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Masa Darurat Sampah di Kota Bandung Disebut akan Diperpanjang

Masa berlaku darurat sampah di Kota Bandung, Jawa Barat, akan berakhir pada 25 Oktober 2023. Namun, kemungkinan joker123 masa darurat itu akan diperpanjang.

Darurat sampah itu diberlakukan imbas kebakaran di area Tempat Pembuangan Akhir (TPA) Sarimukti, Kabupaten Bandung Barat, yang dilaporkan terjadi sejak 19 Agustus 2023. Terjadinya kebakaran membuat pengangkutan sampah ke TPA terhambat dari sejumlah daerah di Bandung Raya.

Sekretaris Daerah (Sekda) Kota Bandung Ema Sumarna mengatakan, Pemerintah Kota (Pemkot) Bandung berkoordinasi dan menunggu keputusan dari Pemerintah Provinsi Jawa Barat (Pemprov Jabar) ihwal masa darurat sampah itu.

“Darurat sampah itu dipastikan ada pertambahan waktu, sambil kita menunggu nanti kebijakan dari pemerintah provinsi. Kalau melihat kondisi lapangan, memang berbagai faktor mendukung untuk dilakukannya perpanjangan,” kata Ema, selepas meninjau pengolahan sampah di Kelurahan Cisurupan, Kecamatan Cibiru, Kota Bandung, Selasa (24/10/2023).

Menurut Ema, mengacu ketentuan Peraturan Daerah (Perda) Kota Bandung Nomor 9 Tahun 2018 tentang Pengolahan Sampah, kepala daerah juga mempunyai kewenangan untuk menentukan status kedaruratan terkait sampah.

“Apabila dari provinsi misalnya ada keterlambatan waktu dalam menentukan status, Pemkot Bandung sebetulnya, dalam artian ini Pak Pj Wali Kota, itu punya otoritas untuk mengambil kebijakan bahwa di Bandung masih darurat tanggap penanganan sampah, dan faktornya kan sangat logis kalau kebijakan itu diambil,” kata Ema.

Pada masa darurat ini, Pemkot Bandung menekankan berbagai upaya untuk pemilahan dan pengolahan sampah. Dengan begitu, diharapkan dapat mengurangi beban tempat penampungan sementara (TPS) maupun TPA. Bahkan, pemkot menyampaikan TPS hanya ditujukan untuk menampung sampah residu.

Dalam Rapat Pleno Satgas Penanggulangan Sampah yang digelar di Balai Kota, Senin (23/10/2023), sebagaimana dilansir Pemkot Bandung, Ema sempat menyampaikan capaian pengolahan sampah. Per 22 Oktober 2023, dilaporkan total sampah organik yang bisa diolah sekitar 5,98 ton per hari dan sampah anorganik yang diolah sekitar 5,07 ton per hari. Adapun sampah residu 0,69 ton per hari. “Meski belum signifikan, namun kami melihat ada progres,” kata Ema.

Pemkot Bandung juga menerapkan pola klaster dalam pengelolaan sampah ini. Seperti klaster kantor pemerintahan, nonpemerintahan, klaster pusat perbelanjaan, perhotelan, lembaga pendidikan, juga klaster instansi pelayanan kesehatan.

Menurut Kepala Dinas Lingkungan Hidup (DLH) Kota Bandung Dudy Prayudi, saat ini masih banyak TPS yang sampahnya melebihi kapasitas (overload). Ia mengatakan, pengangkutan sampah ke TPA pun masih terbatas dengan kuota yang diberikan Pemprov Jabar. “Itu salah satu yang menjadi pertimbangan kita perlu adanya perpanjangan darurat sampah,” kata dia, Selasa (24/10/2023).

Berdasarkan hasil rapat, kemarin, Dudy mengatakan, masa darurat sampah di Kota Bandung akan diperpanjang. Namun, Pemkot Bandung akan berkoordinasi lebih lanjut dengan Pemprov Jabar. “Untuk tanggal pastinya kita koordinasi lagi dengan pemprov, tapi yang pasti ada perpanjangan,” kata Dudy.

Embarking on the journey of starting a family is filled with anticipation and excitement. However, the process of conceiving a child is often more complex than anticipated. There’s a lot more to conceiving than people are generally aware of. Once you have made the big decision to have a baby, it’s natural to start wondering how to get pregnant fast. While getting pregnant may be easy for some couples, the process can be difficult for others, and that’s totally normal.

There is no magic pill or special formula that can ensure a quick conception, however there are a few steps you can take to prepare your body for a healthy pregnancy. In this article, we will explore expert tips that can help you on your journey toward parenthood. From adopting a healthy lifestyle to understanding your menstrual cycle, we’ll cover various aspects that play a crucial role in maximizing your chances of conceiving.

Best Tips to Conceive Faster

• Track your menstrual cycle:

Tracking your menstrual cycle can be important for a number of reasons when you are planning to conceive. First, it helps in predicting your fertile window. Second, it gives a good idea of your health, alerting you to any changes from the norm.

• Find your fertile window:

The fertile window is the term used to describe the time segment in which you are most likely to get pregnant. Knowing when you’re going to ovulate and, therefore, when you’re most fertile is a crucial step for increasing your chances of getting pregnant.

• Timing of intercourse:

You can only conceive during the 5 to 6 days surrounding ovulation. Make sure you are having sex during that time. There is a greater chance of conceiving if you have sex a few days before ovulation. This is because the sperm will be in your body right when the egg is released.

• Maintain normal weight:

Both being underweight and overweight can have adverse effects on fertility and increase the time it takes to conceive. Reach a healthy weight before you begin trying to conceive. You can talk to your doctor about healthy strategies for losing weight.

• Eat a nutritious, balanced diet:

A healthy lifestyle is crucial for optimizing fertility. Eating a healthy diet with a balance of whole grains, fruits, vegetables, fish, poultry, and dairy products is beneficial when trying to conceive.

• Establish healthy exercise habits:

Engaging in prenatal workouts can contribute to a healthier pregnancy, shorter labor duration, and smoother delivery process. Aim for a moderate exercise routine of 150 minutes per week. Walking, swimming, yoga, and strength training are a few ideal exercises.

• Start taking prenatal vitamins:

Start taking prenatal vitamins if you are trying to get pregnant. Among other important nutrients, folic acid is essential for the development of a baby’s brain and spine. Not only that, it helps promote ovulation, encourages fertilization, and supports early embryo survival

• Discontinue birth control:

Stop taking birth control pills a few months before you start trying to conceive. Some forms of birth control, like intrauterine devices (IUDs) or contraceptive implants https://radarkediri.net/, physically prevent fertilization or implantation. Removing these devices is necessary to restore fertility.

• Make time to relax:

Find calming activities that bring you joy and help you stay grounded. Consider incorporating gentle yoga, meditation, or even talk therapy into your routine. Prioritizing your mental health is an important form of self-care. If you are struggling, don’t hesitate to reach out for help.

• Get diagnosed for medical problems:

Getting and staying healthy also means managing any existing health condition. Diabetes, high blood pressure, PCOS, and thyroid conditions can make it harder for you to get pregnant. Schedule a preconception checkup with your doctor to ensure that you do not have any underlying medical issues.

What to avoid?

• Avoid smoking, alcohol, and drugs:

If you smoke, drink alcohol, or use drugs, it is recommended that you quit as soon as possible. Smoking and consuming alcohol can affect ovulation, hormone levels, and can reduce your chances of getting pregnant. Minimizing or eliminating these substances from your lifestyle will not only improve your chances of conceiving but also contribute to a healthier pregnancy and baby.

• Avoid processed foods and foods that are high in fats and sugar:

Certain processed foods, such as those containing artificial additives, preservatives, and trans fats, can disrupt hormonal balance in the body. Hormonal imbalances can interfere with ovulation and the menstrual cycle, making it more challenging to conceive.

• Limit caffeine:

You should also cut down on caffeine when trying to get pregnant. Women who daily consume more than 2 cups of coffee or 2 liters of soda may have a harder time getting pregnant and a greater chance of miscarriage.

• Don’t overdo strenuous exercise:

Engaging in rigorous and intense exercise for over five hours per week has been associated with decreased ovulation.

When to Talk to a Doctor

Infertility affects both men and women. How long it may take to conceive depends on many factors – age, health history, unique menstrual cycle, and lifestyle factors. Depending on the problem, your gynecologist might be able to help. In some cases, a fertility specialist offers the best hope.

• If you are under 35 and healthy, chances are good that you will conceive within 3 to 6 months. But, if you don’t conceive within 12 months of trying, you may have fertility issues and may need treatment.

• Fertility starts to decline more sharply after age 35. If you are 35 or older and haven’t conceived after six months of unprotected sex, ensure to visit an fertility clinic near you.

By implementing these expert tips, you can increase your chances of conceiving faster. Remember that every individual is unique, and it may take time for some couples to achieve pregnancy. If concerns persist, consult with the best infertility specialist in Gurgaon, who can provide personalized guidance and support throughout your journey to parenthood.

Artificial intelligence is a fascinating and growing area in the field of data science. Although we’re far from having the robot servants depicted in science fiction films, AI is already a part of our everyday lives and we would all do well to learn artificial intelligence (AI). While some artificial intelligence (AI) applications, like autonomous cars, are still in the developmental stages, other uses, like predictive analysis, are already here. Learning AI and how to use AI tools can open up a world of possibilities in fields like data analysis.

AI is a versatile field with applications in all industries, which means that AI-related jobs are in high demand. A McKinsey survey found that AI is increasingly being used for service operation optimization, product enhancement, data analysis, data visualization, data manipulation, risk modeling, and fraud prevention. Between now and 2030, the demand for computer and information research jobs is expected to grow by 22%. While artificial intelligence and clever AI tools can’t replace human intelligence, this fascinating branch of computer science can help us do so much more. And if you want a career that is always in demand, you need to learn artificial intelligence techniques and how to use machine learning algorithms to your advantage!

What Is Artificial Intelligence?

Artificial intelligence refers to the part of the data science industry and involves the building of computer programs that can mimic tasks associated with human intelligence. Artificial intelligence solves problems by using computer programming and large data sets. Learning AI or artificial intellgence includes machine learning, deep learning, and natural language processing, which allow computers to “learn” from experience and perform human-like tasks like data visualization or data manipulation, often much more efficiently than humans can.

This type of artificial intelligence is called narrow or weak artifical Intelligence. In these cases, a computer accomplishes a specific task by recognizing patterns in large data sets. Some examples of narrow artificial intelligence include recommendations from your streaming platform, chess bots, and smart speakers.

While narrow artificial intelligence can adapt to inputs, it can’t perform outside of its given parameters. Still, it has its uses. The Fourth Industrial Revolution and the digital-first approach of modern businesses generate enormous amounts of data that can fuel narrow artificial intelligence applications.

Strong AI, also called artificial general intelligence (AGI), is the kind of artificial intelligence associated with robots in science fiction plots – the ones that surpass or mimic human intelligence. This type of artificial intelligence isn’t going to happen soon, although developers are working to overcome the challenges associated with AGI, such as prediction and control models.

AI Terms To Learn

Learning AI? Start your AI learning journey with this glossary of terms:

• Deep learning: Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. These networks are inspired by the structure of the human brain and are able to learn complex patterns from data. Deep learning has been used to achieve state-of-the-art results in a wide range of tasks, including image recognition, speech recognition, and natural language processing.
• Data science: Data science is a field that deals with the collection, analysis, and interpretation of data. Data scientists use a variety of tools and techniques to extract meaningful insights from data. These insights can then be used to inform decision-making in a variety of fields, such as business, finance, and healthcare.
• Data scientist: A data scientist is a professional who collects, analyzes, and interprets data to extract meaningful insights and knowledge. They use their skills to solve real-world problems in various industries.
• AI algorithms: AI algorithms are the building blocks of artificial intelligence. They are the mathematical procedures that allow machines to learn and make decisions.
• Data processing: Data processing is the process of cleaning, transforming, and preparing data for analysis. It is a critical step in the data science process.
• Computer systems: Computer systems are the hardware and software that make up computers. They are essential for storing, processing, and transmitting data.
• Mathematical concepts: Mathematical concepts are the building blocks of mathematics. They are used to solve problems and make predictions.
• Software engineering: Software engineering is the field of designing, developing, and maintaining software applications.
• Deep learning algorithms: Deep learning algorithms are a type of machine learning algorithm that is inspired by the structure of the human brain. They are able to learn complex patterns from data.
• Weak AI: Weak AI, also known as narrow AI, is a type of AI that is designed to perform a specific task. Examples of weak AI include chatbots, facial recognition software, and self-driving cars.
• Reinforcement learning: Reinforcement learning is a type of machine learning that allows machines to learn through trial and error. It is often used to train robots and other autonomous agents.
• Machine learning fundamentals: Machine learning fundamentals are the basic concepts of machine learning. They include supervised learning, unsupervised learning, and reinforcement learning.
• Artificial general intelligence (AGI): Artificial general intelligence (AGI) is a type of AI that is capable of performing any intellectual task that a human can. AGI is still a theoretical concept and does not yet exist.
• Linear and logistical regression: Linear and logistical regression are two statistical techniques that are used to predict continuous and categorical values, respectively.
• Linear algebra: Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transformations. It is used in a wide range of applications, including machine learning, computer graphics, and physics.
• Learning path: A learning path is a sequence of steps that a person takes to learn a new skill or knowledge.
• Machine learning engineer: A machine learning engineer is a professional who designs, develops, and deploys machine learning models.
• Narrow AI: Narrow AI, also known as weak AI, is a type of AI that is designed to perform a specific task. Examples of narrow AI include chatbots, facial recognition software, and self-driving cars.
• Logistic regression: Logistic regression is a statistical technique that is used to predict binary outcomes.
• AI models: AI models are mathematical representations of the world that are used to make predictions or decisions.

Why Learn Artificial Intelligence?

Artificial intelligence is an exciting field at the forefront of finding solutions to society’s most pressing problems, including disease, pollution, and climate change. You should learn AI if you are passionate about those fields and what AI tools can do to solve those issues. It’s also a rapidly growing sector of the economy, with artificial intelligence software revenue expected to increase 21.3% from the previous year, for a total of $62.5 billion in 2022. By learning artificial intelligence and mastering machine learning algorithms, you’ll be prepared for a challenging and rewarding career that pays well too. The average base salary for an AI engineer is over $119,000.

How Do You Know If an AI Course Is Right For You?

With so many online courses available, which one is right for you? Here are a few ways you can evaluate your options and find an online AI course that works for you:

• If you are starting from scratch, look for online courses that teach basic mathematics and coding skills. More advanced AI professionals that want to progress their AI career should look for online courses that cover advanced programming skills, deep theoretical knowledge, and insights into new AI algorithms.
• Look for online courses that are up to date. Thanks to the dynamic nature of artificial intelligence (AI), learning AI isn’t a once-off. When choosing between online courses, make sure they cover new AI algorithms, new research and new AI technologies.
• If you are at the very beginning of your AI learning journey, opt for online courses that cover the basics of AI, including data structures, linear and logistic regression, Ai models, and more.
• Online courses should prepare you for your dream job. Look for online courses that offer practical projects, career services, and resources for continuous learning.
• Consider the prerequisites. Do you need a bachelor’s degree in Computer Science or is it suitable for beginners? There are online courses for AI specialists and complete novices; pick one that fits your skill level.
• If you want to specialize, look for online courses that enable you to do so. Do you want to be a generalist or an AI researcher? An NLP engineer? A data scientist? Your course should be tailored accordingly.

Bear in mind that you can learn AI in a number of ways. Many AI professionals entered the field through self-study – and self-study alone. If you have a comprehensive learning plan in place, and a solid understanding of the theoretical knowledge required (machine learning and deep learning, modern AI techniques, how to manipulate data, how to build AI models, etc), you already have a solid foundation for a lucrative AI career.

What Does an Artificial Intelligence Engineer Do?

The roles and responsibilities for an AI engineer will vary based on their industry, but generally speaking, artificial intelligence engineers develop artificial intelligence systems and applications to make better decisions, improve performance, and increase efficiency. They may develop AI algorithms or use existing machine learning algorithms to process and manipulate existing data structures. When you learn artificial intelligence, it opens up a world of possibilities in the field of computer and data science. Artificial intelligence engineering is a complex job that requires you to:

• Achieve objectives using artificial intelligence methods
• Solve problems with logic, probability analysis, and machine learning
• Monitor and steer development projects by analyzing systems
• Understand and apply best practices in speech recognition, data processing, data mining, and robotics

You’ll find that isn’t easy to learn AI and you may need to complete a degree in Computer Science or a related field first.

8 Steps To Effectively Learn Artificial Intelligence

One of the biggest hurdles to learning artificial intelligence https://www.tangbuje.com/ is not knowing where to start. It’s a broad field that consists of many components. Many of the concepts involved in AI rely on advanced math and formal logic, which can be an obstacle to joining the industry. To help you overcome these hurdles, we’ve broken down the field of AI into a manageable step-by-step guide to mastery.

Understand the Prerequisites

Before you start learning AI, you should have a solid foundation in the following areas.

Computer Science Fundamentals

You’ll need to understand the fundamental principles of computer science before you can start programming AI. This includes:

• Theory and algorithms such as Boolean algebra, binary mathematics, and theory of computation
• Computer hardware systems, including the physical components of computers, digital logic, computer architecture, and network architecture
• Software systems and elements such as programming languages, compilers, computer graphics, and operating systems

Look for artificial intelligence courses or data science bootcamps that can give you a solid introduction to these concepts.

Probability and Statistics

Learning AI involves learning about probability. Probability is one of the core principles used in AI, as it allows you to teach the computer to “reason” in the face of uncertainty. Machines learn through data, which they understand through statistics. Probability and statistics can answer questions such as:

• What is the most common outcome?
• What is the expected outcome?
• What does the data look like?

Probability and statistics for AI should include some of the following topics:

• Numerical and graphical description of data
• Elements of probability
• Sampling distributions
• Probability distribution functions
• Estimation of population parameters
• Hypothesis tests

You can learn artificial intelligence concepts like this in a data science course, or take specialized artificial intelligence courses that are more immersive.

Mathematics

In addition to probability and statistics, you’ll need to know some math fundamentals to master AI, including:

• Linear algebra, which is essential to understanding approaches to AI and machine learning
• Basic differential and some multivariable calculus, which deals with changes in parameters, functions, errors, and approximations
• Coordinate and nonlinear transformations, which are key ideas in AI
• Linear and higher-order regressions to make predictions based on data sets
• Logistic regression to classify data
• Numerical analysis to turn math formulae into effective code

Programming

You’ll also want to be familiar with the programming languages suitable for developing AI applications. Some of the most useful include:

• Python, which is easy to learn and has widely available resources and support
• Java, which is user-friendly and platform-independent
• R, which was created to handle large data sets
• Prolog, which was used to create the first-ever chatbot therapist, Eliza, in 1966
• Lisp, which is the second-oldest programming language, predated only by Fortran
• SQL, which is used to manage databases

Data Structures

To learn, AI requires input in the form of data. Data structures are different methods of organizing data to be used effectively. If you want to launch a career in AI, you’ll need to understand how to use and apply the most suitable data structure for your program. Some of the most common types of data structures are:

• Array
• Linked list
• Stack
• Queue
• Binary tree
• Binary search tree
• Heap
• Hashing
• Graph
• Matrix

Algorithms

An algorithm gives step-by-step methods for performing a calculation. To facilitate machine learning, you’ll have to design algorithms that allow a computer to learn on its own. Algorithms can use data mining and pattern recognition to make recommendations. This is how apps recommend shows for you to watch and how Facebook decides what shows up in your feed.

Algorithms are also used for more consequential purposes, such as approving home loans and deciding jail sentences. Algorithms are powerful tools, but they’re not as objective as they sometimes seem, and massive amounts of data can lead to some spurious correlations. So algorithms have to be tempered with the good judgment of human minds.

Ace AI Theory

Once you’ve learned the prerequisites, you’re ready to dive into AI theory. Regardless of whether you learn AI through an in-person class, with a self-paced online course, or in piecemeal fashion with YouTube videos, you’ll need to cover the same basic theoretical concepts. Here are some of the most important tenets that you’ll need to learn:

Problem-Solving

The purpose of AI is to solve a problem, which involves a number of techniques, including algorithms and heuristics. An AI system includes an agent and its environment. In AI, an agent is the program that makes decisions. A problem-solving agent in AI is focused on achieving its goal. Once the goal is formulated, a process for solving the problem is created through problem formulation. This involves several components, including:

• The initial state of the agent
• The possible actions the agent can take
• A transaction model that describes each action
• A goal test to determine if the goal has been achieved
• The cost of each action path

Reasoning

Reasoning is the process of drawing conclusions or making predictions based on your existing knowledge. Because machines aren’t capable of thinking, they have to be programmed to do this kind of reasoning with algorithms. When you’re programming AI to reach conclusions, you’ll need to teach it how to complete a task based on one of several reasoning methods, such as the following.

Deductive reasoning. This type of reasoning uses existing data to determine if the premise of an argument is valid. It’s a kind of reasoning that applies general principles to a specific case. If you’ve ever taken an introductory logic course, you probably remember the basic deductive reasoning example: If all men are mortal and Socrates is a man, then Socrates is mortal.

Inductive reasoning. Unlike deductive reasoning, inductive reasoning produces a general conclusion from specific observations. In inductive reasoning, a conclusion can be false even if all of the observations are true. For example, you might notice that all of the dogs in your neighborhood are brown and reach the erroneous conclusion that all dogs are brown. In AI, supervised learning uses inductive reasoning to generalize from specific data. The more comprehensive a database is, the better its generalizations will be.

Abductive reasoning. Abductive reasoning is the process of drawing a conclusion that most likely fits the observations. This type of reasoning is used by doctors to make medical diagnoses. Abductive reasoning is similar to deductive reasoning, but the premise doesn’t guarantee the conclusion. In AI, this type of reasoning could be used by a diagnostic assistant program to suggest a diagnosis based on the symptoms a patient exhibits.

Common-sense reasoning. Common-sense reasoning is an informal type of reasoning that relies on experience. Using good judgment, rather than formal rules, it is implemented with heuristic knowledge and heuristic rules, which are common-sense rules intended to increase the likelihood of solving a problem. Common-sense reasoning is most widely used in the AI field of natural language processing to help computers communicate more effectively with humans.

Monotonic reasoning. In monotonic reasoning, once a conclusion is reached, it will never change, even if additional facts are added. Any theorem that proves an example is using monotonic reasoning. For example, “The earth revolves around the sun.”

In AI, monotonic reasoning can be used for applications such as content filtering. A website that contains any amount of inappropriate content will be filtered out, and that decision will never change, even if the website has plenty of appropriate content.

Non-monotonic reasoning. In non-monotonic reasoning, the conclusion may be invalidated if new information is added. Incomplete and uncertain models use non-monotonic reasoning. This type of reasoning is useful in AI applications such as robotic navigation systems.

Data Manipulation

AI relies on data sets to learn and to make predictions, so you’ll need to be skilled at structuring data into a useful format. You will need to create programs that identify connections among data sets. SQL is the programming language used to manage databases, and R is frequently used in data science applications.

Natural Language Understanding

Natural language understanding is a subset of NL processing that uses programming to understand human speech. It allows computers to understand human speech without the formal syntax of computer languages. Natural language understanding also allows computers to communicate back to humans in their own language.

Natural language understanding uses algorithms to analyze human speech and format it as a structured data model based on sentiment, named entities, and numeric entities. Voice-enabled assistants and chatbots both use natural language processing.

Computer Vision

Computer vision is the process of training computers to observe and understand visual input. It allows computers to extract information from images, videos, and other visual inputs. The program can then use that information to take action or make recommendations. Computers can analyze visual information much faster than humans, analyzing thousands of images per minute.

As with other AI training methods, computer vision requires large data sets to notice small differences and recognize particular images. Algorithmic models in machine learning enable the computer to teach itself about visual data.

Automated Programming

Automated programming is a type of computer program that generates the code for another program based on a set of specifications. One example of this is DeepMind’s AlphaCode, which writes computer programs well enough to rank in the 54th percentile of human programmers when tested in coding challenges.

AlphaCode was given a set of challenges used in coding competitions such as transforming a random string of letters into another random string of the same letters using limited inputs. AlphaCode approached this challenge by generating a huge number of possible answers. It then ran the code, tested the output, and tested the answer to select the best option.

AlphaCode isn’t the only example of automated programming: Microsoft and OpenAI have GPT-3, which automatically completes strings of code. Automated programming is still limited in scope, but it could eventually make programming more accessible to non-programmers.

Master Data Processing

Data processing is such a significant aspect of AI that it’s a field unto itself. Big data permeates all aspects of modern life. Almost all businesses incorporate data-driven decision-making into their strategies. This is possible through machine learning, which relies on processing massive data sets. If you’re interested in the big data element of AI, you might enjoy one of the following careers.

Data Preprocessing

Data preprocessing involves transforming raw data into an understandable format and ensuring its quality. The quality of data depends on its:

• Accuracy
• Consistency
• Completeness
• Timeliness
• Trustworthiness

During data preprocessing, data is cleaned to remove inaccurate, incomplete, or unnecessary data. Multiple sources of data are also combined into one data set during this phase. Finally, data is reduced and transformed so that it’s ready to use.

Machine Learning Engineering

A machine learning engineer builds AI systems that automate predictive models based on machine learning. Their systems use huge data sets to generate and develop algorithms that learn from results and refine the process of performing future operations for more accurate results.

What Is Machine Learning?

Machine learning is a branch of AI in which computers are taught to learn and improve on their processes with minimal human intervention. Machine learning programs can even detect more complex and subtle patterns than humans can. This happens through the use of data sets and pattern recognition. There are two main types of machine learning: supervised learning and unsupervised learning.

In supervised learning, you can collect or produce data from a previous output of machine learning. You give the computer a training set of labeled data points.

In unsupervised learning, the algorithm tries to discern the inherent structure of the data without a training set. This can help you find many unknown patterns in your data.

How To Learn Machine Learning

Machine learning is a specialized field of AI, so you’ll still need to understand the prerequisites and general AI theory. In addition, here are some steps that you can take to specialize in machine learning:

• Learn Python
• Learn data science tools such as Jupyter and Anaconda
• Learn data analysis tools like Pandas, NumPy, and Matplotlib
• Use the Python library SciKit-Learn to find patterns in your data
• Learn to build deep learning neural networks
• Work on your own projects

How Is Machine Learning Related to AI?

Machine learning is a branch of AI. Machine learning is one way to implement AI principles, giving computers the ability to learn on their own without being explicitly programmed.

Data Scientist

Data science is closely related to machine learning engineering, but they aren’t the same. Data science is a broad field aimed at extracting insights from data. Machine learning is one tool data scientists use.

As far as education goes, data scientists often have advanced degrees in a variety of subject areas, while machine learning engineers usually come from the field of software engineering.

What Is Data Science?

Data science is the process of using scientific methods, processes, algorithms, and systems to extract meaning and insights from unstructured data.

What Does a Data Scientist Do?

Data scientists use machine learning to develop solutions for business problems. Unlike machine learning engineers, data scientists often use existing machine learning tools to process data, although they may have to develop novel applications if needed. After identifying business problems that can be solved with machine learning, a data scientist will then develop custom algorithms and models to solve those problems.

How To Learn Data Science

As with machine learning, mastering the prerequisites and fundamentals of AI is necessary for learning data science. Because it’s a branch of AI, many of the same principles apply. Once you’ve mastered the basics, you can continue your learning journey by:

• Mastering data cleaning, which will be a huge part of your job
• Using existing data sets to work on your own projects
• Gain experience and contribute to valuable work through data science volunteering

How Is Data Science Related to AI? 

Data scientists use AI to do their jobs, so there’s a lot of overlap between data science, machine learning, and artificial intelligence. The biggest difference among the three is that data science uses AI and machine learning to produce insights. Data science relies on humans to gain insights and make conclusions from the results produced by machine learning.

Data Engineer

It can be difficult to distinguish between a data scientist and a data engineer, particularly if you look at job postings. Data scientists are often expected to also fill the role of a data engineer. However, these two roles are distinct.

What Is Data Engineering?

Data engineering is the process of designing and building pipelines for transforming data into a usable format. These pipelines take data from different sources and combine them into a single source for further analysis.

What Does a Data Engineer Do?

Data engineers build and maintain data infrastructure that serves as the foundation for all other data functions. They use databases, servers, and large-scale processing systems to transform unstructured data into usable formats. They do this through a process called ETL (extract, transform, load) using tools such as SQL, Cassandra, and BigTable.

How To Learn Data Engineering

Data engineers need to be well-versed in the following skills:

• Data warehousing
• ETL tools such as Xplenty or Hevo
• Machine learning
• Database systems such as SQL
• Programming languages such as Python and Julia
• Algorithms and data structures
• Distributed systems

How Is Data Engineering Related to AI?

This provides the raw materials for data-related AI tasks. Machine learning and AI require such massive amounts of data that it wouldn’t be possible to scale them without data engineering. The exponential growth in data that’s created on a daily basis feeds AI, but the majority of it is unstructured. Data engineering transforms unstructured data into usable formats for AI developers.

Work on AI Project

The best way to develop an understanding of AI algorithms is to build them from scratch. Start with projects that require simple algorithms and then take on harder projects, gradually increasing the skill level required. When you’re trying to master AI, theory alone isn’t enough. A practical, hands-on approach will cement your learning and boost your skills.

How To Choose Projects

There are several ways to choose AI projects. Because AI is applicable to every industry, the options can seem overwhelming. Start by choosing projects based on your interests, fundamental projects, and projects that add value to your community.

Choose a Project Based on Your Interests

Pick a project that combines learning AI with your other hobbies and interests. If you’re an avid gamer, design a game you can play against. Chess is a classic option.

Work on Fundamental Projects

There are some traditional projects that are routinely recommended for beginners. These projects are fun and teach some foundational skills. Although there is controversy over what’s considered foundational in AI, there’s no doubt that learning to train one model on a huge amount of data and then adapting it to different applications is a fundamental skill in AI. Even if you eventually decide this model isn’t foundational, it still has many practical uses.

One common fundamental project recommended for beginners is using Enron’s email database to analyze social networks or detect anomalies. The Enron debacle in 2001 was one of the most massive fraud scandals in recent history. The investigation resulted in a database of more than half a million emails that are publicly archived.

Build Professionally and Personally Valuable Projects

One way to make your portfolio stand out is to include projects that are important to you personally and add value to the community. Choose an issue that is significant to you and design an algorithm to address a problem related to it, like using social media posts to predict depression.

Ideas To Get You Started

If you can’t think of any projects or you’re just looking for inspiration, here are some ideas:

• Fake news detection
• Translation
• Stock price prediction
• Facial recognition
• Human activity recognition
• Sales price forecasting

Learn and Work With AI Tools

There are many AI tools you can choose from, but these are some of the most popular frameworks and tools currently in use.

SciKit-Learn

SciKit-Learn is one of the most popular tools in ML libraries. It’s used with unsupervised and administered calculations. SciKit is a great tool to use for fledglings.

TensorFlow

TensorFlow can be used for a variety of machine learning tasks, but it’s especially useful for the training and inference of deep neural networks.

PyTorch

PyTorch was developed by Facebook. It’s used primarily for applications such as natural language processing and computer vision.

Opt for AI Courses

Although you could take a piecemeal approach to learning AI, choosing a formal course will accelerate the process and provide some structure. A class can provide accountability, feedback, and resources if you run into questions or problems.

Depending on how you learn best, you can choose a self-paced MOOC, a formal graduate degree program, or a bootcamp.

Apply for an Internship

Once you’ve finished your classes and built a portfolio, applying for an internship is a great way to get some real-world experience to make your job search easier. To increase your chances of landing an internship, you can:

• Tell people in your professional and personal networks that you’re looking for an internship
• Attend local meetups and AI hackathons
• Keep your professional networking accounts updated
• Take advantage of career resources from your coursework
• Prepare for your technical interview

Get a Job

Your internship should provide experience and professional connections that will help you land a job. When you finish your internship, reach out to the contacts you’ve developed to let them know you’re looking for a permanent position.

The most valuable aspect of an AI internship is the opportunity to solve real-world problems. Be sure to highlight the AI projects you worked on during your internship when you’re discussing your portfolio with prospective employers, including the specific contributions you made.

Can You Learn AI on Your Own?

You can learn AI on your own, although it’s more complicated than learning a programming language like Python. There are many resources for teaching yourself AI, including YouTube videos, blogs, and free online courses.

Because AI includes advanced mathematical concepts such as linear functions, linear algebra, probability, statistics, and logic, it may be easier to learn as part of an organized course. However, as long as you have a comprehensive learning plan and are dedicated, you can learn by yourself.

Your body depends on water to survive. Every cell, tissue, and organ in your body needs water to work properly. For example, your body uses water to maintain its temperature, remove waste, and lubricate your joints. Good hydration is important for overall good health.

Making sure you get enough water every day is an important step in maintaining your health.

Path to improved health

Most people have been told they should drink 6 to 8, 8-ounce glasses of water each day. That’s a reasonable goal. However, different people need different amounts of water to stay hydrated. Most healthy people can stay well hydrated by drinking water and other fluids whenever they feel thirsty. For some people, fewer than 8 glasses may be enough. Other people may need more.

While plain water is best for staying hydrated, other drinks and foods can help, too. Water can be found in fruits and vegetables (for example, watermelon, tomatoes, and lettuce), and in soup broths. Fruit and vegetable juices, milk, and herbal teas add to the amount of water you get each day. Just make sure to limit sugary drinks that are high in calories.

Hydration and caffeine

Even some caffeinated drinks (for example, coffee, tea, and soda) can contribute a little to your daily water intake. A moderate amount of caffeine (400 milligrams) isn’t harmful for most people. Here are the caffeine amounts found in popular drinks:

• 12 ounces of soda: 30 to 50 milligrams
• 8 ounces of green or black tea: 30 to 50 milligrams
• 8 ounces black coffee: 80 to 100 milligrams
• 8-ounce energy drink: 45 to 80 milligrams

However, it’s best to limit caffeinated drinks. Caffeine will cause you to urinate more frequently. This can make it difficult to stay hydrated. It can also make you feel anxious or jittery.

Sports drinks can be helpful if you’re planning on exercising at higher-than-normal levels for more than an hour. They contain carbohydrates and electrolytes that can increase your energy. They help your body absorb water. However, some sports drinks are high in calories from added sugar. They also may contain high levels of sodium (salt). Check the serving size on the label. One bottle usually contains more than one serving. Some sports drinks contain caffeine, too. Remember that a safe amount of caffeine to consume each day is no more than 400 milligrams.

Energy drinks are not the same as sports drinks. Energy drinks usually contain large amounts of caffeine. Also, they contain ingredients that overstimulate you (guarana, ginseng, or taurine). These are things your body doesn’t need. Most of these drinks are also high in added sugar. According to doctors, children and teens should not have energy drinks. Because these drinks have high amounts of caffeine, they do not offer good hydration.

If staying hydrated is difficult for you, here are some tips that can help:

• Keep a bottle of water with you during the day. To reduce your costs, carry a reusable water bottle and fill it with tap water.
• If you don’t like the taste of plain water, try adding a slice of lemon or lime to your drink.
• Drink water before, during, and after a workout.
• When you’re feeling hungry, drink water. Thirst is often confused with hunger. True hunger will not be satisfied by drinking water. Drinking water may also contribute to a healthy weight-loss plan. Some research suggests that drinking water can help you feel full.
• If you have trouble remembering to drink water, drink on a schedule. For example, drink water when you wake up, at breakfast, lunch, and dinner, and when you go to bed. Or drink a small glass of water at the beginning of each hour.
• Drink water when you go to a restaurant. It will keep you hydrated, and it’s free.

Things to consider

If you don’t drink enough water, you may become dehydrated. This means your body doesn’t have enough fluid to operate properly. https://tropicalnailsandspa.com/

Your urine can be an indicator if you’re dehydrated. If it’s colorless or light yellow, you’re well hydrated. If your urine is a dark yellow or amber color, you may be dehydrated.

There are other signs that can signal you may be dehydrated. They include:

• Little or no urine
• Urine that is darker than usual
• Dry mouth
• Sleepiness or fatigue
• Extreme thirst
• Headache
• Confusion
• Dizziness or lightheadedness
• No tears when crying

Some people are at higher risk of dehydration. They include people who:

Exercise at a high intensity (or in hot weather) for too long

Have certain medical conditions (kidney stones, bladder infection)

Are sick (fever, vomiting, diarrhea)

Are pregnant or breastfeeding

Are trying to lose weight

Can’t get enough fluids during the day

Are on medications that contribute to dehydration

Older adults are also at higher risk. As you get older, your brain may not be able to sense dehydration. It doesn’t send signals for thirst.

Note that water makes up more than half of your body weight. You lose water each day when you go to the bathroom, sweat, and even when you breathe. You lose water even faster when the weather is hot, when you’re physically active, or if you have a fever. Vomiting and diarrhea can also lead to rapid water loss. Be sure to actively drink plenty of water to avoid becoming dehydrated.

Questions to ask your doctor

• I don’t like water. What’s the next best thing to keep me hydrated?
• What can I add to water to make it taste better?
• What if I can’t consume as many fluids as doctors recommend?
• What does it mean if I drink a lot of fluids but don’t urinate often?
• How does drinking alcohol affect hydration?
• Am I on any medications that contribute to dehydration?

As an actor, it can be difficult to keep your personal life private while also dealing with the demands of the media and your fans. (You’ve heard enough stories of successful actors having their privacy compromised to know this is true.) As a result, it’s easy to make decisions in the moment that can negatively impact both your acting career and personal relationships.

To avoid these types of decisions, let me offer seven tips on successfully managing your private life and relationship to the media and your fans.

1. Monitor your fame and adjust accordingly.
The first step an actor can take is to monitor his or her fame. If you become a household name, get ready for a serious upswing in media exposure and plan accordingly.

2. Develop a plan for managing your private life.
This directly follows the first step. As soon as you feel your career rising, decide what you’re comfortable with making public and what needs to stay private. Don’t wait until the last minute to decide that you need your privacy. Plan ahead of time to avoid any future problems. It may not be fair, but that is often the price you pay for fame and recognition.

3. Be consistent.
Typically, if a celebrity is consistent in the way they do things and deal with the media, they’re not as exciting to the media and therefore won’t get as much attention. The media much prefer people who cause attention to be paid in their direction.

4. Stay out of the spotlight.
Why is it that some actors are always in the spotlight while others seem to stay under the radar?  It all comes down to choices. If you want to have a private life that’s separate from your career, make the decisions that will keep you out of the spotlight. https://saloncandnailspa.com/

READ: Remember This When Your Friends Book More Work Than You

5. Watch your public actions.
An actor should be consistent in their behaviors when they’re out in public. The media and fans are eager to jump on anything that raises questions or flags. If you want privacy, don’t give the media something to talk about.

6. Learn from others.
There are many people in this industry who have struggled to deal with the pressure of fame, popularity, and money. Some of worked through the pressures and landed in a good, healthy place while others have succumbed. Learn from the past.

7. Remember that fame and popularity don’t last forever.
If you’re an actor who gets a lot of attention, remember that your fame won’t last forever. There will always be another, younger, newer actor to fill the spotlight. Ride your wave if you want to, but remember that eventually, it will end, for better or worse.

The bottom line is that the media is only interested in you if they know there’s money to be made off your image and name. Use that to your advantage when it comes to maintaining a private life, and don’t give them anything that could make them money.

I can’t emphasise enough the fact that in business there is often no single right or wrong answer.

Even if you decide that the idea is a good one, the fact that you’ll have analysed and considered it in detail will allow you to act with greater confidence. Conversely, if you decide that the reality of your proposed idea isn’t quite as promising as your initial thoughts suggested, the time that you’ve put into exploring it more fully before launching will payback many times over. The earlier you find out that a new avenue isn’t viable the better, because you will minimise the cost, effort and attention that you invest in it overall.

There is no fail-proof recipe for business success, but using whole-brain thinking – that is, getting the best from data and evidence (sometimes called ‘left-brain thinking’), intuition and gut instinct (‘right-brain thinking’) will help you to read a new business opportunity with maximum insight and give you the best chance.

Emotions drive decision-making

Humans make decisions subconsciously, based on emotions – how we feel, and how we want to feel. We then justify those decisions to ourselves and other people using logic and rationale.

The metaphor of the elephant and the rider, which you can read about here, illustrates the role of emotions in decision-making brilliantly.

There is no fail-proof recipe for business success, but using whole-brain thinking will help you to read a new business opportunity with maximum insight and give you the best chance. Click To Tweet

To sense-check your decision-making, there are some straightforward steps that you can take to bring as much objectivity into your thinking process as possible, by using data and seeking evidence.

Use data for strategic decisions

The use of statistics in business can be dated back as early as the 8th century (1). Data can be used to drive your decision-making to provide insight into the current situation as well as to help create sensitivity and scenario models and forecast the future performance of the business.

The challenge is often having too much or too little of the right sort of data. I’ve worked on decisions for corporate clients ranging from literally multi-millions of pounds through to tens of thousands for smaller businesses. To be honest, the only difference between the two is the scale and value of the money involved. The importance is the same – a few thousand pounds to an entrepreneur is often a big, big spend for them. The process I use is the same too, because it really works in terms of thinking things through, whatever the level of investment and whoever is making it.

Here’s a step-by-step guide to making better business decisions.

Step 1. Define the questions

The first step is to define the questions that you need, or would like to have answers to, to inform your decision. Without taking this step, any subsequent data analysis is likely to have limited impact on the decision-making processes.

Write down all the things you want to know about your decision, using these questions as prompts:

1. What do I need to know in order to make a sound, well-informed decision? Why do I need to know these things?
2. What information would I like to have, but don’t know how to get? Who could I ask to help me with that? (If you’re stuck, get in touch with me via our Contact Us page, and I will do my best to help).
3. What information would I love to have, but know that I am not going to get? What can I do to get round this?

Step 2. Define your measures of success

Define what success looks like and how you will measure that success in practise. What do you intend to realistically achieve, and by when?

You can ask yourself how achievable these goals seem to be and look at any past performance you may have experienced as an informative comparison.

This may seem early, but please do consider profit margins as soon as you can in your decision-making process. A business idea is only a good one if it enhances your company’s performance.

At this stage I recommend that you use a quick, high-level break-even analysis to take the costs of production, marketing, tax, and sales revenue into account. I love this free, easy-to-use break-even analysis tool here.

Completing your break-even calculations will provide far clearer insights https://thetravisfund.org/. Simply stating that you need to sell 10,000 units to make a profit is far different to knowing the magic numbers. You will need to do much deeper, more detailed analysis later, but this should give you an early view.

Step 3. Go and get the data together

Collect the right types of data and information from the right audiences. Don’t rely on just one data set or one perspective. You need to see your opportunity from different angles in order to select the optimal route.

Make sure that the any briefs, surveys or other methods are designed to:

1. Answer as many questions as possible to the questions you defined in step 1; and
2. Help you, wherever possible, to assess how realistic the goals are that you have set in step 2.

Studies show that companies using consumer behaviour insights outperform those that don’t use them by 85% on sales growth (2) and 25% on gross margins. Collecting this data can help you forecast how a new product is likely to perform with your existing clients, or how new demographics may respond to the business, for example.

Look out for growing trends in your industry or for specific products. This can go a long way to pinpointing the right solutions when looking at different product development options. Netflix (3) does this when evaluating content development choices.

Tip: look inside as well as outside your organisation

Please don’t forget, as some entrepreneurs do, any data that exists within your business already. There is usually a lot of untapped, potentially illuminating insight to be found by looking at recent historic sales and profit trends for products and services and doing a deeper dive into segmenting and really understanding your existing customer base. We can be so keen to move ahead to the next thing that sometimes we don’t use the goldmine of information that’s right under our noses.

My final point about this step is to explore timings. Even when a good idea is a good idea forever, insights gained from the right data (5) can offer clarity in many areas, such as the right month or quarter to launch scale the business or the right time to launch a new product. This includes internally – consider resourcing, cashflow, other events – as well as market responsiveness.

Step 4: Turn data into usable insight

Analyse the data by manipulating it in a way that can shed light on the issues that you set out to answer in step 1 of the process. This is turning data into insight. If you’re not great at this, it really is worth asking for some expert help. Remember that a small investment in making the right call now can save you a lot more money in the long run.

Step 5: Interpret the data

Use the answers to those original questions as a form of guidance as to which decision should be made, as well as proactively looking out for any new issues that may have surfaced. At this stage, do your best to put any personal bias, feeling that you know best, and individual beliefs to one side. Listen to what the data is telling you as objectively as you can. You will have the opportunity to overlay your own views later on.

In addition to providing insight that aids you and your team, your groundwork will go a long way to satisfying shareholders and other financial backers if you need to get their support.

Use data in hindsight for descriptive analysis of what happened as well as diagnostic analysis of why it happened. Also use it to gain insight and foresight through predictive analytics of what might happen and prescriptive analytics of how things might be made to happen. Usually, persisting with ‘blind’ decisions that are not supported by any data is truly relying on as much luck as it is judgement.

Starting a small business is exciting and scary, but sharing your product with the world is a beautiful opportunity. It is natural that you want to get it just right.

There are so many things running through your mind: goals, strategy, how to organize your processes and increase revenue, how to cut potential losses. iecpasarminggu.com Although there is no one universal “right” way to own a small business, there are definitely some great examples out there that will inspire you. Let’s look at a few success stories in the world of small companies.

What are the types of small businesses?

A small business is a pretty general term – there are so many structures, colors, and flavors to them. The most popular kinds, however, are the following:

• Sole proprietorships are an example of ordinary small businesses. They are owned by single individuals who are liable for all business transactions, debts, and lawsuits, unlike LLCs, which protect owners from some legal complications;
• Partnerships, also one of the most popular types of small businesses, are run by two or more individuals who are liable for the financial and legal aspects of all their business operations;
• Incorporated companies: this example is registered with a state to become separate legal entities, independent from its owners and shareholders. This is a significant legal distinction since, in the eyes of the law, an incorporated corporation practically becomes a different “person”. Limited liability is provided to the owners of corporations, and in the event of an owner’s passing, the corporation continues to exist.

Professional, scientific, and technical services

What is it? This sector includes a great variety of services. Scientists, lawyers, engineers, and a good number of people with STEM degrees reside within this industry. It also includes advertising, specialized design services, tax preparation, and more.
Why is this field popular? The popularity of this sector is directly related to the number of essential industries and niches it encompasses. Scientists and labs are greatly sponsored by the government and private organizations, ensuring that there are workspaces within several scientific fields. The crimes always get committed, and tax forms always get issued, hence the constant need for lawyers and tax advisers. Industries keep growing and require engineers to optimize processes and increase gains, and everything that has been made needs a design, hence the need for specialized design services.

Real estate & rental and leasing

What is it? This field is all about renting and leasing assets, both tangible and intangible. That means not only property rent and leasing are included, but also patents and trademarks (but not copyrights). This field also includes handling other people’s rent, such as real estate agencies that represent both property owners and buyers. This means: equity real estate investment trusts, rental and leasing of motor vehicles, computers, and consumer goods.
Why is this field popular? Leasing and renting property is always in high demand. You may only want a jetski for the weekend by the lake; a lot of things are only needed for a short period of time, especially in tourist areas. And trademarks are included in almost every major brand out there, coming and going as the companies are passed on or sold.

Administrative, support, and waste management

What is it? This industry sector includes large and small companies that support other establishments. This could be anything from security to HR services. Waste disposal, solicitation, office administration, paperwork, cleaning, and surveillance are all included in the Administrative, Support, and Waste Management sector.
Why is this field popular? What franchise doesn’t have paperwork? How quickly will an office become a health hazard without cleaning? Where will the waste from your community go if no one collects it? How does a store function without surveillance? These are just a few rhetorical questions that emphasize the important role administrative, support, and waste management companies play in our society.

and many others make up the foundation of the financial segment.
Why is this field popular? Finance is involved in everything