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1916 – ESTABLISHMENT OF BMW.BMW can trace its roots back to Karl Rapp and Gustav Otto. In 1916, the Flugmaschinenfabrik ...
07/11/2024

1916 – ESTABLISHMENT OF BMW.
BMW can trace its roots back to Karl Rapp and Gustav Otto. In 1916, the Flugmaschinenfabrik Gustav Otto company had merged into Bayerische Flugzeug-Werke AG (BFW) at government behest. Elsewhere, in 1917, the Rapp Motorenwerke company became Bayerische Motoren Werke GmbH, which was duly converted into an AG (public limited company) in 1918. BMW AG subsequently transferred its engine construction operations – including the company and brand names – to BFW in 1922. The date of BFW’s founding, 7 March 1916, has therefore gone down in history as the foundation date of Bayerische Motoren Werke AG.

1945 – RECONSTRUCTION DIFFICULTIES

After the Second World War, allied soldiers requisitioned and occupied the BMW plants. Since BMW had been classified as an armaments company, machines and tools were dismantled. From 1945 onwards “stopgap” production, mainly of household appliances, was started in Milbertshofen - as was also the case at the Berlin plant.

1959 – BMW REMAINS INDEPENDENT

As the 1950s progressed, the position of the company became increasingly precarious. In late 1959, Daimler-Benz submitted a restructuring offer for BMW subject to a time limit for acceptance. But small shareholders and the workforce rejected this offer at the Annual General Meeting held on 9 December. Their perseverance and his confidence in the BMW 700 motivated Herbert Quandt to expand his package of shares. After the government provided some temporary financial assistance, BMW was restructured under Quandt’s management in the following year.

1973 – BMW HEADQUARTERS AND BMW MUSEUM

Starting in 1970, BMW began building an administrative tower block in the north of Munich. Its unusual shape soon led to it to be known as the "four-cylinder building", and it soon became a notable landmark in the city's architecture. The BMW Museum was installed next to it in a bowl-shaped building that is still one of a kind to this day. The new building complex was officially opened on 18 May 1973.

1990 – THE BMW RESEARCH AND INNOVATION CENTRE: A SPECIAL KIND OF THINK-TANK.

In 1986, BMW AG brought together all research and development work under one roof at the Forschungs- und Innovationszentrum (Research and Innovation Centre, or FIZ) in Munich. It became the first automotive manufacturer to establish such an institution, with around 7,000 scientists, engineers, designers, managers and technicians, working together as part of an integrated team. The facility was officially opened on 27 April 1990. In 2004, the FIZ was expanded with the addition of the Projekthaus building. Incorporating the principles of “construction communications”, the development was completed in two years and spans 12,000 m². The nine-storey building offers an open gallery and atrium, and with its offices, studios and meeting rooms, forms the new heart of the FIZ. Today almost 9,000 staff work at the FIZ.

1998 – ROLLS-ROYCE.

In July 1998, BMW acquired a piece of automotive history. Following long negotiations, the company obtains the brand and naming rights for Rolls-Royce motor cars from Rolls-Royce plc. Rolls-Royce is held entirely by Volkswagen until the end of 2002, when BMW takes on full responsibility for Rolls-Royce Motor Cars, along with all rights. The new Rolls-Royce plant and a new company headquarters are built in Goodwood, in southern England. This is the sixth facility constructed since 1904, scheduled to manufacture newly developed Rolls-Royce models from the start of 2003.

2001 – THE MINI – PREMIUM IN THE SMALL-CAR SEGMENT.

First unveiled as a concept car in 1997, the new MINI was launched by the BMW Group in 2001 – the first premium vehicle in the small-car segment. MINI’s advanced design combined with the athletic, cheeky character expressed the lifestyle of an entire generation. The MINI Hatch was the first in 2001 – with many more model variants to follow.

2013 – LAUNCH OF THE BMW i3.

The BMW Group embarked on a new era of electric mobility. With the BMW i3, the new BMW i brand presented the first all-electric series-production model from the BMW Group. Propelled by an electric motor – and thus entirely emission-free – this vehicle delivers a completely new driving experience that is compelling for its agility and sheer fun. Sustainability has never been so electrifying.

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USA ELECTIONS 2024Donald Trump will be America’s 47th president, after mounting the most momentous comeback in political...
07/11/2024

USA ELECTIONS 2024
Donald Trump will be America’s 47th president, after mounting the most momentous comeback in political history that will hand him massive, disruptive power at home and will send shockwaves around the world. Four years after leaving Washington as a pariah, following his attempt to overturn the 2020 election to stay in office, Trump’s victory defied two assassination attempts, two presidential impeachments, his criminal conviction and many other criminal charges.

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require...
14/08/2024

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence, such as:

1. Learning
2. Reasoning
3. Problem-solving
4. Perception
5. Language understanding

AI systems use algorithms and data to make decisions, often independently, and can improve their performance over time through machine learning.

Types of AI:

1. Narrow or Weak AI: Designed for a specific task, like facial recognition or language translation.
2. General or Strong AI: Aims to match human intelligence, with the ability to reason and apply knowledge across various tasks.
3. Superintelligence: Significantly surpasses human intelligence, potentially leading to transformative changes.

AI applications:

1. Virtual assistants (e.g., Siri, Alexa)
2. Image and speech recognition
3. Natural Language Processing (NLP)
4. Expert systems
5. Robotics
6. Predictive analytics
7. Autonomous vehicles

AI has the potential to revolutionize numerous industries and aspects of life, but also raises ethical concerns, such as:

1. Job displacement
2. Bias and discrimination
3. Privacy
4. Security
5. Accountability

As AI continues to evolve, it's crucial to address these concerns and ensure responsible development and deployment.

Here are some additional aspects of Artificial Intelligence:

AI Techniques:

1. Machine Learning (ML): Enables AI to learn from data and improve performance.
2. Deep Learning (DL): A subset of ML, using neural networks to analyze complex data.
3. Natural Language Processing (NLP): Allows AI to understand and generate human language.
4. Computer Vision: Enables AI to interpret and understand visual data from images and videos.

AI Applications:

1. Healthcare: AI-assisted diagnosis, personalized medicine, and patient care.
2. Finance: AI-powered trading, risk management, and customer service.
3. Education: AI-based learning platforms, adaptive learning, and intelligent tutoring systems.
4. Transportation: Autonomous vehicles, route optimization, and traffic management.
5. Home Automation: AI-powered smart homes, voice assistants, and energy management.

AI Challenges:

1. Explainability: Understanding AI decision-making processes.
2. Transparency: Knowing how AI systems work and what data they use.
3. Bias: Mitigating AI systems' biases and ensuring fairness.
4. Security: Protecting AI systems from cyber threats and data breaches.
5. Ethics: Ensuring AI development aligns with human values and principles.

AI Future:

1. Increased Automation: AI replacing routine and repetitive tasks.
2. Augmented Intelligence: AI enhancing human capabilities, not replacing them.
3. AI for Social Good: Applying AI to solve complex societal challenges.
4. Job Market Shifts: New job opportunities emerging, while others become obsolete.
5. AI Governance: Establishing regulations and standards for AI development and deployment.

Let me know if you'd like........

What is cryptocurrency trading?Cryptocurrency trading is the act of speculating on cryptocurrency price movements via a ...
08/07/2024

What is cryptocurrency trading?

Cryptocurrency trading is the act of speculating on cryptocurrency price movements via a CFD trading account, or buying and selling the underlying coins via an exchange.

CFD trading on cryptocurrencies

CFDs trading are derivatives, which enable you to speculate on cryptocurrency price movements without taking ownership of the underlying coins. You can go long (‘buy’) if you think a cryptocurrency will rise in value, or short (‘sell’) if you think it will fall.

Both are leveraged products, meaning you only need to put up a small deposit – known as margin – to gain full exposure to the underlying market. Your profit or loss are still calculated according to the full size of your position, so leverage will magnify both profits and losses.

Buying and selling cryptocurrencies via an exchange

When you buy cryptocurrencies via an exchange, you purchase the coins themselves. You’ll need to create an exchange account, put up the full value of the asset to open a position, and store the cryptocurrency tokens in your own wallet until you’re ready to sell.

Exchanges bring their own steep learning curve as you’ll need to get to grips with the technology involved and learn how to make sense of the data. Many exchanges also have limits on how much you can deposit, while accounts can be very expensive to maintain.

How do cryptocurrency markets work?

Cryptocurrency markets are decentralised, which means they are not issued or backed by a central authority such as a government. Instead, they run across a network of computers. However, cryptocurrencies can be bought and sold via exchanges and stored in ‘wallets’ .

Unlike traditional currencies, cryptocurrencies exist only as a shared digital record of ownership, stored on a blockchain. When a user wants to send cryptocurrency units to another user, they send it to that user’s digital wallet. The transaction isn’t considered final until it has been verified and added to the blockchain through a process called mining. This is also how new cryptocurrency tokens are usually created.

What is blockchain?

A blockchain is a shared digital register of recorded data. For cryptocurrencies, this is the transaction history for every unit of the cryptocurrency, which shows how ownership has changed over time. Blockchain works by recording transactions in ‘blocks’, with new blocks added at the front of the chain.

In Search Of Artificial Intelligence And Better Outcomes!!🎁💻💰📞⚖️🛒🕯️The term “artificial intelligence” was coined more th...
08/07/2024

In Search Of Artificial Intelligence And Better Outcomes!!
🎁💻💰📞⚖️🛒🕯️

The term “artificial intelligence” was coined more than 60 years ago, but only recently have we begun to realize all the benefits of AI, machine learning and deep learning in our everyday lives.

Most of us already use smart machines that learn, recognize voices, make decisions, solve problems and make recommendations on everything from the routes we drive, to the movies we watch, to the clothes we buy. We have smartphones in our pockets, intelligent personal assistants on our countertops, robots in our factories and autonomous vehicles on our highways. And that’s just for starters.

Artificial intelligence, Deep Learning / Machine Learning Systems are having a major impact on the aerospace industry, too. With the technologies mentioned above, flying is becoming safer, more comfortable, more predictive and outcome based. Airlines improve schedule performance, use less fuel and create a better passenger experience. Airports are more efficient and easier for travelers to navigate. Ground crews turn flights around faster and dispatch operations are getting more efficient and autonomy based. Airlines are able to use the learning systems to derive better segment strategies and charge according to the relevance and value. And aircraft maintenance is easier, faster, prescriptive and more precise.

All this – and much more – is possible because aerospace is such a data-rich ecosystem. With recent advancements in connectivity, data analytics and the Industrial Internet of Things (IIoT), we can use the vast amounts of data available from disparate systems on and off the aircraft to drive outcomes that impact the operational efficiency, mission effectiveness and profitability of all kinds of operators. AI, Machine Learning and Deep Learning are the engines that connects brains throughout the ecosystem. Not to leave out manufacturing, the digital—physical—digital loop bound by digital threads is taking Industry 4.0 to an altogether different level by creating self-learning networks capable of bringing in an astounding level of autonomy and decision making without humans in the loop.

Let’s look at a few areas in the airline world which are impacted very positively and deeply. For example, artificial intelligence is getting at the heart of maintenance solutions, which provides maintenance techs with an unprecedented level of information and insights about the performance and health of onboard aircraft systems. Airline operators have experienced a 35 percent reduction in operational disruptions thanks to artificial intelligence and deep learning infusion.

With AI and Deep Learning, the intelligent and self-learning maintenance system has the capability to monitors onboard systems, model nominal behavior, detect aberrations and analyzes data and patterns from past events to predict that a fault will occur days in advance. Then, the system provides prescriptive insights to recommend corrective actions and alert the supply chain to order the right parts and materials.

Airlines spend a lot on fuel and what a treat would it be to use modern technologies to bring down this significant cost. AI and Deep Learning Models can crunch data from hundreds of available sources and make recommendations that can reduce fuel consumption and hence operation cost. Even a 1-3 percent saving amounts to tens-of-millions of dollars in annual savings for some carriers. And all this is being made possible by the ingress of intelligent and self-learning models. It is helping airlines uncover novel fuel savings opportunities that are tailored for their specific fleet and operating profile, covering critical factors such as weight, engine utilization and fuel planning.

Another great case where we see lot of benefit is streamlining and make ground operations more efficient and automated. Connectivity, data analytics expertise and modelling the success criteria help airlines reduce block time by improving the ground-handling process, which can have a significant impact on the carrier’s on-time performance. It enables airlines and ground service providers reduce turn-around time by as much as 13 – 15 percent, increasing the number of flights that takeoff on time, a key airline metric and passenger-satisfaction factor.

The setup gives the operations team real-time insight into the status, location and activity of all aircraft and ground equipment, creating the first truly “connected ramp.” Ground vehicles transmit data to the enabling solution, allowing users to monitor vehicle use, improve services and enhance safety.

There are many other examples of how connected aerospace, AI and advanced data analytics are helping to reinvent the entire air transportation system by smashing down the data silos of the past and bringing the power of connected to deliver better outcomes for everyone in the aerospace ecosystem.

If you want something done right, do it yourself.Charles-Guillaume Étienne                   ゚
08/07/2024

If you want something done right, do it yourself.
Charles-Guillaume Étienne

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