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HP: Investing Heavily in webOS R&D, Looking Into Tablets
We’re still reeling from the news that Palm has just been bought by HP. It’s a bold move by both companies, but one that we’re sure is going to take off well. HP wanted to clarify their position on the purchase, along with shine a light on a few of the reasons why they thought buying the house that built webOS would be a good idea. To do that, they had Todd Bradley of HP take the reigns of a live webcast and go over some of the major points, along with some of the finer details. You can find a Q&A session after the break, which does a good job of bringing it all together.
Q. Android has a tremendous amount of momentum in the marketplace, why pick up webOS?
A. The breadth of products represents an enormous opportunity. It’s an early market – we believe developer support for webOS will grow. We believe in choice, and we believe to remain a strategic partner with Microsoft. There’s a unique opportunity to create products with webOS.
Q. What’s the timeline to evolve webOS to run on HP hardware and specifically on different form-factors with larger screens?
A. We need to get the transaction closed before we talk timelines.
Q. Can you talk about the competitive landscape – where do you see devices running webOS, are they consumer first or corporate?
A. While Palm currently has smartphones, that’s a space that’s very consumer oriented. We’re looking at how to broaden our distribution. Tablets and slates are such new categories, we’re still looking at that. There’s terrific interest in terms of vertical deployment.
Q. You referenced leveraging your strategic positions, what would you characterise HP’s position at the carrier level, and how will they help alleviate the challenges Palm has faced?
A. Investments in building out application/developer capabilities will be very important. As we build our execution plans we focus on leveraging several larger carriers – that will provide a significant growth platform going forward.
Q. Will the Palm R&D team remain separate within HP?
A. We intend to operate it as a business unit, in line with the structure today. Jon Rubinstein is excited at the opportunity it represents to build out the platform.
Q. Will iPaq remain on WinMo and Palm on webOS, or will they merge?
A. We haven’t made those decisions, and won’t until after the deal is finalised.
Q. We’ve seen Apple succeeding as a content platform in addition to hardware – they’re doing a lot of content aggregation. Do you intend to get into that side of the business moving forward?
A. Our focus is to provide connected devices that allow people to connect seamlessly to their information whether that be work or entertainment. We’re not going to get into specifics of strategy until we finalise the transaction.
Q. Palm are spending around $190m a year on R&D – do you think that’s adequate?
A. We’ll be putting more money into all of it – investing heavily not only in R&D but in sales & marketing
HP is taking this very seriously, just as we expected them to. And while the details are being kept close to the vest right now by both companies, we can expect to learn a lot more as the months lead us into the future. There’s obviously a point where Palm and HP will have to decide on the current Palm handsets being worked on, and which of those will make it to the real world, but with HP’s wide range of funds, R&D, and reach into almost every angle in the tech market, Palm seems to be in capable, and wealthy, hands.
You're reading Hp: Investing Heavily In Webos R&D, Looking Into Tablets
Scalable Data Processing In R
Most of the time, the R programmers encounter large data that causes problems as by default variables are stored in the memory. The R language doesn’t work well with a huge amount of data larger than 10% of the computer’s RAM. But data processing should be scalable if we want to excel in the field of data science. So, we will discuss how we can apply certain operations and use scalable data processing easily when the data is sufficiently larger than the computer’s RAM. The discussion would also be focussed on dealing with “out of core” objects.
What is Scalable data processing?Scalability is a very important aspect when big data comes into the picture. As we know, it takes more time to read and write data into a disk than to read and write data into RAM. Because of this, it takes a lot of time when we need to retrieve some data from a hard disk as compared to RAM. The computer’s resources like RAM, processors, and hard drives play a major role in deciding how fast our R code executes. As we know that we cannot change such resources (except replacing them with new hardware) but we can make use of these resources in an efficient way.
For example, let us consider that we have a dataset that is equal to the size of the RAM. The cost could be cut by loading only the part of the dataset that we actually require at the moment.
Relationship between processing time and the data sizeThe processing time depends upon the size of the dataset. The larger dataset generally takes a longer time to process. But it is important to note that the processing time is not directly proportional to the size of the dataset.
Let us understand in simple words by taking an example. Suppose we have two datasets out of which one is twice the size of the other. So, the time taken to process the larger dataset is not twice the time taken to process the other dataset.
The processing time for the larger dataset would obviously be more than the smaller one but we cannot clearly say that it would be twice the smaller one or three times etc. It totally depends upon the operations being performed on the dataset elements.
R provides us a microbenchmark package that can be used to compare the timing of two or more operations. We can also plot the difference using plot() function in R.
ExampleFor example, consider the following program that sorts random arrays of different sizes −
#
Load
the microbenchmarkpackage
library
(
microbenchmark)
#Time
comparison between sorting vectors of # different sizes microbenchmarkObject<
-
microbenchmark
(
#Sort
a random normal vector length5e5
"5e5"
=
sort
(
rnorm
(
5e5
)
)
,
#Sort
a random normal vector length4e5
"4e5"
=
sort
(
rnorm
(
4e5
)
)
,
#Sort
a random normal vector length3e5
"3e5"
=
sort
(
rnorm
(
3e5
)
)
,
#Sort
a random normal vector length2e5
"2e5"
=
sort
(
rnorm
(
2e5
)
)
,
#Sort
a random normal vector length1e6
"1e6"
=
sort
(
rnorm
(
1e6
)
)
,
times=
15
)
#Plot
the resulting benchmark objectplot
(
microbenchmarkObject)
As you can see in the output, the execution time does not come out to be the same every time. The reason for this is that while the system is executing the R code, other things are also going under the hood.
Note that it is also a good practice to keep the track of the operations using the microbenchmark library while evaluating the execution time of R code.
Working with “out of core” ObjectsIn this section, we will discuss the big.matrix object. The big.matrix comprises an object in R that does more or less the same as the data structure in C++. This object seems like a generic R matrix but prevents developers from memory-consuming problems of a generic R matrix.
Now we will create our own big.matrix object using the read.big.matrix() function.
The function is also quite similar to read.table() but it requires us to specify the type of value we want to read i.e, “char”, “short”, “integer”, “double”. The name of the file must be given to hold the matrix’s data (the backing file), and it needs the name of the file to hold information about the matrix (a descriptor file). The outcome will be stored in a file on the disk that keeps the value read with a descriptor file that keeps more description about the resulting big.matrix object.
Installing bigmemory libraryBefore moving further, we need to install the “bigmemory” library. You can download this library using the following command in CRAN −
install.packages("bigmemory") Importing bigmemoryThe first step is to import the bigmemory library. Import the library using the following command −
library(bigmemory) Downloading the FileNow download a sample csv file “mortgage-sample.csv” −
# Download file using URL Creating big.matrix objectWe will now create an object of big.matrix. For this purpose, the argument passed is going to be “mortgage-sample.desc”. The dimension of the object can be extracted using dim() function −
Example#
Create
an object of object<
-
read.
big.
matrix
(
"mortgage-sample.csv"
,
header=
TRUE,
type=
"integer"
,
descriptorfile=
"mortgage-sample.desc"
)
#Display
the dimensionsdim
(
object)
Output [1] 70000 16As you can see in the output, sizes of the big.matrix object have been displayed.
Display big.matrix objectTo display the first 6 rows, we can use the head() function −
Examplehead
(
object)
Output enterprise record_number msa perc_minority [1,] 1 566 1 1 [2,] 1 116 1 3 [3,] 1 239 1 2 [4,] 1 62 1 2 [5,] 1 106 1 2 [6,] 1 759 1 3 tract_income_ratio borrower_income_ratio [1,] 3 1 [2,] 2 1 [3,] 2 3 [4,] 3 3 [5,] 3 3 [6,] 3 2 loan_purpose federal_guarantee borrower_race [1,] 2 4 3 [2,] 2 4 5 [3,] 8 4 5 [4,] 2 4 5 [5,] 2 4 9 [6,] 2 4 9 co_borrower_race borrower_gender [1,] 9 2 [2,] 9 1 [3,] 5 1 [4,] 9 2 [5,] 9 3 [6,] 9 1 co_borrower_gender num_units affordability year [1,] 4 1 3 2010 [2,] 4 1 3 2008 [3,] 2 1 4 2014 [4,] 4 1 4 2009 [5,] 4 1 4 2013 [6,] 2 2 4 2010 type [1,] 1 [2,] 1 [3,] 0 [4,] 1 [5,] 1 [6,] 1 Creating TableWe can also create a table. The sample csv file contains year as the column name, so we can display the number of mortgages using the following program −
Example#
Create
a tablefor
the number # of mortagesfor
each specific year(
table
(
object[
,
"year"
]
)
)
Output 2008 2009 2010 2011 2012 2013 2014 2023 6919 8996 7269 6561 8932 8316 4687 5493As you can see in the output, the number of mortgages have been displayed for each specific year.
Data SummaryNow we have learnt how we can import a big.matrix object. We will now see how we can analyze the data stored in the object. R provides us binanalytics package using which we can create summaries. You can download biganalytics library using the following in CRAN −
install.packages("biganalytics") ExampleNow let us consider the following program that displays the summary of the mortgage −
library
(
biganalytics)
#Download
file using URL destfile=
"mortgage-sample.csv"
)
#Create
an object of object<
-
read.
big.
matrix
(
"mortgage-sample.csv"
,
header=
TRUE,
type=
"integer"
,
descriptorfile=
"mortgage-sample.desc"
)
#Display
the summarysummary
(
object)
Output enterprise 1.0000000 2.0000000 1.3814571 0.0000000 record_number 0.0000000 999.0000000 499.9080571 0.0000000 msa 0.0000000 1.0000000 0.8943571 0.0000000 perc_minority 1.0000000 9.0000000 1.9701857 0.0000000 tract_income_ratio 1.0000000 9.0000000 2.3431571 0.0000000 borrower_income_ratio 1.0000000 9.0000000 2.6898857 0.0000000 loan_purpose 1.0000000 9.0000000 3.7670143 0.0000000 federal_guarantee 1.0000000 4.0000000 3.9840857 0.0000000 borrower_race 1.0000000 9.0000000 5.3572429 0.0000000 co_borrower_race 1.0000000 9.0000000 7.0002714 0.0000000 borrower_gender 1.0000000 9.0000000 1.4590714 0.0000000 co_borrower_gender 1.0000000 9.0000000 3.0494857 0.0000000 num_units 1.0000000 4.0000000 1.0398143 0.0000000 affordability 0.0000000 9.0000000 4.2863429 0.0000000 year 2008.0000000 2023.0000000 2011.2714714 0.0000000 type 0.0000000 1.0000000 0.5300429 0.0000000The output shows the summary of the object by specifying min, max, mean, and NAs values.
ConclusionIn this tutorial, we discussed scalable data processing in R. We started with how processing time is related to data size. We saw the working of “out of core” objects like big.matrix. I hope through this tutorial you would have gained the knowledge of scalable data processing in R which is important from the perspective of data science.
Data Types In R With Example
In this tutorial, you will learn:
What are the Data Types in R?
Following are the Data Types or Data Structures in R Programming:
Scalars
Vectors (numerical, character, logical)
Matrices
Data frames
Lists
Basics types
4.5 is a decimal value called numerics.
4 is a natural value called integers. Integers are also numerics.
TRUE or FALSE is a Boolean value called logical binary operators in R.
The value inside ” ” or ‘ ‘ are text (string). They are called characters.
We can check the type of a variable with the class function
Example 1: # Declare variables of different types # Numeric x <- 28 class(x)Output:
## [1] "numeric" Example 2: # String y <- "R is Fantastic" class(y)Output:
## [1] "character" Example 3: # Boolean z <- TRUE class(z)Output:
## [1] "logical" VariablesVariables are one of the basic data types in R that store values and are an important component in R programming, especially for a data scientist. A variable in R data types can store a number, an object, a statistical result, vector, dataset, a model prediction basically anything R outputs. We can use that variable later simply by calling the name of the variable.
To declare variable data structures in R, we need to assign a variable name. The name should not have space. We can use _ to connect to words.
To add a value to the variable in data types in R programming, use <- or =.
Here is the syntax:
# First way to declare a variable: use the `<-` name_of_variable <- value # Second way to declare a variable: use the `=` name_of_variable = valueIn the command line, we can write the following codes to see what happens:
Example 1: # Print variable x x <- 42 xOutput:
## [1] 42 Example 2: y <- 10 yOutput:
## [1] 10 Example 3: # We call x and y and apply a subtraction x-yOutput:
## [1] 32 VectorsA vector is a one-dimensional array. We can create a vector with all the basic R data types we learnt before. The simplest way to build vector data structures in R, is to use the c command.
Example 1: # Numerical vec_num <- c(1, 10, 49) vec_numOutput:
## [1] 1 10 49 Example 2: # Character vec_chr <- c("a", "b", "c") vec_chrOutput:
## [1] "a" "b" "c" Example 3: # Boolean vec_bool <- c(TRUE, FALSE, TRUE) vec_boolOutput:
##[1] TRUE FALSE TRUEWe can do arithmetic calculations on vector binary operators in R.
Example 4: # Create the vectors vect_1 <- c(1, 3, 5) vect_2 <- c(2, 4, 6) # Take the sum of A_vector and B_vector sum_vect <- vect_1 + vect_2 # Print out total_vector sum_vectOutput:
[1] 3 7 11 Example 5:In R, it is possible to slice a vector. In some occasion, we are interested in only the first five rows of a vector. We can use the [1:5] command to extract the value 1 to 5.
# Slice the first five rows of the vector slice_vector <- c(1,2,3,4,5,6,7,8,9,10) slice_vector[1:5]Output:
## [1] 1 2 3 4 5 Example 6:The shortest way to create a range of values is to use the: between two numbers. For instance, from the above example, we can write c(1:10) to create a vector of value from one to ten.
# Faster way to create adjacent values c(1:10)Output:
## [1] 1 2 3 4 5 6 7 8 9 10 R Arithmetic OperatorsWe will first see the basic arithmetic operators in R data types. Following are the arithmetic and boolean operators in R programming which stand for:
Operator Description
+ Addition
– Subtraction
* Multiplication
/ Division
^ or ** Exponentiation
Example 1: # An addition 3 + 4Output:
## [1] 7You can easily copy and paste the above R code into Rstudio Console. The output is displayed after the character #. For instance, we write the code print(‘Guru99’) the output will be ##[1] Guru99.
The ## means we print output and the number in the square bracket ([1]) is the number of the display
Example 2: # A multiplication 3*5Output:
## [1] 15 Example 3: # A division (5+5)/2Output:
## [1] 5 Example 4: # Exponentiation 2^5Output:
Example 5: ## [1] 32 # Modulo 28%%6Output:
## [1] 4 R Logical OperatorsWith logical operators, we want to return values inside the vector based on logical conditions. Following is a detailed list of logical operators of data types in R programming
Logical Operators in R
The logical statements in R are wrapped inside the []. We can add as many conditional statements as we like but we need to include them in a parenthesis. We can follow this structure to create a conditional statement:
variable_name[(conditional_statement)] Example 1: # Create a vector from 1 to 10 logical_vector <- c(1:10)Output:
## [1]FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE Example 2:In the example below, we want to extract the values that only meet the condition ‘is strictly superior to five’. For that, we can wrap the condition inside a square bracket precede by the vector containing the values.
# Print value strictly above 5Output:
## [1] 6 7 8 9 10 Example 3: # Print 5 and 6 logical_vector <- c(1:10)Output:
## [1] 5 6Hp Or No Hp: The Pc Lives
Suggestions that the PC is dead are greatly exaggerated. Flexibility, innovation and users’ storage needs will keep it around a good long while.
Hewlett-Packard, the largest computer maker in the world, said Wednesday it’s looking to get out of the consumer PC business. HP believes its fortunes lie in a business similar to IBM that focuses on supporting large enterprises instead of trying to profit off the razor-thin margins of Pavilion PCs.
Also recently, Mark Dean, IBM’s chief technology officer for the Middle East and Africa and an engineer who worked on the IBM’s first PC, the 5150, declared the end of the PC. Dean argued the traditional mouse-and-keyboard computer was going the way of the vacuum tube, typewriter, vinyl records and a number of other extinct or outmoded technologies. The IBM exec says he now uses a tablet as his primary computing device.
Market research firm IDC recently cut its PC growth forecast for 2011 by nearly 3 percentage points from 7.1 to 4.2 percent (IDC and PC World are both owned by International Data Group).
Clearly, the PC is dead, right? Well, no.
First of all, despite falling growth in PCs this year, IDC also predicts PC sales will rebound in 2012 driven by laptop sales. Shoppers are expected to opt for PCs next year thanks to devices with thinner designs, longer battery life, instant on, and touch.
But it’s not just sales that will keep the PC afloat, there also are practical reasons to keep the PC alive for the foreseeable future.
We All Drive Trucks For A LivingSteve Jobs in 2010 famously compared PCs to trucks. The argument goes that more people will gravitate toward using tablets while PCs will be used by people who need some serious horsepower to drive down the digital highway such as graphic designers and video editors. The premise of this argument is that people largely use PCs for doing email and other light typing, checking Facebook, watching videos and playing games.
The problem is an untold number of people still need to use PCs at work, because tablets (at least as they stand now) simply aren’t up to the job.
Even lowly writers are better off using a PC, as PC World’s Tony Bradley recently discovered during a month-long bender with an iPad.
PCs Are FlexibleTablets aren’t even close to being this flexible. You might be able to jailbreak the iPad to get unauthorized software to run on it, but that’s much more involved than inserting a bootable thumb drive. Tablets are also designed to be replaced year after year because the hardware is largely static and unchangeable, while PCs can change as your needs change and are typically serviceable for at least 3-5 years.
Innovation Is Out TherePCs with integrated touchscreens such as the Acer Aspire Z5610 are evolving, voice control is getting better, and Microsoft’s Kinect Windows SDK may open up a whole new world of interaction for the PC. Who knows? Maybe those wall-sized PC displays predicted by tech luminaries and sci-fi writers may become a standard part of most people’s homes before the decade is out.
You Need To Store Your StuffRight now, tablets have puny storage capabilities compared to a PC, where 500GB drives are standard and 1TB drives are easy to come by.
But what about “the cloud,” you say? “We’ll just store all that stuff online.” Maybe. But the future of the cloud is currently up for debate. Amazon and Google want you to use the cloud as if it were a hard drive accessible from anywhere you have an Internet connection.
Connect with Ian Paul ( @ianpaul ) and Today@PCWorld on Twitter for the latest tech news and analysis.
Top 10 Venture Capitalists Investing In Ai And Ml Start
Artificial intelligence and machine learning applications are certainly going to be the lifeblood of the next industrial revolution and it is almost there. While AI/ML development is largely in its developmental stage, unique inventions by AI and ML startups focussed on AI operating systems, self-learning language processing platforms, and self-driving cars are making a wave in the tech sector. Given its potential to disrupt growth and development scenarios in literally any sector, venture capitalists are jumping on the bandwagon to fund the brains behind the projects. Check out the top 10 venture capitalists investing in AI and ML startups.
Softbank GroupMasayoshi Son who owns this Japanese MNC group is known to have devoted 97% of his ‘time and mind’ to AI development. Its technological development fund, Softbank Vision Fund is the largest in the world at $93 billion out of which $28 billion has gone into AI-focused projects.
Institutional Venture PartnersIt is a US-based private equity fund, largely involved in providing funds to companies in later stages of development. IVP has become a major investor in AI startups with its investments in companies like GitHub, Soundcloud, and Indiegogo. The total investments in AI and ML startups add up to $7 billion.
Two Sigma VenturesIt is a subsidiary of Two Sigma Investments focussing on early-stage capital that focuses on IT and Computer technology projects aiming at bringing positive change in the world. Amper Music, an AI software for making music for videos based on emotional mapping, Zymergen, an AI software to improve biological manipulation, and Socore, a predictive analytics software are some of the AI companies its funds are parked in.
Lightspeed VenturesA US-based VC firm is into AI/ML investments for early-stage projects. Its tech portfolio includes companies that were later acquired by Oracle, Texas Instruments, Blackberry, Walmart, and other corporate biggies. Their biggest investment includes $ 7 million in chúng tôi an AI company known for developing applications for revenue optimization.
Andreessen HorowitzIt is one of the most well-known names among venture capital firms investing in AI. With its humble beginnings in Silicon Valley, California in 2009, it has now reached the status of venture capital unicorn investing in all stages of company development. Apart from raising $30 million for chúng tôi it has a dedicated bio fund to help medical AI companies.
Toyota AI Ventures Y CombinatorAs a business incubator, they are experts in investing small amounts of money in large numbers of AI/ML startups. After investing $150K twice a year into startups, they focus on strategic planning for 3 months working closely with company experts. The projects it has so invested in include May Mobility, Lyre Bird, chúng tôi etc.
Data CollectiveHaving funded 20 AI startups in the last 7 years, it holds the record for backing the most AI companies since 2012. Its main focus lies in funding Big Data companies operating at a global scale. Companies like Plenty, Recursion Pharmaceuticals, and Vicarious are in their portfolio.
Comet LabsAn incubator and fund it mainly invests in AI and robotic startups. They are also into partnering with industry experts to provide startups with an environment conducive to their growth, in technology and business realms. The projects which have received their funding include Creator, Iron Ox, Prenav, etc.
AccelTop 10 Things You Must Know Before Investing In Cryptocurrency
Investing in cryptocurrency might not slow down anytime soon! Note down these facts for more profits
Over the past few years, the significance of cryptocurrencies has grown far and wide. The digital asset market is constantly evolving with investors discovering new use cases regularly. Currently, there are thousands of cryptocurrencies in the market, with Bitcoin as the largest and the greatest of them all. However, the present condition of the crypto market is scaring investors away from it. The market’s growing popularity has led to an increase in cryptocurrency investments, however, investing in cryptocurrency might not be that easy! There are various facts about cryptocurrencies that beginners should understand and analyze before diving into the market, starting with its intense volatility that led to the fall of major cryptocurrencies like Bitcoin and Ethereum. Even though the crypto market’s volatility is worrying investors, investing in cryptocurrency is not likely to slow down anytime soon. In this article, we have enlisted the top 10 things you must know before investing in cryptocurrency in 2023.
Cryptocurrency is Unregulated and Decentralized Extremely VolatileLarge-scale, trusted investments like Bitcoin and Ethereum have lost significant chunks of their values due to their extreme volatility. However, investors are still unaware of how to control the volatility in a manner to satisfy their own needs, without losing massive amounts of funding.
Analyzing Market SentimentsThe buying and selling of cryptocurrencies define what and how customers are feeling about a specific digital asset. Understanding the basic conduct of buying and selling, the rising mainstream adoption of specific crypto, and how it’s being adopted by external users indicate the market sentiments about the digital asset. Beginners should take note of such investments since it demonstrates which cryptocurrency has higher potential to yield profits.
Keeping a Modified Crypto PortfolioInvesting in cryptocurrency requires investors to spread their money across various digital assets. The assortment should include potentially less volatile cryptos, and some volatile, yet high-reward assets like Bitcoin. Keeping a diversified portfolio will help investors endure profits for a longer period of time.
Analyzing Various Crypto DevelopmentsCryptocurrencies are based on blockchain technology that is open-source. It provides investors with the ability to check out the latest developer activity to get a better glimpse of how the crypto might prove useful in the days to come.
Invest Money You are Comfortable LosingCryptocurrencies are innately risky, infact, sometimes plummet down to zero! For instance, the implosion of the Terra LUNA stablecoin token taught investors to not completely put their investments into one token and only invest what they are capable of losing.
Beware of ICOsInitial coin offerings became quite popular, a few years back. However, ICOs became one of the primary hunting grounds for naive investors. ICOs can be extremely risky, hence, investors need to go through the whitepapers of cryptocurrencies on their respective websites before plunging into it.
Choose the Right Crypto Exchange and Wallet ServicesInvestors need to look for trustworthy crypto exchanges and wallet services, through which they can handle their crypto funds and investments. The rising popularity of cryptocurrencies gave birth to several new crypto exchanges and wallet services, however, choosing the right one might make investors quite overwhelmed.
Protecting the Private Keys is CriticalInvestors might not always remember the passwords to all their crypto wallets; however, it is critical that they remember and protect the private keys. Experts say that one of the best ways is to handle crypto funds through a hardware wallet that will not require any internet connection, making it less vulnerable to attacks.
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