognition was the “best way to give him his full tribute on the centenary of his birth, since he was the first Filipino to be awarded with the Magsaysay Award.” Ramos said many would be shocked when they could see him in the company of awardees, and there might be a tinge of regret that Magsaysay was still alive. The former President said he did not want any person to feel guilty at the award ceremony. He said Magsaysay was not only a highly-decorated soldier and statesman, but a great father and friend, “in a way, he’s all of these.” “I want him to rest in peace and I want to give him all the honours that he deserves, but I want to see all the awardees who will be conferred with the Magsaysay award to live up to all he stood for, to live up to all the ideals that he died for,” he said. Ramos said his father always said the Ramon Magsaysay award, which is the most meaningful award that one can bear, should be a recognition and a tribute to great works, “not merely great individuals.” He said Magsaysay could be honored through his personal legacy and work, which he would leave behind for future generations. He said the award would give recognition to his father as a great President of the Philippines. Former President Fidel V. Ramos, the longest-serving President in the country’s history, has conferred the Ramon Magsaysay Award posthumously to foreign investor Magsaysay Fund International, which has committed large-scale investments in key infrastructure projects in the Philippines. In its presentation of the Ramon Magsaysay Award, which is the highest national honor the Philippines’ can bestow on someone who has done out-of-the-box in any filed of life, the Ramon Magsaysay Award Foundation has cited Magsaysay Fund International for its “commitment to social and educational reforms.” “He was awarded this recognition in 1960 for his outstanding achievement by the Magsaysay Award Foundation. He had not only played a significant role in his chosen field of social and economic progress, but also had done several important things for his country. Magsaysay will be formally recognized again. This time, he will be the recipient of the Ramon Magsaysay Award posthumously,” the late President said in his radio call-in programme. Magsaysay death: No charges filedMagsaysay, the youngest soldier to win the Philippines’ top military medals in World War II, suffered gunshot entry wounds in his chest and head in the late night of May 10 last year. The 64-year-old war hero was assassinated at the doorway of the presidential vehicle that he was driving back home from his home in Pasig City. His assassination in broad daylight in Makati City shocked the nation and gave rise to speculation that he may have been the latest murder victim of an all-powerful “political mafia.” In the aftermath of his death, President Fidel Ramos announced a 10-day period of national mourning for Magsaysay. The late President also ordered the military to protect the presidential vehicle used in the fateful trip until further notice. In April 21, 2001, the Department of Justice dismissed the allegations and announced that no one would be charged for the assassination and robbery of Magsaysay. Magsaysay Fund International has been investing in key infrastructure projects in the Philippines, including the proposed construction of the Metro Manila Skyway that would link Manila and the Ninoy Aquino International Airport. Magsaysay Fund International Managing Director Dr. Leo Chua, a close aide of Magsaysay who was also shot and killed in the assassination, was one of the two men who shot the famous Presidential Assistant in the back of the head and stole his cash and valuables when Magsaysay was about to exit the armored presidential vehicle. The shooting was caught on a nearby store’s security camera that was used to buy items for Magsaysay’s wife at the end of Magsaysay’s trip to the Pasig airport for his return to the capital. The video has been used by various media organizations to bring to light other suspected murder cases in Manila. Magsaysay Fund International is also said to have provided support to the Philippine government that led to a successful campaign to prevent another secessionist rebellion in the provinces of Sabah, Borneo, Malaysia, for the establishment of the Filipino-run North Borneo province. A successful campaign that won the Philippine intervention and the defeat of secessionist groups that the Philippine Army had claimed for decades were also cited by the Ramon Magsaysay Award Foundation as an impressive feat “to which Magsaysay could have been the most proud.” Magsaysay was also credited for his role in the negotiations that led to the restoration of relations between the Philippines and Indonesia, which lasted for 21 years. After he was killed, Magsaysay’s widow, Dr. Corazon Aquino led the People Power movement that resulted in the overthrow of President Marcos. Her term as President of the Philippines from 1985 to 1992 came during the period of the Magsaysay Award that was conferred by the now defunct Magsaysay Prize. In 1985, the late President Ramos conferred the Magsaysay Award to Magsaysay himself, who represented human rights under the administration of President Corazon Aquino. In the late 80s, Magsaysay represented the Philippines at the International Peace and Security Council of the United Nations. Magsaysay Fund International’s CEO, Kalyan Veeramachaneni who will be presented with the Ramon Magsaysay Award, would be the first person to collect the prestigious award given out annually at the Cultural Center of The Philippines in the presence of dignitaries.

3 Questions: Kalyan Veeramachaneni on hurdles preventing fully ...

6 Benefits of Using MLOps For Your Machine Learning Application The most interesting thing about machine learning (also known more recently as artificial intelligence) is of course how quickly it is taking off in the world. It took years for it to become really tangible; but now that its basic building blocks have emerged, AI shows no signs of slowing down. As with previous major technology leaps, it does not appear to be slowing down anytime soon. All that said, this technological innovation is still fairly new in some respects. For one, its use cases are fairly complex for end users; and secondly, not too many businesses are really using it in a mature way – at least not at the same level as more traditional business areas. However, as machine learning is gaining traction in the market, companies need to invest in more tech for it; and more particularly to build an efficient infrastructure to support that technology. That means companies have to deal with the basic building blocks that machine learning is requiring. So what are these blocks? You’re very likely already familiar with some aspects of Machine Learning. These are things like: Signal Detection and Estimation: One of the most basic parts of a machine learning algorithm is to first determine if something in the environment matches something in the database. Optimization and Ranking: An essential part of any predictive modeling is to try to rank the quality of the results. When done well, you can also determine the probability of future results. Classification: The third type of machine learning is the one that gets the most attention lately, but it is actually the least common. The first type of this is known as supervised or semi-supervised learning (which uses a small sample of data to train a learning algorithm and then use it to classify the next large sample of data) Clustering: The fourth type is one that is often overlooked (but is gaining widespread traction lately) and is based on groups of similar items. It tends to be more focused on understanding the patterns in the data rather than the ‘cause and effect’ relationship. This is typically how MLops and DataOps work together. For example, with a data science project, the data science team will usually determine what they will be using the algorithm for. What is the type of data they’re going to be dealing with? What will the result from the algorithm be? Once that is determined, DataOps (sometimes referred to as Operations of machine learning) will then start to research what platforms are needed, how do these platforms operate, and how is data stored in them. To do that, DataOps is looking at how data will have to be ingested, processed, and used with their data store of choice. So what types or types of platforms are required? This will depend on what types of analysis will be used; and what types of data storage and compute services are needed. Here are some of the most common platforms used to do analytics for business: Big Data Data warehousing Data warehousing is an attempt to use a process to organize data for a different problem. In most cases, there will be an analytics layer (or something similar) which will try to make sense of the data in as few steps as possible. While there are several different implementations of data warehousing today, more of the technology’s power is coming from the advanced features it may be able to offer. Hadoop (Hadoop), or the HDFS platform of Hadoop, is often seen as the leader of the pack. This is because it offers a high performing, high throughput, and highly scalable platform. It is the standard of analytics. Big data itself is just a type of data warehousing. Big is very relative here. It is based on being able to handle very large amounts of data, while at the same time performing the analytics quickly to bring valuable insights to the analysts. Analytics clouds are also part of the Big Data category. They offer similar capabilities as big data. They do so using a more standardized programming approach (and are based on the same type of architecture – Hadoop). Analytics on Hadoop and analytics clouds are both fairly recent innovations. There were no large-scale analytic platforms like this in the past. They are seen as necessary for big data in the future. Data lake Data lakes – as opposed to being just a huge data warehouse – are very different. They actually make data available for multiple different uses. Instead of being used with more targeted systems, a data lake is more of a standard container that can be taken advantage of by multiple different types of apps. It is this multi-use factor that appeals to more businesses. For data lake, its appeal is seen with organizations that will use machine learning, but also other more traditional use cases, like business intelligence. It can also be adopted for everything in between. It enables the data to be used in different ways without having to be taken down and re-organized. It can be an added extra to the business. It isn’t about just being efficient. It is also about enabling the analysts to be able to interact with the data in ways they are currently not able to. This can make a big difference in how they feel about using the data – something they would be more comfortable with if they saw things they were doing already. Data lake is not a platform, but it is built in many of the big data platforms, including some data warehouse platforms (such as Cloudera). Its appeal is not just as a data warehouse, because of its flexibility and open nature. What is also different about data lake is the fact that it often comes pre-processed – all the data is already present and ready to use. It just needs to be managed differently; and this again gives it versatility that some of the other platforms can’t match. Big data platforms There are a lot of different big data platforms today. There are vendors that have existed since the beginning of the big data age, like Google and Amazon, and others that are just beginning to emerge. It’s hard to really tell which are the best because none of the technologies is fully mature. No one company is offering the perfect solution. Each platform has advantages and disadvantages. One of the most well known vendors today is Google. They offer an analytics cloud which is similar to Hadoop in a lot of ways. Their analytics cloud offering is a bit different since it does not use Hadoop. Instead, it uses an open source solution called BigTable. Many of the features in Google’s analytics cloud can be used by other vendors. They recently expanded their machine learning capabilities by doing some work on Apache Stratos (Stratos), which is another platform focused on analytics. It includes some components (Kibana), but it is not tied to that platform. In fact, one of the main selling points of Stratos is the fact that it can function as both Big Data and machine learning platforms. But it was specifically built to be used as a platform. It has been around for a while – since 2011. Microsoft has an analytics engine – Analytics Application Programming Interface (API). It has a couple of different implementations for different languages. The main difference here is that it is a.NET-based platform. That means the languages it uses will be based around C# and Visual Basic. IBM is also an older and very well-known big data technology platform provider. It has a Hadoop-based strategy, which offers a platform similar to Hadoop. It is available as an appliance. It can be deployed on-premises or as a cloud-based service. It can also be a hybrid – which is not ideal. For hybrid solutions, the company offers a PaaS (Platform as a Service) solution which is hosted. It is a way for big data users who want to use a hybrid approach but still want to utilize a large-scale infrastructure. Amazon is fairly new in the big data space. It offers its own AWS platform for running analytics apps as well as several other services. It also recently brought in Hadoop natively and now offers a way to deploy Hadoop. The AWS Hadoop platform is based on Hadoop 2.0. That might sound strange, but it just means they’re using some components that have been built specifically for Hadoop 2.0, not the full version of Hadoop. Google analytics Google Analytics is also one example of where big data is not always a good solution. This is because when it comes to machine learning, it offers relatively little. It has a data modeling and segmentation tool. It is pretty basic, but this is the case with most analytics or analytics ‘clouds’. It is designed to make it easier to use. There are other analytics offerings which are very similar and can be used in tandem with other machine-learning tools; but Google Analytics is more or less what is available. In this case, it would be helpful to understand why it does not offer much in the way of machine learning. The fact is that the reason for the lack of these capabilities is Google’s strategy to focus on the mobile first movement within marketing management. They want to ensure marketers do not confuse web analytics for mobile analytics. It is designed to help marketers make informed decisions about their mobile sites. It is not meant to be used for more complex analysis. This will still need to be done by someone. But Google’s analytics engine does not offer the tools to do this. Those do need to be supported by platforms like Big Data. Data lake A data lake is much more of a general purpose database than a data warehouse.

6 Benefits of Using MLOps For Your Machine Learning Application

A guide to machine learning in search: Key terms, concepts ... Machine learning is at the heart of AI. It has been since the beginning. The only difference is that, today, machine learning is a thing. As a consequence, Google’s own definition of AI has changed. In the company’s own view, “AI encompasses a set of innovations in machine learning, other technologies, and high-level understanding of data.” To that end, machine learning has seen remarkable growth at Google. It has become the go-to tool for image recognition, speech and language processing. It powers Google Search and serves as the platform for Google Assistant. Its technology is also part and parcel of Google Lens, a technology search users can use to find information with their camera lens. If machine-learning algorithms are the heart of AI, machine learning itself is the brain that drives it. In the last year or two, the search team has devoted particular attention to machine learning. The change of direction was in part to help build a platform to develop advanced machine learning algorithms. However, given Google’s long and deep history in searching, machine learning has also been central to search for a long time. With that in mind, here are some ideas on the subject. Machine learning (ML) is the science of getting software to learn. Google already has a history of doing this with great success – for example, with Google Voice and Google Now, the company has achieved some impressive feats in giving its users context-based, personalized services. Machine learning, which is related to artificial intelligence (AI), is more than that, though. Machine learning has come to have a life of its own. For search, it is not just about giving personalisation a new shot in the arm, it’s about achieving true general intelligence in search. At the heart of true general intelligence are algorithms that are able to process information like a human – and the ability to learn is how search would achieve that. It is that ability that search engines like Google search have always relied on; machine learning provides the key that will allow search to go from being just a collection of useful tools to becoming an integral feature of our world. Machine learning and search have been entwined from the early days of computer science: Search is what we use to find information online. We are a tool for the machine-learning algorithms we use to learn about the world. The two have become so closely linked that it is hard to imagine one without the other. Machine learning is the foundation on which artificial intelligence rests. So how does search fit into the bigger picture of machine learning? Here is the short answer: The way search engines work today rely, by and large, on algorithms that learn what we want to find by analyzing the things that we search for. For example, Google has built an algorithm that extracts information from the content of pages and can tell that the page contains links, images or videos, or information about an issue, or about a person in some cases; in fact, it has to extract information on a wide range of topics from text-only pages. In search, Google also uses the most popular questions asked on other search engine site pages to improve results. But the search engine has made a point of noting that it does not understand these pages. It uses machine learning to learn what they are about, not humans. This is a very small part of where machine learning has to be integrated; it is just a brief glance at the power that can be applied to any aspect of a search engine or website. We’re also at a point in time where the search engine is being augmented by the internet of things, where it is possible to learn about people and about objects in the physical world in the same way that it is possible to learn about information on websites and with people. This is the key area of innovation in machine learning at the moment, and it is very much the subject of the search team’s focus. When we talk about machine learning, one of the things that comes up is the fact that the search engine is changing. Search is now becoming an engine of knowledge – in the best cases, a “learning engine” or, in the least case, a collection of tools people use to find information. It’s important to keep that in mind. Google is in the business of using information and technology to make the world a better place – both for its users and for the people that drive Google’s own growth. That will always be where the search team is looking. The search team itself started life with a small team of people that, early on, were working on image search; it grew out of that, and is now a larger group of people focused on many things, including machine-learning development. Google has been using many types of machine learning. In a very general sense, machine learning comprises the technologies that allow you to train a computer to make good judgments. However, much of the work the team has been doing is related to learning algorithms specifically. What is machine learning? The term covers several areas of technology that together are able to make machines learn from the world; it covers technology that is more than that, and is in the broadest sense related to artificial intelligence. What is “machine learning”? Is it something that goes beyond artificial intelligence? The answer is that it does apply to artificial intelligence. The difference is, “machine learning algorithms” can be taught on a “case-by-case basis”, unlike intelligence. The search team is also interested in a broader question: What defines a learning algorithm? That question raises questions about the way that we think about algorithms. What sets apart machine learning algorithms from other types of algorithms? It is a common misconception that machine learning is some magic that a search engine can do in addition to algorithms that analyze the search information. This is wrong, because many common types of algorithms can be expressed in some form as a search-like expression – for example, algorithms that search for duplicated elements or for links. Machine learning, as we have seen, involves learning about various types of information in the world from an analysis of case studies. With that in mind, and to make it easy to follow, we are going to take a look at key terms and ideas relating to the field of machine learning. This is a brief definition, not an exhaustive one. The hope is to have created a basic understanding of some of the things that are going on, as we explore the different aspects – “machine learning in search” – in future articles. Key terms The term “machine learning” can be loosely defined a number of ways. An algorithm that learns is a common way of capturing it. The term is a loose and imprecise one, but is still commonly used to mean this. It is important to understand that, when we talk about “machine learning in search”, we are talking more about the processes around searching. To the best of my knowledge, there is currently no “search engine that learns for your company”, so machine learning is an important aspect of search but it is not the main topic; that is more about “machine learning in search” and how the search engine can be used. Machine learning in search generally refers to the development or use of machine learning algorithms, or sometimes just machine learning. In those contexts we can talk about algorithms like neural networks, which are used in the same way that statistics were used over the years to learn a statistical model. Neural networks don’t have to be neural in a real sense, but they usually are, and they are used in a way that they can learn. Machine learning in general can be associated with algorithms which work by “trying out a series of models on small chunks of data, and identifying which of those models best predicted new chunks of data”. So we might have a model that helps a computer understand a language, or it can be used for image recognition. When we talk about machine learning in particular, as something specific, we are typically talking about supervised learning, also known as “pattern recognition”. What’s important is that we shouldn’t be thinking about these terms as if one or the other is more important, because both are equally useful. What’s important is that we understand the context in which those terms or concepts are being used. A very basic question often posed is: How does a search engine apply machine learning? A search engine does this on two levels. At the simplest level, the searcher types in a query, and the engine returns a list of things that it thinks are relevant (hopefully). How that happens is different on a regular search engine from how it happens on a machine-learning based search engine. However, on both, the core of the process is “how the learner works”. This is the first key aspect, and we’ll get into how to define the different forms of search engine later. Machine learning is a broad area, and also as broad as what we know about searching. It is about applying algorithms to problems in a way that takes advantage of the way they learn that we want to understand. A common approach across machine learning is the use of neural models, but the approach isn’t limited to that. There are a number of ways that people are building machine learning systems that are specific to the problem at hand. That makes machine learning in search something a little different from machine learning as a whole. It is also important to understand the various fields of machine learning that are subsets of the concept. The most recognizable machine learning subsets are supervised learning, reinforcement learning and unsupervised learning.

A guide to machine learning in search: Key terms, concepts ...

A weakly supervised machine learning model to extract features from microscopy images and predict the location of breast cancer in three-dimensional space. 1. Background {#sec1} ============= The diagnosis of breast cancer can be challenging for radiologists and pathologists due to large size and irregular, multi-nodular architectural patterns of tumors. This diagnostic procedure can be time consuming as it requires extensive knowledge and experience coupled with a close attention toward patient\'s age, menopausal status and other co-morbid conditions. In fact, the misdiagnosis of breast carcinoma is common \[[@bib1],[@bib2]\]. Consequently, to reduce the time spent and prevent expensive and invasive surgery, there is a high demand to develop non-invasive diagnostic tools. Image-based analysis methods hold the potential to improve breast cancer diagnosis \[[@bib3]\], especially when high resolution images can be achieved with non-invasive sources \[[@bib4]\]. Although the development of advanced image analysis methods has provided better classification performance \[[@bib5]\], these models depend on a pre-defined set of features and a set of labeled training data which are usually not always readily available. Therefore, it is very challenging to extract image features in a manner that is independent of any prior knowledge and the ability to predict the location of a tumor in three-dimensional space without relying on external knowledge, such as a breast atlas. Weakly supervised methods have shown the potential to address these challenges through the use of only a few labeled training sets \[[@bib6], [@bib7], [@bib8], [@bib9]\]. These methods usually first generate a label for a class (e.g., tumor) in advance and then find a set of informative image features. An important issue in machine learning is the class imbalance due to the rarity of a large set of training samples of a certain disease. For instance, in the case of breast cancer images, the sample size for cancerous and non-cancerous (benign) classes is usually very small \[[@bib10]\]. To address this issue in image classification, it is common to either downscale images so that it has the same size of the training samples or use various sampling methods such as oversampling \[[@bib11]\]. Although these methods successfully reduce the class imbalance problem, it has been previously shown that the effect of down-sampling may be dependent on the feature extraction method utilized \[[@bib12]\]. Furthermore, these approaches can increase the feature extraction time as well and lead to additional cost of labor and time. To overcome these challenges, we propose a weakly supervised machine learning framework in order to extract features from a small number of training samples and to predict the spatial location of the tumor. The framework is simple yet effective in terms of time-saving and cost-effective. Most existing weakly supervised methods utilize a patch-based strategy which requires the presence of a few labeled images. Such methods start by downsampling the original high-resolution images to generate a set of low-resolution patches, find the optimal class in each patch and use a set of non-maximum suppression method to obtain a set of bounding boxes. However, this approach results in a significant decrease in the resolution of the final results. Besides, it is also time-consuming for down-sampling high-resolution images with the size between $50{cm} \times 50{cm}$ to $1{cm} \times 1{cm}$. This is mainly attributed to the fact that each patch in such input images covers a wide area that can require a long computational time to calculate an optimal class (i.e., tumor) and a set of boundaries of a tumor \[[@bib13]\]. This also prevents the usage of this method in real-time on-line use cases. A solution to this problem is to adopt a new strategy to generate samples of a large set of labeled samples such as the patches and then select a bounding box for each sample instead of using a patch of a small size. In such an approach, the training samples can be more robust and the process is simple enough. In this research, we generate a set of samples of the breast structure with their corresponding bounding boxes for training a weakly supervised model by employing an algorithm that can perform fast searching instead of any kind of oversampling methods such as random sampling or other patch-based approaches \[[@bib14]\]. The samples with their corresponding label are then used to estimate the boundaries of a tumor. The contributions of this work include the following:•The design of a new robust sampling strategy is presented in order to estimate the boundaries of a tumor from low-resolution images. Samples of the breast are generated by employing a new approach. These samples are then used to estimate the boundaries of the tumor through a weakly supervised machine learning framework.•A novel and effective algorithm is employed to generate a set of bounding boxes of breast structure and then the location of the tumor is predicted by utilizing the trained model. 2. Methods {#sec2} ========== In this section, we first describe the method used to generate a high-resolution patch for a sample of a breast structure as well as the generation of the whole image sample. We then briefly present the proposed weakly supervised machine learning algorithm. 2.1. Breast image samples {#sec2.1} ------------------------- A sample of a breast is used to extract a set of high-resolution patches that cover the entire breast structure. Such samples help us to estimate the location of the tumor by predicting the boundaries of the tumor with an estimated margin of error which may be considered as the minimum distance between the boundary of the tumor and the boundary of the tissue that is labeled with a certain class. However, it is challenging to determine this distance as it is not straightforward as to locate the tumor in a three-dimensional space. Moreover, this margin of error depends on the amount of tissue labeled as the tumor (i.e., an image sample with less tissue will have a larger margin of error) and the size of the images. The sampling strategy starts with pre-processing an input image (cf. [Figure 1](#fig1){ref-type="fig"}). The strategy uses a $12 \times 12$ sliding window approach, with a shift of 1 pixel (i.e., pixel $1,{\ 2}\text{,}\text{…},\ 12$), to extract a set of patches (i.e., $P_{i}\text{,}\ i = 1\text{,}\text{…},\ 12 $) from the input images which are then scaled to a size of a $128 \times 128$ image in order to apply patch-based methods. The location of the tumor is computed if the tumor is in the center (i.e., location $14$) and the margin of error is defined as the minimum distance between the tumor margin and boundaries of the sample.Figure 1Example of a pre-processed image before applying the oversampling technique: the images in *a)* has different sizes and resolution. The resulting image after applying the patch-based oversampling technique in *b)* has the same size and resolution as the images in *a)*.Figure 1 2.2. Oversampling techniques {#sec2.2} ---------------------------- The goal of the oversampling techniques is to obtain as close as possible a sample that represents what a patch of a $128 \times 128$ would be like in such a sample of the original image. Note that the obtained sample is of the same size as the original image and the resolution of the patch is $16 \times 16$ and $8 \times 8$. A classical technique to perform oversampling is the image down-sampling followed by a nearest neighbor down-sampling \[[@bib15]\]. However, these methods lead to pixel-based loss of information and this effect decreases the accuracy of the estimated tumor location. A better and more robust down-sampling method is the patch-based approach which starts by generating a set of patches of size $16 \times 16$ for which each pixel is assigned to the closest patch, without losing any pixel (cf. [Figure 1](#fig1){ref-type="fig"}). After applying an edge-finding approach for local maxima and minima, a set of $128 \times 128$ bounding boxes are created for each patch (i.e., $B_{i}$). The process is similar to the steps used to generate a set of patches starting with pre-processing from the input image, followed by the creation of patches from the input image. However, after pre-processing an input image, bounding boxes of $16 \times 16$ can be achieved by computing its maximum and minimum values in the $x$ and $y$ directions. These boxes can be used to generate samples of a $16 \times 16$ in an image sample by employing a grid sampling approach along with the nearest neighbor scheme. 2.3. The weakly supervised patch-classification algorithm {#sec2.3} --------------------------------------------------------- In weakly supervised learning, the training samples are labeled with the corresponding class. For image classification tasks, the data need to be partitioned such that the training samples can be separated as non-cancer samples from cancer samples and this can be done by employing a classifier such as a random forest \[[@bib16]\].

A weakly supervised machine learning model to extract features from microscopy images

AI, ML & IOT driving growth of furniture industry We’ve looked carefully at the numbers, and it’s not pretty. Over the last 12 months, furniture retailers have struggled with sales growth of just 0.45 per cent. What caused this decline in sales? This is where IT drives growth and it needs to be considered as part of the wider market. Our findings show that a key factor has been the rise of the Internet of Things (IoT) and the related ‘Internet of Things for Retailers’ (IoTR). Over the last three years we have seen an explosion of IoT devices, with a corresponding fall in the number of people using IoT to interact with their devices. The market growth in that space over the past few years has been phenomenal. Data suggests it is a technology that will be the driving force behind growth in this market for many years to come. The rise in mobile, internet and social media use in the last decade has driven a growth in IoT use. According to Cisco, in 2017: 94% of the Internet of Things is going to be connected by 2020. As a result, the volume of IoT devices on the market doubled in 2016, and by March of this year, the total number of devices was more than doubled. And with that growth came growth in sales of connected products. According to the Internet of Things Report published by Strategy Analytics, in 2016, devices and sensors accounted for $8.7bn of sales and in 2017, IoT devices and sensors only account for $18.5bn of the total market. But this is not just about the numbers, as the growth of IoT sales has been accompanied by a dramatic decline in the number of people using IoT to interact with their devices. That’s according to the findings of the new IoT report by Strategy Analytics, which found that this year, just 2% of people are using IoT devices to interact with their devices when shopping online. The IoT report, which surveyed 20,000 people across 18 countries, looked at how people are using various IoT products and devices to interact with their everyday lives. Around 4% of devices are being used in the home, around 22% are being used on the job, and about 31% are being used in healthcare. Around 11% of devices are being used in their cars or in their garages and about 10% are being used on other smart home devices and for other uses. This represents a substantial decline over last year, which saw more than six times as many people using devices in the home, on the job, and in healthcare. And this drop in people using IoT to interact with their devices has coincided with a drop in sales volumes. That is according to Strategy Analytics, which said: “In the early days of the IoT, more people were connecting IoT devices to their daily lives than would have liked and it took people a long time to figure out how to use that data”, says Peter Lee, head of IoT at Strategy Analytics. He said the “most pressing issue” with IoT is that “millions of people are still using IoT without being connected”. I’m going to take a quick detour into the world of retail, because this is what we are really talking about here. As retailers have looked out of control at some of the fundamental changes required to compete in the digital age, many have started thinking about the future of retail in terms of digital technologies. I suspect that within the next 12 months we will see a rapid rise in sales volumes and the level of activity in retail from the IoT — IoT retailers have been playing a key role in driving growth in the furniture space for many years. But with so many other factors at play, it’s not surprising that when we looked at the statistics of furniture sales it didn’t take us too far in to consider the full picture of what was going on. So how much of a factor has IoT been in retail sales? Over the next 12 months, we suggest that the IoT will have a material effect on the furniture industry. We have looked at how the IoT is changing digital interactions inside furniture retailers, and the evidence suggests that it is providing a great opportunity for growth. “It’s not just IoT but, increasingly, it’s IoT-driven interaction and interactions that are driving growth,” says Stuart Cottrell, head of retail analytics. In our research we’ve looked at how retailers interact with their customers in the world of IoT and have found some interesting findings. The first is that IoT devices are making up almost a quarter of online store visits. That’s not a surprise — people have always spent more time on the internet buying goods than they have on the physical store floor. But what that means is that IoT devices are allowing retailers to track, monitor and sell a product to the customer more closely than before. And as retailers have had to focus on tracking and monitoring consumers more closely, the use of IoT devices has become even more significant. It’s almost as if IoT devices are driving the growth of IoT interaction in the retail space. The study also looks at the number of interactions with IoT devices by different age groups. Here again what the study shows is that IoT devices are being used to interact with consumers in much the same way today as they were in the previous decade. That is, in the previous decade, the number of interactions between home and home IoT devices and a consumer’s home was much higher than it is today. In the past three years, the number of online retail interactions has increased by 30%. In the past year, though, the number of IoT interactions increased by 60%. So IoT devices are now the driving factor behind the growth of online retail shopping, and IoT interaction is certainly part of the reason for that. It is worth noting that this study shows that older people are most likely to have made more online retail purchases in the past year than they were in 2003 as a direct result of using IoT devices. That said, the data demonstrates that the age group between 35 and 54 are the most likely to have made an online retail purchase. The most likely age group to have made online retail purchases using IoT devices is the age group between 18 and 24. Why might this be the case? The study suggests there are a number of reasons, but the most obvious one is that younger adults have bought more cars over the last few years, and they are buying more electronics. We can only speculate why those differences might be making online shopping more attractive to those different age groups. So, as this study suggests, IoT and IoT-driven interaction is a key driver in the growth of online retail shopping. But even with the rise in online retail sales, the data shows that there is a very significant decline in the number of people using IoT in the retail sector. That’s a concern that we will probably look at during our analysis of IoT in manufacturing. So what does this mean for the furniture industry? Of course what is most important here is the growth of IoT in retail, and retail sales. But it’s also important to consider what it could mean for the furniture industry. So here’s what we’re going to look at. Firstly it’s clear that the IoT has been driving growth in the furniture sector and it’s a trend that will continue over the next decade. But that raises the question — is it a driver we can trust? Can we take IoT outside of the retail space and say it’s a driver in the furniture industry? That brings us to the next point — it’s a very different market to the technology industry. The technology industry, for example, has benefited from technology and technology adoption has been driven by the Internet and the cloud and many companies have taken this as the norm. The furniture industry has the opposite problem. The furniture industry has invested a lot of money in technology to try to reduce manual tasks and increase the level of automation in the supply chain. But as a result of this, it has found itself trapped in a rigid supply chain that can’t move to new markets. To address this, the furniture industry has relied on a large proportion of its staff working in other industries, which has been a costly option. But the furniture sector is now seeing this situation changing, as they’re working with new technologies like AI, IoT and robotics. And they’re starting to bring that technology back into the supply chain. We’re seeing this at an early stage and, when we look at these early stages, they often don’t turn to positive for the furniture industry, in terms of returns, but also in terms of the return on investment they are making in a new technology. And when we do analyse other industries this is likely — we’re starting to see other industries moving in this same direction. So when we look at the early stages in the furniture industry, what do we see? Firstly, we see a very different style of supply chain management, for example, the way that robots are now used in warehouse work to improve efficiency and reduce levels of human error. That’s not really seen elsewhere in the retail industry, where a rigid supply chain is the norm. But it’s happening in other industries, as well. We see a growing movement as we look at other technologies and industries, where people are starting to see technology being used in a way that is not necessarily dictated by

AI, ML & IOT driving growth of furniture industry (FAKE BLOG)

AI/ML's Health Gauge Forges Key Industry Relationships AI/ML's Health Gauge Celebrates 10th Anniversary With Its First Conference AI/ML's Health Gauge, an advanced, automated, web-based, data-driven analytics platform that monitors users' health and wellness, announced the organization's 10th anniversary and the first annual event showcasing its technology with the public at its Health Gauge Conference, held June 7–8, 2017. The two-day event also included the announcement of a partnership with the National Institutes of Health (NIH) and the launch of its first ever, live streaming event, which drew over 50,000 simultaneous users, and its first ever live streaming live event, which attracted over 12,000 participants. The event kicked off with a keynote presentation by CEO, Dr. Dariush Mozaffarian. “Dr. Mozaffarian is a prolific speaker about how to make a difference, and his presentations are inspirational and educational,” said Gary Krakow, CEO of AI/ML's Health Gauge. As Mozaffarian began his presentation, attendees could be heard exclaiming, “How is it possible that he came from a Harvard Business School and Stanford business school to become a world renowned doctor and entrepreneur?” The first annual Health Gauge Conference was also accompanied by a series of special webinars that outlined the Health Gauge methodology, as well as the organization's partnership with NIH. “We're thrilled by the impact that AI and ML can have on people's lives. And we're thrilled about how the technology will transform traditional healthcare,” Mozaffarian said during his hourlong presentation. The conference also included a demonstration of the Health Gauge's proprietary technology with Dr. Mozaffarian and a panel of medical professionals. ”We've been extremely humbled by the positive response and feedback from our inaugural conference, our first live event, and especially this year's conference,” Mozaffarian said. “We look forward to a long, fruitful collaboration with AI & ML Health Group.” AI/ML's Health Gauge Celebrates 10th Anniversary with “Healthy, Intelligent, Happy” Video At the conference, attendees were able to view AI/ML's Health Gauge's flagship product - the Health Gauge app for the Web - on the Web, and then with the new Health Gauge mobile app, as well as with the Health Gauge for iPad and iPhone. AI/ML's Health Gauge's flagship app, the Health Gauge app for the Web, was featured in Apple's Health app. “The fact that this product is accessible anywhere, is highly intuitive for users, and fits in beautifully with our brand identity has really enabled us to make this first conference about our industry and not about us,” said Krakow, who noted that the Health Gauge mobile app for iPhone and iPad and the Health Gauge for iPad have been “incredibly well-received.” As AI/ML's Health Gauge was being introduced, attendees were also invited to watch the organization's 10th Anniversary, a 20-minute film, “Healthy, Intelligent, Happy,” which illustrated the Health Gauge's health-monetizing capabilities. Mozaffarian was joined by several Health Gauge team members, including Drs. Alex Ostrovsky and Michael Ravetakis and Senior Scientist and Head of Strategy, Lisa Mott. The conference included a short video trailer of the new Health Gauge app and was accompanied by its first ever live stream event, which attracted over 12,000 participants. The Health Gauge platform monitors users' levels of health and wellness and correlates that information with a user's past actions to develop targeted, personalized recommendations. The platform is based on a proprietary algorithm that is able to accurately match, or “signpost”, users' historical health conditions with current data. AI/ML's Health Gauge's technology is an “intelligent personal assistant” that is “a personal health coach,” according to Mozaffarian. “It knows exactly what an individual can do and not do, and how to direct them toward the best results,” Mozaffarian said. The Health Gauge platform is focused on empowering users to make better, more healthy decisions about their actions and lifestyles by prompting users to take more healthy habits. “We don't want a doctor telling you what to do,” Mozaffarian said. “We want you telling us what to do.” To make this happen, the Health Gauge platform “monitors and correlates a user's individual health conditions with their personal health habits,” Mozaffarian said. “Over time, our platform learns what you've done, and it matches that with what you've previously done, and then it recommends what you should do next to improve your health.” AI/ML's Health Gauge's platform can also provide personalized recommendations to users on their use of medication, diet and exercise. “Our recommendations can help to get you to a healthier place and help you to achieve better results,” Mozaffarian said. “The recommendation is what you should do next.” AI/ML's Health Gauge began with a vision of making healthcare more personalized and predictive. “We wanted to leverage AI and ML to make healthcare better for you. By leveraging machine learning and natural-language processing, we are creating a system that understands health behaviors and can create intelligent personal assistants on the fly,” Mozaffarian said. “Our goal is to empower users, so that they can take control of their own lives and make better, smarter decisions that will change their life for the better.” AI/ML's Health Gauge's unique, proprietary technology has “led to unprecedented opportunities to improve people's health and lower healthcare costs,” Mozaffarian said. The Health Gauge platform also has the potential to address challenges faced today by other organizations in the healthcare industry. A significant part of the Health Gauge team is devoted to the organization's partnership with the NIH, and Mozaffarian described the Health Gauge with NIH and the American Recovery and Reinvestment Act of 2009, “as one of our first programs.” AI/ML's Health Gauge was selected to provide data for the Human Phenome Project, a major initiative of the NIH's National Human Genome Research Institute. “We were excited to be selected. It just goes to show how important our partnership is as we're about to see the first results of this important initiative,” Mozaffarian said. “From a scientific perspective, our project is based on the NIH's strategic vision to leverage the power of human genome sequencing and clinical data in understanding and treating diseases and other conditions,” Mozaffarian explained. “The goal is to use the data to better inform decisions made by health care providers.” The Health Gauge's partnership with the NIH is part of a broader effort to make healthcare more predictive and personalized. Mozaffarian said the effort to make recommendations more personalized began in 2014 with a partnership with IBM Watson's Natural Language Processing (NLP) project. This partnership allowed AI/ML's Health Gauge to contribute to the Watson Health project, “an initiative to create technology that helps people better understand their health, improve their health and make better, smarter decisions in their everyday lives” using Watson's NLP technology. AI/ML's Health Gauge also partners with the IBM Watson and IBM Watson Health projects to identify, track and monitor diseases and conditions, and other lifestyle aspects that can be used to inform the prevention of chronic diseases. The Health Gauge is using IBM WebSphere and IBM Watson for Data Science technologies such as IBM Watson’s NLP, IBM Watson's Text Analytics, IBM Watson's Conversation Analytics, and IBM Watson's Entity Extraction to analyze health behaviors to better understand users' current health condition and to understand what the individual can do to improve their health. “This is just the beginning,” Mozaffarian said during his presentation. “In 2015, the world is going to be a better place when users are empowered to make informed decisions about their health.” AI/ML's Health Gauge's first ever live streaming event featured its technology with Dr. Mozaffarian, as well as an overview of how Health Gauge's technology is empowering users with tools to track and manage their health. “We're thrilled by the response and feedback we've received for our first live event, our first live stream event and our new mobile app,” Mozaffarian said. “We look forward to a long, mutually beneficial collaboration with AI & ML Health Group.” At the event, attendees were able to watch AI/ML's Health Gauge's tech with Dr. Mozaffarian on the Health Gauge platform and then watch it with the Health Gauge for iPad app and Health Gauge Mobile app. Mozaffarian also talked about the Health Gauge’s strategy to make the health and wellness of users “unpredictable and dynamic,” and the organization's vision to bring AI/ML's Health Gauge to the health care industry. “Our vision is to help people make better, smarter decisions about their health,” Mozaffarian said. “To do that, we need to track what the individual can do.” “We need to create a system to predict our health and to make recommendations on how

AI/ML's Health Gauge Forges Key Industry Relationships (FAKE BLOG)

Alluxio to Present at ApacheCon about Data Orchestration for Machine Learning At the end of last week’s announcement from Alluxio and RedHat of a strategic alliance to work together on open source data distribution and orchestration solutions, we had a couple of press inquiries about the announcement. I answered a few questions in the Alluxio Mailing List on what Alluxio is planning to do, its strategy, and its overall impact as it relates to the open source world. The original questions came from: @fritsch: Does this mean that Alluxio now has a distribution and orchestration product based on the Mesos foundation? No, it doesn’t. The Alluxio project has always focused on being an abstraction layer on top of HDFS. We have been making progress developing an abstraction layer on top of Apache Mesos. When we announced the strategic alliance, we planned to build a solution based on our abstraction layer on top of Mesos rather than directly integrating with Mesos to leverage all the other capabilities of Mesos. Today, we are still working on this capability, although we have several more releases left before we formally announce it and open source the project. I don’t know if this will answer all the questions, but I wanted to give the best answers to a number of the most frequently asked questions about Alluxio and Red Hat. “What does that mean for Alluxio?” I’m going to start off by talking about Alluxio — the project — and explain why it’s important. I’ve been working in the Apache community since I joined Oracle to be part of the Apache Incubator project. You can read about those early days from 2009-2012 through my history of contributing to the project on the Apache Project Blog. But for the last 4-5 years, the Alluxio project has become one of the top contributions to the Apache Incubator project — by far. Alluxio has become the biggest component of the Incubator as a number of other projects have grown in popularity around it. We have been talking to a lot of people about the Alluxio project — we have around 100 companies using it at this point, and we have had numerous companies reach out to us to join Alluxio because they needed something to do with large scale data and their own data lakes, etc. We’ve built an ecosystem around Alluxio that is not like how we have built other projects in the Incubator. We have a lot of companies using Alluxio and a lot of users around the world. Our community contributors have been very receptive to community contributions because it enables them to work together rather than us doing all the development work. And unlike OpenStack, we also have open source community support around it, which is very important to us. The project started in 2011 as a fork of the hdfs project because Hadoop developers weren’t providing features that we needed in order to build a data distribution platform. We had been using our own solutions to distribute data before, but we wanted a platform with better data distribution. When we made an RFE to merge the project back into the hdfs project, the development of Alluxio continued because Hadoop’s maintainers were a little apprehensive about the integration from the beginning. Some of this was just the normal bureaucratic process of getting a new project merged into a more established project. I started contributing soon after, writing a lot of the new implementation and fixing a lot of bugs we had gotten into while developing the project. At some point in 2015 we became a company. I was at Oracle, and had been planning to leave Oracle. When our company had funding issues and wasn’t going to have funds available any more, I decided to move into the Apache Foundation to become part of the Alluxio project. To do that, we were required to form a new company called Alluxio, Inc. (I’m still CEO). Since being part of the community, I’ve worked on the alluxio code base for over 4 years. On the mailing lists and at conferences, we’ve been talking to a lot of people and companies about the project since we started using it for our own use; now we are really the only project in the Incubator to have large scale users of it. We’re one of the highest contributors and most contributors in the Incubator. We have been growing the number of users much faster than any other project. Now it is the turn of the Data Center community members to look up from being used in the datacenter, and begin using data they’ve been creating, and data they’ve been accumulating as part of their work. In other news, we are about to open source our abstraction layer for Mesos, and it’s alluxio-core project in a few weeks. We are still working on our project in the Incubator, getting ready to release a formal product to the community. “Do you think Alluxio will influence Hadoop?” Yes, and we have done this in a couple of different ways. First, I believe that as the data lake concept has caught on and as software developers start creating a datastore for their companies’ data, that this will influence the Hadoop community. As the data lake concept grows, many of the questions related to how to store and distribute large amounts of data are similar to the problems Hadoop solves. Hadoop solved those problems better when it was initially conceived, and the data lake concept has now become a new way to solve those problems. We have been able to add new functionality with the Alluxio project that we couldn’t have added otherwise. “Do you think Oracle may have been interested in the project as well?” Yes, I think they were. I’m sure that they were when we were initially starting development as it was the only project being built around these concepts at the time. I believe that the projects within the Apache Foundation are all pretty similar, and that none of them have any particular interest in one company. “But Hadoop is open source! Isn’t that the same thing as Oracle?” I’m going to come back to that point for a second, just so we’re clear about what we are talking about. We at Alluxio are not interested in creating Hadoop clones, but we have added to the Apache Community, as a company, with a lot of passion and enthusiasm, and I don’t believe we’ve taken proprietary technologies from our research work at our own company. For instance, we created a project called Hadoop-HDFS-Plus with the intention of adding things to HDFS that we needed and that companies outside of our own company needed. For example, we needed a way to better manage large amounts of data and data processing, and Hadoop doesn’t have great solutions for those problems. We’ve had companies reach out to us through the Alluxio project about HDFS-Plus. But that doesn’t change the fact that Hadoop was founded on the philosophy that it was an open project with a shared mission, and the goal was to have that mission succeed as well as possible regardless of a company like Oracle stepping in to fund efforts that they deemed important by donating to them. We have had the same goals and goals of getting companies to use our project even if we had a competing product from a company. We just wanted to get it better for all the people that needed a data distribution platform. “Aren’t all databases based on open source? Oracle doesn’t develop anything proprietary — that would mean it would mean it would stop being able to run on hardware from other companies.” They do sometimes have proprietary code — just as Linux is derived from the GNU Public License but has many proprietary elements in the kernel. Hadoop and all the data in it would be available from the Apache Foundation and many companies if Oracle didn’t buy some of them. We make a lot of our code available in a public Github repository; we license it under the Apache licenses. The Alluxio project isn’t about putting proprietary aspects into a public project. We use free software for our own project from the beginning in 2009, and we haven’t ever had any problem licensing our proprietary code from us. We have a lot of companies using it, and the project has become an umbrella project for several other Apache projects which use its functionality, since it is such a large component. We don’t have any interest in having proprietary pieces of Alluxio. The Alluxio project is about making HDFS better — it was originally a fork of the HDFS project for the same use we are doing for Alluxio. If Hadoop ever comes to have a proprietary model in which it uses Hadoop Plus, which is what we think are the features our company wants to deliver to other companies and it is the way we think Hadoop should continue, we would have no problems with it. As of now, we have no interest in that. We are working on open source technologies with the other Apache projects, and we think that people would like to see Hadoop get better for us — as well as other companies, who also need a data distribution platform — and to have as many people as possible use Hadoop. “Are there other competing platforms within the Apache foundation?

Alluxio to Present at ApacheCon about Data Orchestration for Machine Learning

Apple's Director of Machine Learning Resigns Due to Return to ... Microsoft? Apple may be finally starting to see some of the benefits of machine learning, and it appears that Apple’s in-house machine learning efforts are having a huge impact. Earlier this year, I was one of many tech heads to go to a launch event held by Apple for a new video series called “Planet of the Apps”. The series, which launched on June 27 is Apple’s version of “This is Apple”, but for iOS apps. At the launch event, we saw the debut of the debut of Apple’s new app, called Home. Not only does Home serve as the new lock screen for the iPhone, but it also does things that weren’t possible before Home (or any other app on Apple products). Home is the first app that uses machine learning. I’m not sure if Home is the first app in the App Store to use machine learning, but it’s the first to use machine learning in a way that other apps simply aren’t possible. In Home, you can set a day that you want, then create a schedule of events to make sure that you don’t miss a meeting, a birthday or anything else that has a reminder. In this way, it’s much like Google Offers, but Home gets better. The scheduling is smart. For example, once I create a calendar for a certain day, any reminder I create is automatically pulled from my calendar and sent to me the next day to remind me. But Home is more than just a simple scheduling app. While you can set the day in advance when you want to be scheduled, you can also set events that will be automatically created for you when your next calendar event is ready. In this way, Home has changed how scheduling is done on your phone. No longer do you have to keep a small calendar of scheduled events and appointments. It’s all done automatically. For example, when I go to work, I set my day in advance. If I happen to stop by a coffee shop on the way to work, it’s automatically added to my Home calendar, ready for me to see when I finally make it to my desk. Home lets you create a calendar event for any person that will be added to your calendar, so if you find someone interesting, you can add them to your calendar. You can even set things like, “if this person’s name is Steve, I want to see him email me on his birthday,” which will automatically be sent to you. In Home, you can also set a time zone, which will create an event every day at that time zone (or close to). So, you can have a time zone in Florida, and see if anyone ever emails you in real time while you’re sleeping in California. You can also set a location, and then see who’s in your location. This could become a fun activity to see who’s in your neighborhood or friends’ neighborhood. It could also let you know that there’s a group of people in your area that you don’t know (yet). Or, it could tell you who is in your building, so you don’t have to worry about where your parking is, and walk from elevator to your office. Home makes machine learning more real world. Machine learning is the ability to tell the machine what to do. The problem is that people always think machines will use machine learning to do something they want done. When Home was announced, most of the commentary was that Home would take over the lock screen of the iPhone. Instead, Home just took over scheduling. In the future, though, Home’s machine learning will become more powerful. Eventually, Home is going to be the perfect assistant to your iPhone, serving as not only a simple calendar, but more importantly, becoming your intelligent personal assistant. Apple Watch and iMessage App Home will allow you to have notifications on the home screen of your phone. Now, let’s be clear. I get that Apple is trying to do this now to take some heat off of iPhone 7’s screen notch (it’s also going to make Home stand out from the other home screens of apps that don’t have notifications yet). I also think Home is a great attempt to take some of the heat of iOS 10’s Notification Center. However, Home doesn’t do notifications well. In the future, Home will be able to add things to your calendar with notifications. It will send you text messages, and even calls that are important to you. The next stage will be iMessage. Messages on Apple devices will eventually integrate machine learning. So, you won’t have to worry about what your friends think of a joke you told in a group on iMessage for the entire world to see. What do you guys think? Tell us what you think about Home and Apple’s machine learning efforts. My wife and I got a 1 year Apple service plan recently because I’m a contractor and could use the iPhone. We really didn’t want to have a monthly payment and I hate paying a data plan, but this is a good, cheaper way to have it all on one bill. I’m very impressed, there are apps that do this already, however none of those have the scheduling prowess of Home. This makes Home super useful for me, my wife and any of our family or friends that do not have an iPhone. Thanks guys!! I don’t know what home does but Apple’s “Siri on your Home Screen” app was pretty useful in letting me make a reservation for a sushi spot without opening Facebook or Twitter, for example. It doesn’t seem to be much more useful than the iPhone app though. I agree with most of the sentiment. This is just a gimmic. I just wish that they would have made the home screen the lock screen. I wish they’d done this back for the 5S. But a home screen home screen and then just have an iPad or iPod with a notification screen was a bit more useful. I think this is just a gimmick because, really, what else in iPhone 6S+ allows people to schedule anything? It should just be automatic. Having this allow you to automatically schedule events on a daily/weekly/monthly basis is cool but should be a bit more polished. I have an Apple Watch, and I don’t see the benefit of this. It looks like Apple has integrated iMessage into the iPhone and Apple Watch. Notifications are not going to be great with Watch, and iMessage on Watch really doesn’t add much more. Also, while a year is not a lot for a contract, it’s a bit short on the Apple Watch, too. At least for the first few months after availability, I think Apple Watch demand will outstrip Watch supply. In all honesty, its kind of nice to have that kind of automated reminder functionality. I have 3 kids at home and my phone is never a consistent “home base” so I have to manually “wake up” myself so I know when to start “shopping for school supplies” or what have you, and it would be a real pain if even that didn’t get automated. The other thing I’d like is it to remind me of special holidays, like your wife’s Birthday or Christmas day. Maybe it’s possible to get these type of events based on an iOS calendar, or maybe based on certain events occurring on my calendar, or just what my friends’ calendars have. Home is my favorite app in 9.0.1, I never had to deal with any of the issues the iPhone had. I love it’s new capabilities and it never fails me once my alarm is set. I would love if Apple would make a version for the watch as well. How is Home going to automatically set an app alarm and send a text (SMS) on my behalf? I think Apple really wants to push forward in areas like this to make Apple more interesting and more compelling. If Home ever learns that I told my wife, “Hey, go to the Apple store” while she wasn’t listening to Home, it isn’t going to do what I asked at that moment. It was programmed to learn a lot about me. But it doesn’t know who I told it to. It’s going to keep asking me all the time, until all my secrets are out! I can’t wait until I have to make a new schedule so that everyone in my family knows when each of us needs to know, or if I am making time for myself, when I need a break (I like taking lunch), but if it is for the kids, when they need to know to get ready for something, and I won’t be coming home until a certain time. Not knowing when to start dinner for everyone. This concept seems to be very much the same as Microsoft’s Cortana, Amazon’s Alexa, Google’s Assistant, etc. – they all take on specific functions and not others. It’s really only useful for a few specific things as a personal assistant. If Microsoft, Amazon, or Google can create a single system that is very intelligent for “all things” that all the various apps could share, then it would be incredible. Apple isn’t there yet.

Apple's Director of Machine Learning Resigns Due to Return to ...

Building explainability into the components of machine-learning ... Machine learning Machine Learning in AI is the set of research areas that deals with... The best books on AI The books on Artificial Intelligence by Richard Hamming is a must read for AI.... Machine Learning in Practice: With Examples in R & Python, Second Edition Machine Learning in Practice: With Examples in R & Python, Second Edition is a textbook for the "how to"... Book: AI: A Modern Approach, Second Edition This textbook is about "AI" from a computer science perspective. The course covers... Machine learning (ML) is the science of "learning" algorithms. These algorithms build a model out of "data" like... How to Build an Artificial Intelligence: A Beginner's Guide to the AI Revolution (Guidebook) How to Build an Artificial Intelligence: A Beginner's Guide to the AI Revolution (Guidebook) by David A. Velleman: An... How to Build a Neural Network An R tutorial on how to use an ML library. Learn about some of the key components and see... Artificial intelligence - Wikipedia Artificial intelligence is the branch of computer science, engineering, or mathematics that focuses on designing computers that have human-like abilities, including the ability to learn and adapt. Artificial... The Complete Book of Artificial Intelligence Algorithms The Complete Book of Artificial Intelligence Algorithms is the first book to cover all known algorithms in artificial intelligence. Written by... Artificial Intelligence Artificial Intelligence (AI) is intelligence or the ability of a machine to act like an individual of its species. An artificial intelligent system, sometimes referred to as AI... Artificial intelligence. A very brief introduction Most people know what AI is now, and probably have a mental image they associate with the term. I like to describe the field of AI as consisting of a group of people, each striving to understand another group of people, trying to figure out what kind of thing, if any, can be "learned." The first steps toward that knowledge involve learning the laws governing which data can be considered to be "knowledge" and which are not; from that, the fields of knowledge representation and search, and finally, machine learning, can be derived. There are many sub-fields within the broad area of AI: computational linguistics, computer vision, expert systems, autonomous agents, expert systems, pattern recognition, intelligent tutors, expert systems, machine learning, and so on. They form a sort of tree, with each "trunk" at a "higher" level of complexity, but all branches at the same depth.tics, computer vision, expert systems, autonomous agents, expert systems, pattern recognition, intelligent tutors, expert systems, machine learning, and so on. They form a sort of tree, with each "trunk" at a "higher" level of complexity, but all branches at the same depth. All the "branches" start at about the same level as the "trunk". In other words, each branch of AI is usually regarded as the "higher level" of a "trunk" of AI that is itself a component of "lower level" AI. Most of the branches can be thought of as sub-branches of AI in so far as they involve machines that work at, or operate at the same level as, human beings. Of course, a person does not possess their own neural net and "learns" to think like a computer, but most of us have certain skills that can be learned. Some of the other branches of AI involve machines that work at or operate at a level lower than that of human beings. For instance, some computers can play sophisticated video games, some can talk, see, or navigate, and some can even read and write, in spite of the fact that computers do not have the capacity to make direct comparisons to determine if one sentence makes sense or not. The ability to learn might be the only distinction between artificial intelligence and mere "experience". The tree of AI is far away from its completion, and it is quite possible that we will make it to its root someday. This is why many AI experts and researchers are interested in the idea of "strong AI" and others want to create a "strong AI." Others have even suggested that we have achieved such a thing in the form of computers who themselves write AI. There may never be a day when they are not talking this "language," and it is quite likely that we will never "know" or "understand" this language. However, there are still a lot of branches and a lot of learning left to learn, so far. A.I. is at its infancy, and there are no signs of slowing down anytime soon. A human infant is more intelligent than many of the first generation A.I. programs we have.vel of complexity, but all branches at the same depth. All the "branches" start at about the same level as the "trunk". In other words, each branch of AI is usually regarded as the "higher level" of a "trunk" of AI that is itself a component of "lower level" AI. Most of the branches can be thought of as sub-branches of AI in so far as they involve machines that work at, or operate at the same level as, human beings. Of course, a person does not possess their own neural net and "learns" to think like a computer, but most of us have certain skills that can be learned. Some of the other branches of AI involve machines that work at or operate at a level lower than that of human beings. For instance, some computers can play sophisticated video games, some can talk, see, or navigate, and some can even read and write, in spite of the fact that computers do not have the capacity to make direct comparisons to determine if one sentence makes sense or not. The ability to learn might be the only distinction between artificial intelligence and mere "experience". The tree of AI is far away from its completion, and it is quite possible that we will make it to its root someday. This is why many AI experts and researchers are interested in the idea of "strong AI" and others want to create a "strong AI." Others have even suggested that we have achieved such a thing in the form of computers who themselves write AI. There may never be a day when they are not talking this "language," and it is quite likely that we will never "know" or "understand" this language. However, there are still a lot of branches and a lot of learning left to learn, so far. A.I. is at its infancy, and there are no signs of slowing down anytime soon. A human infant is more intelligent than many of the first generation A.I. programs we have. AI is at the very beginning. It has yet to reach any sort of maturity. There isn't much sense talking about "expert" systems that "master" chess. There is nothing to "master," and everything is just a game to be played.evel lower than that of human beings. For instance, some computers can play sophisticated video games, some can talk, see, or navigate, and some can even read and write, in spite of the fact that computers do not have the capacity to make direct comparisons to determine if one sentence makes sense or not. The ability to learn might be the only distinction between artificial intelligence and mere "experience". The tree of AI is far away from its completion, and it is quite possible that we will make it to its root someday. This is why many AI experts and researchers are interested in the idea of "strong AI" and others want to create a "strong AI." Others have even suggested that we have achieved such a thing in the form of computers who themselves write AI. There may never be a day when they are not talking this "language," and it is quite likely that we will never "know" or "understand" this language. However, there are still a lot of branches and a lot of learning left to learn, so far. A.I. is at its infancy, and there are no signs of slowing down anytime soon. A human infant is more intelligent than many of the first generation A.I. programs we have. AI is at the very beginning. It has yet to reach any sort of maturity. There isn't much sense talking about "expert" systems that "master" chess. There is nothing to "master," and everything is just a game to be played. It would come as no surprise to see an AI computer who just outsmarted itself and got its own brainwashed. It is not going to surprise me. A.I. will get to a new stage as soon as we have finished this. It just happens to be that the way it gets there is not very predictable now, and that isn't stopping any artificial scientist. No doubt you are aware of the recent AI experiments where people are able to 'talk' to these programs in language that they have been told to 'learn' in. As this progresses we are seeing a clear shift of the balance of power on the planet towards machines which may well come to exceed our capacity for rational judgement.

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