Artificial Intelligence, 1 dari 5 Teknologi yang Hadir di 2023

Hingga saat ini, kemajuan teknologi menjadi salah satu sektor yang memiliki perkembangan pesat. Di mana, hal tersebut tidak bisa dipungkiri dengan angka kebutuhan dan penggunaannya yang kian meningkat dari tahun ke tahun. Untuk itu, tidak heran jika berbagai inovasi baru terus-menerus diciptakan untuk mendukung masyarakat dalam aktivitas dan kinerja hariannya.

Artificial Intelligence hingga mobil listrik menjadi salah satu hasil dari inovasi teknologi beberapa waktu belakangan ini yang disambut baik oleh masyarakat global. Tentunya, kemajuan teknologi di tahun 2023 juga diharapkan tidak kalah canggih dibanding dengan tahun-tahun sebelumnya. Lalu, apa saja perkembangan teknologi yang mungkin hadir pada tahun baru ini? Ketahui informasi selengkapnya berikut ini.

Source: ConstructConnect Canada

Kendaraan Listrik yang Kian Ramai Digunakan
Kendaraan listrik menjadi teknologi pertama yang mungkin alami perkembangan pesat di hadir tahun ini. Di mana, berbagai merek mulai mengeluarkan mobil hingga motor elektronik yang ditenagai oleh listrik untuk temani aktivitas harian penggunanya. Karena respon positif dari masyarakat, tidak heran jika inovasi tersebut akan kian marak digunakan pada tahun 2023 ini.

Pasalnya, industri otomatif sedang melakukan perombakan maksimal untuk beralih dari mesin bensin ke mesin bertenaga listrik. Walau digemari oleh sebagian besar masyarakat, namun teknologi ini tentu masih melalui proses pengembangan untuk raih hasil terbaik dan bisa dinikmati secara nyaman dan aman.

Artificial Intelligence untuk Lakukan Pekerjaan
Selanjutnya, kecerdasan buatan atau AI adalah teknologi berikutnya yang mampu menemani kinerja pengguna untuk maksimalkan aktivitas harian. Di mana, teknologi ini merupakan pengembangan pada bidang ilmu komputer yang mampu berikan pemecahan masalah kognitif hingga lakukan berbagai tugas layaknya dilakukan oleh pola pikir manusia.

Belakangan ini, Artificial Intelligence sudah tidak lagi asing didengar telinga karena telah beberapa kali dipakai pada alat elektronik maupun sektor industri. Pada tahun yang baru ini, kemungkinan meningkatnya kecanggihan AI pun telah dipastikan oleh beberapa pihak. Pasalnya, teknologi tersebut mampu berikan kemudahan dalam beberapa bidang untuk dinikmati masyarakat luas.

Salah satu prediksi kecanggihan AI yang akan rilis pada tahun ini adalah perannya dalam menjadi reporter. Reporter AI akan mampu memberikan pertanyaan, jawaban, bahkan menyusun cerita original tanpa dibantu pihak lain. Tidak hanya itu, Artificial Intelligence juga mampu menyampaikan berita seperti reporter pada umumnya. Dengan begitu, teknologi AI yang akan datang mungkin akan mempermudah aktivitas dan kinerja pengguna dengan fitur-fitur canggih yang dimilikinya

Perangkat Apple Lipat
Lanjut pada teknologi berikutnya yang diprediksi hadir pada tahun 2023 ini adalah perangkat lipat keluaran perusahaan ternama, Apple. Di mana, tren handphone lipat memang sedang naik daun sejak beberapa waktu lalu. Untuk itu, tidak heran jika masyarakat menunggu kehadiran perangkat lipat Apple dengan berbagai teknologi canggih yang mendampinginya.

Pasalnya, tidak sedikit saingannya yang telah melebarkan sayap dengan mengeluarkan perangkat lipat seperti Samsung Galaxy Fold dan Oppo Flip. Langkah drastis tersebut pun disambut baik oleh pecinta teknologi karena keunikan dan teknologi terdepan yang ditawarkan. Untuk itu, terdapat kemungkinan yang cukup besar untuk Apple turut mengikuti jalan kompetitornya dengan membuat perangkat lipat pada tahun 2023 ini.

Smartphone Tanpa Colokan Apapun
Walau belum mengeluarkan perangkat lipat, namun Apple telah mendahului para saingannya dengan membuat smartphone tanpa colokan apapun. Di mana, teknologi tanpa port tersebut mungkin akan jadi tren selanjutnya pada tahun ini karena perkembangannya yang kian canggih. seperti yang kita ketahui, port headset belakangan ini telah diminimalisir keberadaannya dari berbagai keluaran smartphone terbaru.

Pasalnya, headset Bluetooth telah mengambil alih kemudahan mendengarkan audio tanpa perlu memusingkan kabel dan colokan. Tidak hanya itu saja, teknologi pengisian daya tanpa kabel pun sudah lama hadir untuk permudah aktivitas dan mobilitas pengguna. Dengan berbagai perkembangan yang kian canggih, tidak heran jika di tahun 2023 ini smartphone tanpa colokan akan mengambil alih pasar gadget.

Source: Pueblo de Oro

Konsep Sustainability 
Teknologi terakhir yang diprediksi hadir pada tahun 2023 ini adalah Sustainability. Di mana, konsep keberlanjutan tersebut merupakan cara efektif untuk cegah masalah iklim serta meningkatkan kesadaran masyarakat global. Permasalahan terkait sustainability memang telah sejak lama diangkat untuk berikan kualitas hidup terbaik bagi penduduk global.

Konsep sustainability diprediksi kehadirannya karena masalah terkait perubahan iklim yang belum juga tuntas hingga detik ini. Untuk itu, teknologi pendukungnya kemungkinan besar akan rilis pada tahun ini untuk mendukung sumber daya bumi sehingga bisa digunakan dalam waktu yang berkepanjangan.

Itu dia sejumlah teknologi yang diprediksi akan hadir untuk permudah aktivitas dan kinerjamu pada tahun yang baru ini. Hal itu berkat berkembangan teknologi yang semakin pesat dan canggih salah satunya pada sektor smartphone yang bisa didapatkan secara online melalui Eraspace. terdapat berbagai produk gadget dari sejumlah merek ternama yang bisa kamu pilih sesuai kebutuhan dan bujet.zzCaranya pun cukup mudah yaitu kunjungi website resmi Eraspace atau download aplikasinya di smartphone. Yuk, dapatkan gadget canggih yang mampu cukupi berbagai teknologi masa depan hanya di Eraspace, sekarang.

Source https://eraspace.com/artikel/post/artificial-intelligence-jadi-1-dari-5-teknologi-yang-mungkin-hadir-di-2023

source:https://www.dewaweb.com/blog/mengenal-google-colab/

Internet of Things telah menjadi Buzzword yang sering diucapkan para pengguna Internet di Indonesia akhir-akhir ini. Pada intinya IoT mempunyai pengertian bahwa Internet telah berintegrasi ke komputer kita, ponsel, dan peralatan elektronik lainnya, dan mulai hidup di benda-benda sekitar kita, dari lemari es, termos, kursi, meja, lampu lalu lintas, lampu belajar, dan masih banyak benda yang dimungkinkan untuk ‘ditanami’ internet. Beberapa penelitian awal mengenai konsep IoT salah satunya dilakukan oleh Neil Gershenfeld dari Massachuset Institute Technology (MIT) media Lab. Pak Neil menyebutnya ‘Internet O’, konsep yang mengeksplor bagaimana objek (benda) dapat berkomunikasi dengan kita, bagaimana cara kita berkomunikasi dengan benda-benda tersebut, dan bagaimana benda-benda tersebut dapat berkomunikasi satu sama lain, sehingga kita dapat mengendalikan kehidupan menjadi lebih baik. Pada tingkat konsumen, kita ambil sebuah contoh menjadikan rumah lebih pintar dan efisien dengan menghubungkan pendingin udara, oven, lampu teras, sehingga sebelum sampai ke rumah kita dapat mengirimkan pesan kepada mereka untuk siap ‘melayani’ kita (ideas.ted.com). Dalam skala yang lebih luas, sebagai contoh New York memanfaatkan IoT untuk menanggulangi salju dari sensor yang melekat pada bajak salju dan teknologi pemetaan interaktif, warga mendapatkan banyak informasi tentang kondisi jalan dan dapat meminta dinas kebersihan jalan untuk membersihkan jalan dari salju secara lebih efektif (theinternetofthings.eu). Kebijakan IoT Pemerintah Berbagai Negara Pemerintah negara-negara Asia seperti China dan India telah melihat peluang dan tuntutan IoT di masa depan yang akan mengubah kehidupan sosial, ekonomi, juga politik, sehingga pemerintah merasa perlu untuk mengeluarkan kebijakan yang akan me-leading arah pembangunan IoT. Awal tahun ini, pemerintah India mengeluarkan dokumen kebijakan IoT yang menargetkan $15 Miliar Pasar IoT di India pada tahun 2020. Langkah pertama untuk menuju cita-cita tersebut telah dimulai baik oleh sektor pemerintah maupun swasta. Sebagai contoh Tata Motors, produsen kendaraan komersial terbesar di negara tersebut telah mulai menempatkan sensor ke dalam truck dan telah membangun model layanan yang memungkinkan pemilik armada dapat mengetahui secara lebih luas kendaraan yang sedang digunakan dan memprediksi potensi kerusakan (economictimes.indiatimes.com). IoT telah menjadi rencana kerja resmi pemerintah China dan dengan cepat menjadi industri strategis dengan Jiangsu menjadi kota pusat pembangunan IoT yang telah dimulai sejak tahun 2009. Dalam rencana lima tahunan China, IoT adalah salah satu dari tujuh strategi industri berkembang (Strategic Emerging Industry) dengan alokasi dana dari pemerintah sebesar 5 Miliar RMB (Renminbi atau CNY-China Yuan) untuk 5 tahun ke depan. Pemerintah-pemerintah daerah China telah beramai-ramai mengalokasikan dana untuk membangun ‘Smart Cities’ (technode.com). Berdasarkan proyeksi data Asosiasi Penyedia Jasa Internet Indonesia (APJII) diperkirakan pengguna Internet di Indonesia akan mencapai 139 Juta pada tahun 2015. Di mana pengguna mayoritas berada pada usia 16-25 tahun. Bagaimanakah dengan kebijakan pemerintah Indonesia mengenai strategi pembangunan IoT? nantikan pembahasan selanjutnya. (MJ/hdn) Referensi:

• http://ideas.ted.com/2014/10/02/whats-next-for-the-internet-of-things/
• http://economictimes.indiatimes.com/industry/auto/news/auto-technology/on-way-to-15-billion-market-internet-of-things-gaining-ground-in-india/articleshow/45214784.cms
• http://technode.com/2012/05/14/internet-of-things-not-just-a-concept-for-fund-raising/
• http://www.theinternetofthings.eu/brian-heaton-internet-things-helps-cities-snow-remova

sumber: https://bpptik.kominfo.go.id/Publikasi/detail/mengenal-internet-of-things-iot

There are many DBMSs on the market, and many of them use SQL. How can you choose the right database for your business? One way is to look at the most popular databases and compare them.

There are few well-known rankings which collect data each year or month and publish the results. We will now focus on three of them: Stack Overflow Survey, DB-Engines Ranking and Red 9. It is a good practice to compare data from different sources. If a database is ranked high in every survey, it must mean something.

Reading the Stack Overflow Survey can be addicting. It explores not only types of databases but also programming languages, Cloud platforms, and web frameworks and technologies. This is a good way to get to know how many technologies are available. For now, though, I will focus only on the top five databases from these surveys. You can always check the surveys for yourself if you want to learn more.

The Stack Overflow report divides its results into three categories: data from all respondents, professional developers, and learners. Votes are distributed differently, but the first five places belong to the same databases regardless:

1. MySQL
2. PostgreSQL
3. SQLite
4. MongoDB
5. Microsoft SQL Server

When it comes to the details, we find that professionals said the best solution is PostgreSQL. Those who are learning SQL are more likely to choose MySQL. All responders were answering the question: “Which database environments have you done extensive development work in over the past year, and which do you want to work in over the next year?”.

Source: Stack Overflow Survey

This Survey also has an interesting summary of the most loved, most dreaded and most wanted DBMSs. Here the biggest difference between loved and dreaded went to PostgreSQL. Here’s the full result:

Source: Stack Overflow Survey

It is also interesting to look up the “top paying technologies”. The first five spots don’t have any databases that were ranked as most loved or popular. A less popular database likely means fewer professionals know it, which can make it more profitable. PostgreSQL is in 9th place on this list, followed by Microsoft SQL Server, SQLite, Oracle, and MongoDB. Professionals working with these databases earn from a bit under $60,000 to over 70,000 USD per year.

rank of your favorite database almost like the stock market. The top five looks like this:

Source: DB-Engines

Notice that SQLite has fallen to ninth place, while Oracle has taken the top spot.

DB-Engines ranking has few nice features. After clicking on a database, you can see a description of it. You can also choose two different databases to compare them via a summary.

The Red9 ranking also allows you to compare database popularity, but it shows the data on a graph. It too is updated monthly and offers a short description of every ranked system.

According to Red9, first place belongs to Oracle, followed by MySQL, MongoDB, Cassandra, and Microsoft SQL Server, with PostgreSQL coming in sixth.

Source: Red9

Comparing the Top 5 Databases

Now let’s have a short summary of the most popular database management systems.

1. PostgreSQL

This is an open-source, free object-relational DBMS that has gained a lot of popularity over the last few years. An increasing number of major companies are using this DBMS. It works on all major operating systems, including Windows, macOS, and some versions of Linux and Unix. PostgreSQL has a very active community of users developing plugins and libraries. If you think that this system might be for you, you can find out more in our article on how to practice PostgreSQL.

And if you want to learn PostgreSQL, SQL from A to Z in PostgreSQL is the best choice. This is a set of 9 awesome interactive PostgreSQL courses that will have you working freely with this popular database.

2. MySQL

MySQL has been on top of many rankings for years now. Red9 says that it is the world’s most popular database for the web. It is used by Facebook, Twitter, YouTube, and WordPress and comes with a solid pack of useful features.

MySQL is a really good choice for beginners. The basic version is available as an open-source system and it’s just fine for taking your first steps with SQL.

If you want to learn MySQL, start with the basics as covered in our SQL Basics in MySQL course. If you can already write sophisticated queries and are looking for more challenges, reach for Advanced SQL in MySQL 8.

3. Oracle

This is one of the most popular and stable relational databases for enterprise and web. It works for structured and semi-structured data, supports blockchain tables, facilitates lightning-fast transactions, and helps create online analytical processing and transaction processing in a single database instance. Huge companies like eBay, LinkedIn, Netflix use it for their transactions. However, this DBMS is not open source and or low-cost, which makes it less suited for beginners or small businesses.

4. Microsoft SQL Server

Microsoft’s database is also a paid system used mainly in enterprise applications. MS SQL Server gained popularity over the years and has an advantage over Oracle in price.

To learn more about Microsoft SQL Server and see how it differs from PostgreSQL, read this interesting comparison of the two DBMSs. It should help you understand which solution is better for you.

If you’re interested in working with an MS SQL Server database, start with our SQL Fundamentals in MS SQL Server course.

5. MongoDB

Last but not least is a database system that’s a bit different. MongoDB is not a relational database management system like the ones mentioned above. This is a document store (also called a document-oriented database system) based on the NoSQL model. It stores data inside a collection of documents – not in rows and columns like most other databases.

MongoDB is one of the most popular non-relational databases. It is available as a fully managed Cloud service and for deployment on self-managed infrastructure. It is used by Google, Facebook, Adobe, and other companies.

https://learnsql.com/blog/most-popular-databases-2023/

Artificial Intelligence has grown to have a significant impact on the world. With large amounts of data being generated by different applications and sources, machine learning systems can learn from the test data and perform intelligent tasks.

Artificial Intelligence is the field of computer science that deals with imparting decisive ability and thinking ability to machines. Artificial Intelligence is thus a blend of computer science, data analytics, and pure mathematics.

Machine learning has become an integral part of Artificial Intelligence, and it only deals with the first part, the process of learning from input data. Artificial Intelligence and its benefits have never ceased to amaze us.

The artificial intelligence market is expected to grow by leaps and bounds. The market is expected to grow at the rate of 39.4% by 2022-2028. The artificial intelligence allows the organisations to make better decisions and put better measures to increase the growth.

The artificial intelligence increases the accuracy and decision-making process of the company. It also helps in automation of tasks. Some of the examples of artificial intelligence would be self driving cars, visual assistants, face unlock, etc.

There are various types of artificial intelligence, such as reactive, limited memory, theory of mind, and self aware. The machine performs various AI  algorithms in order to carry out the tasks. Algorithms are subsets of machine learning that automates the process of machine learning. It is these algorithms that tell the machine how to learn on its own.

There are algorithms artificial intelligence grouped into broadly three categories such as supervised learning, unsupervised learning and reinforcement learning.

Join the AI Courses online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.

Types of Artificial Intelligence Algorithms

Artificial intelligence algorithms can be broadly classified as :

1. Classification Algorithms

Classification algorithms are part of supervised learning. These algorithms are used to divide the subjected variable into different classes and then predict the class for a given input. For example, classification algorithms can be used to classify emails as spam or not.  In the classification algorithms artificial intelligence classifies a new category  of observations based on the existing data which we can call as training data as well.  The program learns from the dataset that is already given. Let’s discuss some of the commonly used classification algorithms.

1.   a) Naive Bayes

Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. This comes useful when you need to predict whether the input belongs to a given list of classes or not.

This probabilistic classifier predicts on the basis of probability. The Naive Bayes algorithm that is a probabilistic classifier is used in sentiment analysis, recommendation, spam filtering, etc. It is called as Naive Bayes because it assumes class conditional independence. The attribute value of a given class is independent of the values of other existing attributes.

1.   b) Decision Tree

The decision tree algorithm is more of a flowchart like an algorithm where nodes represent the test on an input attribute and branches represent the outcome of the test.

It is a very simple kind of a probabilistic tree that enables to make decisions about some kind of process. This tool assumes a tree like model and its possible consequences.

1.   c) Random Forest

Random forest works like a group of trees. The input data set is subdivided and fed into different decision trees. The average of outputs from all decision trees is considered. Random forests offer a more accurate classifier as compared to Decision tree algorithm.

Existence of many decision trees is random forest algorithm in classification. In order to build uncorrelated forest trees it uses the features of bagging randomness  while building individual trees. This allows the prediction to bemore accurate as compared to the individual tree.

Random forests is used in many industries such as healthcare, manufacturing, banking, retail, etc. One of the real-life applications of random forest would be to decide if an email is spam or not spam.

1.  d) Support Vector Machines

SVM is an algorithm that classifies data using a hyperplane, making sure that the distance between the hyperplane and support vectors is maximum.

It is a supervised learning algorithm that can be used for either classification and regression problems. One of the example of SVM is Face detection, classification of images, hand writing detection, text and hypertext categorization, etc.

1.   e) K Nearest Neighbors

KNN algorithm uses a bunch of data points segregated into classes to predict the class of a new sample data point. It is called “lazy learning algorithm” as it is relatively short as compared to other algorithms.

Some of the applications of KNN  is finance, medicine, such as bank customer profiling, credit rating, etc. There are various advantages to using KNN such as easy to implement and understand, also it is very simple and intuitive.

2. Regression Algorithms

Regression algorithms are a popular algorithm under supervised machine learning algorithms. Regression algorithms can predict the output values based on input data points fed in the learning system. The main application of regression algorithms includes predicting stock market price, predicting weather, etc. The regression algorithms also aids in predictign the output values based on the input features that are fed from the data. There are various types  of regression such as linear regression, polynomial regression, etc. The most common algorithms under this section are

1.   a) Linear regression

It is used to measure genuine qualities by considering the consistent variables. It is the simplest of all regression algorithms but can be implemented only in cases of linear relationship or a linearly separable problem. The algorithm draws a straight line between data points called the best-fit line or regression line and is used to predict new values.

One of the common examples of linear regression would be medical practice wherein the practioners understand the relationship between the sugar intake and high blood sugar levels.

1.   b) Lasso Regression

Lasso regression algorithm works by obtaining the subset of predictors that minimizes prediction error for a response variable. This is achieved by imposing a constraint on data points and allowing some of them to shrink to zero value.

The lasso regression is used to obtain the subset of predictors that helps in minimisng the error in prediction. Lasso puts a constraint on the model parameters  that make the regression coefficients shrink to zero.

In-demand Machine Learning Skills

1.   c) Logistic Regression

Logistic regression is mainly used for binary classification. This method allows you to analyze a set of variables and predict a categorical outcome. Its primary applications include predicting customer lifetime value, house values, etc.

There are multiple real-life applications of logistic regression such as banking. A credit card company can know if the transaction amount and credit score will lead to fraudulent transaction or not.

1.   d) Multivariate Regression

This algorithm has to be used when there is more than one predictor variable. This algorithm is extensively used in retail sector product recommendation engines, where customers preferred products will depend on multiple factors like brand, quality, price, review etc.

The multivariate regression helps in finding the relationship between multiple variables. Also in finding the correlation between dependent and independent variables.

1.   e) Multiple Regression Algorithm

Multiple Regression Algorithm uses a combination of linear regression and non-linear regression algorithms taking multiple explanatory variables as inputs. The main applications include social science research, insurance claim genuineness, behavioural analysis, etc.

3. Clustering Algorithms

Clustering is the process of segregating and organizing the data points into groups based on similarities within members of the group. This is part of unsupervised learning. The main aim is to group similar items. For example, it can arrange all transactions of fraudulent nature together based on some properties in the transaction. There are various advantages to using clustering algorithms. As they are simpler to implement, adaptable to new examples, scale to large data sets and more. Some of the example of clustering algorithms would be identifying fake news, marketing,  spam filter, etc. Below are the most common clustering algorithms.

1.   a) K-Means Clustering

It is the simplest unsupervised learning algorithm. The algorithm gathers similar data points together and then binds them together into a cluster. The clustering is done by calculating the centroid of the group of data points and then evaluating the distance of each data point from the centroid of the cluster. Based on the distance, the analyzed data point is then assigned to the closest cluster. ‘K’ in K-means stands for the number of clusters the data points are being grouped into.

There are various applications to K- means clustering from banking to cybersecurity, search engines, etc. It is an unsupervised learning because the points have no external classification to them.

K-means has various real-life applications such as sentiment analysis, spam detection, etc. It is used where the user has the unlabeled data. Unlabeled data is that type of data which does not have a category or groups.

1.   b) Fuzzy C-means Algorithm

FCM algorithm works on probability. Each data point is considered to have a probability of belonging to another cluster. Data points don’t have an absolute membership over a particular cluster, and this is why the algorithm is called fuzzy.

Fuzzy C- Means is a clustering technique wherein the data set gets grouped into N clusters where each data point in the dataset belongs to every cluster in one way or the other.

1.   c) Expectation-Maximisation (EM) Algorithm

It is based on Gaussian distribution we learned in statistics. Data is pictured into a Gaussian distribution model to solve the problem. After assigning a probability, a point sample is calculated based on expectation and maximization equations.

The Expectation-Maximisation (EM) algorithm is used in those places where there is a need to find a local maximum likelihood parameters of a statistical model. It is also used in the places wherein the equations cannot be solved directly.

1.   d) Hierarchical Clustering Algorithm

These algorithms sort clusters hierarchical order after learning the data points and making similarity observations. It can be of two types

• Divisive clustering, for a top-down approach
• Agglomerative clustering, for a bottom-up approach

https://www.upgrad.com/blog/types-of-artificial-intelligence-algorithms/

Loops and Conditionals

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Source https://www.cs.cmu.edu/~15110-n15/lectures/unit03-Algorithm-1.pdf