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New US embassy in Jerusalem is officially open - Duration: 0:58.
The new U.S. Embassy in Jerusalem is open.
Ivanka Trump and her husband, Jared Kushner, spoke at Monday's dedication ceremony, along
with the U.S. ambassador to Israel and Israeli Prime Minister Benjamin Netanyahu.
"What a glorious day!
Remember this moment.
This is history."
President Donald Trump didn't attend the event.
But he did address the crowd in a recorded video message.
"For many years, we failed to acknowledge the obvious, the plain reality that Israel's
capital is Jerusalem."
Trump's decision to relocate the U.S. Embassy from Tel Aviv to Jerusalem has gone over well
with the Israeli government.
But the move has angered Palestinians and sparked deadly protests on the border between
Gaza and Israel.
The new embassy will be housed in what was the U.S. consulate until a permanent location
is found.
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Ethel newborn change name to ALEX is a male newborn|Adorable newborn baby monkey|Monkey Daily 774 - Duration: 10:38.
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What is a Relay System ? - Duration: 6:09.
Today, you are going to learn all about relay systems,
such as what they are, and when we use them.
Hopefully by the end you will have a pretty good understanding of what a relay system is ?
If you enjoy these videos, do us a favor and click on the like button below.
Also, don't forget to subscribe and click the button for notifications.
That way you will never miss another video!
Let's start by talking about what a relay actually is.
According to Merriam-Webster, a relay is,
"an electromagnetic device for remote or automatic control
that is actuated by variation in conditions of an electric circuit and that operates in turn other devices
(such as switches) in the same or a different circuit."
In a common relay system, you would push a button to energize a relay,
which in turn will pass current through a set of contacts that close when it is energized.
There are many common, everyday items that use a relay to operate.
We will dive in to those in a minute, but first I want to talk about why you may need to use a relay system.
A very common reason, especially in an industrial setting,
is for voltage and current requirements.
Many machines and equipment use a higher voltage to run.
To make it safer for the operators, we use a low voltage and current for our controls.
You wouldn't want someone pushing a button with high voltage attached to it.
I know I wouldn't want to on a regular basis.
It takes a very low amount of current to cause bodily harm.
Plus, most push buttons and switches are rated for fairly low current.
When we use a relay, the contacts that close can be rated for much higher current.
Earlier it was mentioned that relays are used in many everyday applications.
One of those applications are the headlights on your car.
When you turn the headlight switch to on, the wires are not connected straight to the light.
The switch has a positive wire from the fuse box.
It then has a wire from other terminal of the switch to a coil terminal on the relay.
Once this coil circuit is closed with 12 volts,
the electromagnet inside the relay is energized and a set of contacts will close.
The "input" contact will have 12 volts connected to it.
The "output" contact will have a wire connected from it to the headlights.
Therefore, once the coil is energized on the relay via the light switch,
voltage will pass from the input contact to the output contact and turn the lights on.
In an industrial setting, relay systems are used regularly.
One very common example is when an electric motor needs to be turned on and off.
Commonly called contactors and motor starters,
they are a prime example of a relay system.
We use a low voltage, low current circuit for our motor controls.
These can be push buttons or sensors that turn on the motor.
In this case we will use a photoelectric sensor to turn on a motor that runs a conveyor belt.
Whenever a box is placed on the conveyor belt, the photoelectric sensor is blocked.
The sensor acts as a switch that will send the low voltage to the coil of the motor starter.
Once the coil is energized, the electromagnetism closes the contacts of the motor starter
allowing the high voltage to pass to the motor and run the conveyor belt.
Once the box is past the photoelectric sensor,
it will turn off the motor starter by removing the low control voltage.
Let's go back over this one more time.
A relay is an electrical device that closes one circuit by being energized by another circuit.
These can be used for many reasons. One that we covered today was for safety.
When we use a relay to power a high voltage or high current device,
while a lower voltage is used to power the controls that energize the relay.
Relays are commonly controlled with switches, push buttons, and sensors.
make sure that you head over to realpars.com
to find even more training material for all of your PLC Programming needs.
We offer many videos to assist you in learning PLC programming
and landing that job in the high paying,
highly sought after field of Automation and Controls Engineering.
Go to realpars.com and subscribe to our highly effective training series now.
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What is the skill set that you need to be successful in data science? - Duration: 41:04.
[APPLAUSE] >> So thanks so much for
joining in the, in the career panel.
It's, for me, always been one of the most exciting parts of
is to have a little fire chat about various issues.
Related to careers, related to, women in data science, and
anything else that comes up. We'll spend the first 20
minutes or so, talking here on the podium and
I, will feed some questions to the, the panelists. And they
will introduce themselves, and after that it will be open for
quite a long Q&A. So please prepare questions and be ready
to raise your hand. And as we're wrapping up, I will
announce that we'll have last question here from the panel.
And so then you can already start raising your hands, so
the people with the mic can carry them through. If there
are people on the outside on the live stream who wanna ask
a question, please remember, you do that at the hashtag
which is #2018q or on Facebook.
And, we'll take your questions as well, we'll keep an eye
on that. But, let's start by quick introductions, and
you don't have to say much about your bio because it's,
it's in here. But I'm gonna to give you one prompt, just say
your name, that's good. I would like to hear from you,
one reason why you're here today, so why do you like. And
then I would love to hear from you,
what is the one thing that you heard so
far today that really resonated with you?
Yeah, think, can you do that? >> Yep.
>> [LAUGH]
>> You want to start, Ziya?
>> Yeah, sure.
>> Yeah.
>> I'm Ziya Ma,
from Intel, and I'm very excited to be here because
we all know data science is a fast growing area.
Yet, woman only make 20, well,
actually less than 20% of the data professionals. You know,
it's encouraging to see in the last few years that,
forums like has been working rigorously to drive or
to encourage a woman to get into this field. It's also
exciting to see so many women passionate about data science.
I think in the earlier discussion today,
one thing about that really resonates well with me is,
big data is playing a critical part for data
science advancement. So it is big data advancement and,
algorithm, machine learning, deep learning advancement
together that's pushing forward the advancement of
data science, so. >> Thanks.
>> That's one.
>> All right, so I'm Jennifer,
from Atlassian. Before somebody asks what Atlassian
is, Atlassian is the company that makes software like
confluence that help lots of companies collaborate with
their colleagues. So, so one interesting thing about me is,
actually, I'm a particle physicist originally.
And so I jumped into data science, a few years ago.
I absolutely don't, don't regret that, decision.
And so the reason why I'm here today and
why I think it's very important for all of us to be
here, is To never forget you are not alone in this world,
right. Because where you are, you're a woman in data
science among like large crowds of men. And, you know,
you feel like it's challenging to get your voices heard, and
it's not always easy on the daily basis.
And then at some point, it's reassuring to see that they're
all the people facing the same challenges and
to see other faces that, meet the same challenges that we
do. >> Yeah,
I have mentioned about my background when I was giving
my presentations, so I won't go into it.
But to answer your other question, what is that really
sort of inspired me today. One is to see so many women,
it's one thing to hear about it when you are in Texas.
But coming and seeing the women and
listening to all those women- >> It's not fake news.
>> It's amazing.
>> It's not.
>> It's not fake news, yes,
it's real news. >> [APPLAUSE]
>> And among
the presentations, I enjoyed every single one of them.
One, because I had this interview to give outside, so
I didn't listen. And
I had some credit card issues that I had to deal with,
some credit card fraud, so I missed one presentation. But
one thing that sort of really got me interested is
the Airbnb. Because I was always very kind of wondering,
should I, you know, go for Airbnb.
Who is going to clean the room once, you know,
the apartment or whatever once they. After hearing, right,
what Eleanor has talked about I feel sort of more.
>> You're gonna use it now?
>> I think I'm going to use
it. >> Wow, one more customer.
>> So that's the one thing
that's, you know- >> Thank you.
>> Yes, sort of,
I got very interested. >> Eleanor, how about you?
>> Yeah, that's so cool,
we're definitely working hard to make sure it's a great
experience. >> Yeah.
>> So I hope you give it
a try- >> Thank you.
>> And, yeah, so
I'm so thrilled to be here.
I was at the women in data science conference last year,
and had that kind of moment of shock and
awe at this amazing room of women.
And hearing the speakers, it's so inspiring. I love that
there is this combination of technical talks and also,
kinda more broad talks like a career panel. It's a really,
really nice mix and the people here are just incredible.
So that is a reason that I was inspired to come again.
And obviously, I'm a huge supporter of women in data
science and so excited for this community to grow and
to make sure that people don't feel alone and feel supported.
That's really important,
so that's what I'm here for. Definitely, a highlight has
been connecting with Bhavani about Airbnb. Also, you know,
I think a lot of the talks are really inspiring.
I, one that resonated for me was Letanya Sweeney's talk,
and that kind of call to action at the end about using
data science to make the world a better place.
And the power there, and, you know,
I think that's why I'm in it, and, you know, I think that's
a really inspired message. >> And
we hear this from a lot of people, and
one of the questions that often comes up, you know,
when you talk to women who are thinking about entering
the field or women who are wanting to grow in the field.
They always say, now what do I really need to have
a fantastic career? Now you've all been very accomplished,
but the interesting thing is that you, most of you you have
different backgrounds. So you did computational sciences and
engineering, you did particle physics, you did math and-
>> [CROSSTALK]
>> And computing,
and then you,
from education. So that alone shows that women, and
men, of course, from all sorts of different backgrounds
find data science at some point. And
it leads me to this, this one really critical question for
many is, what is the skill set that you really need to be
successful in this field? Judging from the talks today,
you need to do everything. You need to be mathematics,
computing, you need to have empathy. You need to
understand ethics, you need to understand social sciences,
you need to a good humanist, right? What else do you need
to do, you need to actually be able to program a little bit.
And then you need to have team skills,
you need to be good communicator.
Right, and so a lot of you are probably thinking,
I don't have all of that. >> I think there's actually
just one skill you really need, and
that's the willingness to be challenged when you learn new
things on a daily basis. Because it's all about like
trying new things all the time and being comfortable with
the uncomfortable. >> Being comfortable with
the uncomfortable, yeah, I buy into that,
what else, Ziya? >> I think it also depends on
what kind
of job you want to get into in this field. And sure,
there are marketing jobs, there are sales jobs, even in
the technical field, there are data engineering jobs,
data visualization. The jobs that need,
statistician type jobs, and machine learning,
deep learning, algorithm development type of jobs, or
data. You know, all the type of jobs. I think you have to
ask yourself what is the job that you wanna get into? And
then assess the skill sets that you have today,
and the job that's required for your dream job.
And then you need to figure out
a way to get into that.
Actually I provide professional coaching for
a lot of women f rom Intel and
also from the industry. The many women still
find it's a high bar to move into the data science field.
My advice is, that if you find the gap is too significant for
you, you may wanna make adjacent move first.
Move to an area that is not too much a stretch for
you, you can still leverage your previous expertise. But
also it opens the door for
you to learn about new skills in the data science field.
And then it will better prepare you for
finally moving into the field. So that would be-
>> You wanna add something?
>> Yeah, yes,
I think very good points.
But as you said, one person cannot do everything right? So
for me, focus, focus, focus, that's very important.
You'd work hard, and then look at data science, and
look at the areas that you want to focus on, right?
If it's algorithm development, if it's sort of applying
the algorithms, in that case you really need to be a domain
expert. Whether it's healthcare or it's finance or
cyber security or whatever you need.
So, either focus on your developing these deep
algorithms, or learn enough about the algorithms,
focus on the domains, or maybe educating and
teaching people about data science. So whatever you do,
I believe that you've got to work hard and really focus
on what you have to do. >> That being said,
I'd like to actually add something,
because I'm actually an interesting story, because I
was a data scientist in a lot of different companies,
working on different types of projects, right?
I mean so I definitely see the value of
finding like this one type of data that we really think is
absolutely amazing. So I think you know like obviously
[INAUDIBLE] that was because I believe that the data that we
have is really amazing because, we're in a good
position to understand the way people work.
I would even say we're in a good position to understand
how women work compared to men, I even think that we have
a role to play here. But from switching from an industry to
another one, I actually started realizing that it's
just amazing how much of the knowledge I acquired
as a physicist I was able to transpose to e-commerce,
to software and to other things like an. You also meet
people who have like, you know, this disability of
switching one role to another to actually expand knowledge
across different areas. >> Yeah I think that's very
important, and the agility and you hear this very often.
The other thing that you, that you hear a lot and I was just
reading an interview with Maria Clove in Wired Magazine
in January. And she was saying look, sometimes or
very often and maybe that's different with Intel now,
the the way that positions are advertised and
what people are asking for in the in the job position
descriptions, a lot of people look at this and say I don't
have any of that. Knowing that is not really inclusive of
some of the other skills that are really necessary for
a good data scientist. You know,
some of the communication skill, for example, or
team work, what we sometimes call the softer skills.
Although I wish they weren't called softer,
they're just as important as anything else.
Do you think, for those of you who are hiring people and
helping them, that the way that jobs are described
is actually good enough? >> Actually, you know,
that's a great question. I'll start by sharing an example.
You know, my team actually have deep learning or
AI engineers. I also have people that have been working
in data management or data engineering field for
long time. About 18 months ago, a few data engineers in
my team decided to take, the deep learning training, deep
learning class from Stanford. So the two of them went to
class, they finish the class at the end of the semester,
they passed on the exam and they got some certificate.
Immediately afterwards, both of them, without applying,
they got AI job offers from a top cloud service provider.
That shows actually how desperate the industry is for
the skill set and how low the bar is for hiring people.
>> [LAUGH] So we can,
you can all do it, the bar is really low.
>> [LAUGH]
>> So don't be afraid
of the job descriptions. You know, put put yourself through
some necessary training, develop the basic skills,
you can learn from the field after you get the jobs.
But you just have to take that risk and
give it a try. >> You know.
>> That's a good starting
point. >> The, the other thing that
you hear very often and the difference in when talk about,
people talk about the difference between men and
women. I don't like to make this so generic but-
>> We are, we are different.
>> Yeah, we are a little
different. One of the things that you often hear,
is that when, when man see requirements for, for
the next job or promotion or new job,
they will apply even if they don't have many of those. But
with women we always say well I don't know this, and
I don't know that, so maybe I'm not suitable. In your
encounters in Intel and, and UT Dallas, or at Air BNB,
when you're interviewing people, or Atlassian and,
do you see this? Do you see that women or men, male versus
female applicants come to you? >> I have seen that,
I'm beginning to realize more and
more now because I'm aware of it. In the beginning I wasn't
aware, but I've been around for
so many years, that I am seeing now men are just there,
they regardless of where they come from, it doesn't have to
be, it's not American men or Chinese men or
Indian men, it's regardless, they want to go for it. But
women are always, not always most of the time,
they hesitate, and I'm seeing that. And
it's sort of frustrating, but that's, so that's why I think
women really need other women to support them.
And for me my strongest supporters, as I said,
when I went to Honeywell, is a woman who hired me.
National Science Foundation,
again is the woman who supported me. So
my strongest supporters have been women.
So that's really important. >> I definitely think that
women are more prone to like impostor syndrome you know?
Right so, it's much easier for women to
doubt ourself than I think men in similar situations,
so that's definitely something you observe.
>> And it is very true,
because I have been hiring for many years, it is very true.
Usually when women are 80% confident or ready, that we
think we have 80% or 90% of the skills, that's the time
when we feel comfortable saying I'm ready to apply,
I'm qualified for this job.
But I've seen in many of the cases, men there are only 50%
qualified, but they speak with great confidence that I'm so
ready for this job. >> [LAUGH].
>> I am still convinced this
is where we really need to earn ourselves that
opportunity. We need to talk about our qualification,
the potential, our ability to learn, and then go and fight
for those job opportunities. And before we worry about how
big the gap is, sometimes you just have to make that step.
>> Yeah, it comes back to what
you said, being comfortable, being uncomfortable.
And then of course how much discomfort you can deal
with varies a little bit from person to person, right? But
the message I think here is just jumping, right and do it,
that's great. Okay, so let's shift focus a little bit.
You know, a lot of people also ask,
what is it like to be a data scientist from day to day?
And when, when I give data science lectures or courses,
I always say, well, you know, don't make it too idealistic.
Because as data scientists here,
you can solve the world's problems, but you know,
really, 85% of the time you're doing data wrangling. So,
now maybe I'm not right about this, but The, so the question
is, you know, what does your day look like? And if you're
now managing and not doing much data science yourself, go
back to the time when you were starting data science is. What
do you, what do you do on a, on a daily basis? [INAUDIBLE]
do you wanna talk about it? >> Yeah,
I'd be happy to jump in.
This is actually something we collected data on,
our data science team. >> Of course, yeah.
[LAUGH] >> We took a survey of,
>> So it's a lot of,
lot of coffee drinking, yeah, you know.
>> Yeah, we definitely have
a lot of surveys from the data science team.
We're d, very data-driven. We looked at, time use and asked
people how they're spending their time. And, you know,
actually, I talked about, you know, three tracks of work,
analytics, inference, and algorithms. And, you know,
we do see people spending time in those three areas and,
and having some spikes, depending on their area of
expertise. But I would say that data wrangling is
a constant throughout, for any data scientist on the team.
And, you know, I think that's just what you need to expect.
I mean, if you want to produce great results,
you need to great data. And so spending the time to make sure
that your data is high quality and, you know, that, that's
just gonna pay off in, in the results that you can achieve.
You know, I do think that it's something that,
many companies are trying to decrease right because
it is something that we spend a lot of time on. And
I mentioned the global metrics project as one example, where,
you know, if we can build tools to kind of automate and
scale, defining metrics or building dashboards.
The more that we can, develop those tools to,
scale the work that is taking the most time for
data scientists and, and maybe could be automated,
then that could be a big win for our people on the team. So
that's, that's definitely an active area of research for
us in development with our engineering team,
helping to build those tools.
But, yeah, I'm trying to remember what the exact stat
was now of, like, how much time people spent data
wrangling. It was actually a lot less than I thought.
I think people give the impression that's it's, like,
90% of their time. It wa, it was a lot less than that, but
I'm sure that varies, you know, by company and role, so.
>> I actually believe it
depends on the level of maturity of the company,
right? >> Definitely, yeah.
>> I mean, because actually,
like, working for different types of companies,
different sizes,
I actually find out that when you are on an early
stage company that just gets started with data,
you to do a lot of education. Like, people would tell you,
like, hey, you have a lot, ton of data, you can do anything
you want with it. I don't have the right data.
So it's not necessarily as you see, as you think it is. So,
I mean, obviously, like, there is always an educational part,
like, talking to the other stakeholders.
This is how you need to do your data collection.
This is how much you need to
invest for hiring, for technologies, etc.
So I mean, I personally,
I spend a good deal of my career talking to people and
explaining what data science was about. You always, always
face this world with, like, this idea that, you know,
like, you know, well, [LAUGH] >> It's okay, yeah no, that's
great. But, I was hoping that one of you would say, yes,
you do spend, spend a lot of time on data wrangling, but
that's also fun. >> Mm-hm.
>> [LAUGH]
>> Yes.
>> Yeah?
>> Yeah.
>> Yeah, okay, good.
You know, we don't wanna leave you with this impression 85%
of the job is gonna be totally boring and, you know, I'm not,
I'm not gonna go for it. So, so before we,
we give it to the audience, I, I always like to ask people,
you know, you're working in this field,
you're all very passionate about what you do.
And, and you all have a lot of in-influence and
impact on the field. So tell us about, looking at the field
as a whole, or, or maybe even your own narrower, area.
What are your fondest hopes and your biggest fears?
You know, we hear, we heard so much today about the amazing
opportunities of, of, data science and
all the things that we could do with it. And then also,
you know, so in the questions, some, some fears, and, and
some worries about this. And of course, there's,
there's a reason for this. So tell me, I'm interested to
hear from all of you. What, what is your fondest hope,
you know, what you think will be achieved and, and
your, your biggest fear? Or maybe the other way around,
so that we end on a positive note, so-
>> [LAUGH]
>> Biggest fear,
fondest hope. >> I think, my biggest fear or
concern is the, the supply demand gap for the skill sets,
needed. And I, earlier, I, I gave, an example. And
actually, in the last, few years, both academia and
industry have recognized th-this gap, and
that's why we're seeing an increasing number of academic
programs and, industry workshops to help accelerate
to the closure of the gap. But honestly, in my view,
it's a long journey. It will take quite some time before
we see a true ready and a mature workforce.
And my, hope is that, you know,
data science today still has a high bar. And
even though we expect a wider deployment of data science for
businesses or even into our daily life, but the bar is too
high, and my hope is that in the foreseeable future,
we can truly democratize data science so
that it's more accessible for individuals and
businesses to improve our life quality and
our business results. >> Thanks, Ivani,
do you want to jump in? >> Yes, yes, so
I think, my concern,
the fear is what I said,
right? Is cybersecurity because this data mining,
or data science techniques, could be attacked.
So that is something that I'm really concerned about. And
I don't think, I mean, what I talked about is just one tiny,
tiny, tiny solution about modifying the support victim
issue. The hope, the fondest hope is there's so
much that data science can do, including in cybersecurity
when you apply. But I also agree sort of with what they,
said, the supply and demand. I mean, we really need more and
more data scientists, you know, for all these companies.
And so can we have, a workforce,
a trained workforce? One other thing I wanted to mention,
very briefly, is that we need more federal government
support funding. Cybersecurity fortunately,
we have got strong education like NSF has,
scholarship facilities where we train US citizens for
cybersecurity education, BSMS, B, anyone of those levels. And
then they go, and they have to go and work for
the federal government for a certain number of years.
Two-year scholarship means they've got to work for
two years. But I don't think we have such programs,
at the federal level because sometimes they would say,
why can't the companies pay for science, sorry,
data science? But I think we, I really believe that we need
more support to develop these fundamental techniques and
algorithms. >> Right, so everybody,
NSF listening right now, yep.
[LAUGH] >> NSF and
Department of Defense, and anyone.
>> Trump, in the new budget,
should be in there. >> Yes.
>> Yeah.
>> [LAUGH]
>> What about you?
>> No, I think,
I think for me the biggest worry is,
I still see a huge disconnect between what's, the business,
the business side of things and
data science part of things, right?
I mean, so usually, you have, like, a company that at some
point decides that they have sufficient enough data to,
use it for, like, improving their products or whatever.
And, they give a very severe misunderstanding of what data
science is about, right?
I mean, so you have, like, the two extremes of the spectrum
that on the one side you have the person that didn't use
that with the data you can do anything you want. And
then on the other side, you have the, unbelievers, right?
And so, one of the problems that you see a lot in,
in the industry is that, sometimes you have data
science structured as a lab, right? I mean, so
you will have, like, only data scientist siloed in one place.
Far remote from the stakeholders and
the business people, right? And so I'm always worried
about, like, how do you engage the conversation?
How do we explain to people what data science can do for
your business, right?
I mean, so I think, it's really important to try to
engage the conversation with the right stakeholders.
And I think There is not enough investors in
the science field to do just that.
And this is where we, women, I think are probably you know
like a good communicators. And we have a role to play to
actually like try to tie together the business value of
the together with the- >> So
that is your fondest hope, that more women will come and
then that problem will be solved.
>> Yes.
>> [LAUGH] What about you,
Helena? >> So
I would say my biggest fear is the misuse of data, and
the unintended consequences from doing that.
You know, we have so much machine learning.
And a lot of the talks have covered you know,
some of the negative consequences of that. And
how we have people who maybe are applying the methods but
aren't really paying attention to those consequences, and so
that, that scares me. And I, I'm worried about that and
how do we make sure that that's not happening.
I think it's a really interesting open question.
On the flip side, you know, I, I'm really excited about how
there's a shift to use data more and more for
all kinds of applications.
And really to be thinking strategically and
logically. And for that to be something that many more
people are learning about. One of the programs that's been so
inspiring that we've had at Airbnb is what we call
Data University. And the idea is that we educate people at
the company about how to first ask a good question.
And then how to, you know, do simple things with data to be
able to answer and understand that question.
And also to be able to you known, think critically about
what they might read in the newspaper, right?
Like there are so many studies that get published
that are correlation and not causation,
and how do you think about that? And you know,
I think that there's so much power in everyone being able
to think critically about how the data is being used. And
to start to use data more and more to make better decisions.
So I think that's like an exciting future,
so I guess the, the scare is we don't do it well and
the exciting future is we do it well?
[LAUGH] >> Yeah,
well it does make sense.
Okay we're gonna open it up to questions from the audience.
So lot's of questions, can we get some mics? It's easier.
When you ask a question, if there is a particular person
you would like to answer that question, please just address
it to that person, okay? >> Hi, thank you so much for
all your inputs and your experiences and your stories.
It really helps us.
The one thing that I am very curious to found out is
that as recruiters you might have come across a lot
many applications in a, in your years of experience.
I'm just wondering what really stands out to you when you see
these applications versus data science job positions?
And when you look at these applicants usually,
I mean from a third person's perspective,
I'm just wondering as to whether you really try to keep
a men to women ratio? Do you really look at that? Or
is it just your short list based on what appeals to
you and what fits the job? >> Zia, you want to start
with that, cuz you've done a lot of hiring?
>> Yeah definitely, and
we actually we consider, we take both into considerations.
So the first, we, you know,
you have to have the right skill sets. So
we're looking for, especially, we're not looking for somebody
with all the skills that Margot would just explaining.
[LAUGH] So we're looking for a few critical skill sets.
Let's say if we have an opening for
data engineering job. We look at if the person has any, you
known data management and data storage type of experience.
If we're looking for AI a type of skills, then we wanna see
if the person has algorithm development or
machine learning, deep learning type of skills.
But also we all know this,
Intel is very committed to diversity and inclusion. So
we put a lot of effort, after Intel is going to be the first
high tech company to achieve full representation for women
and underrepresenting minority by the end of this year. And
we'll well on track for that. >> [APPLAUSE]
>> And it's not easy to
achieve that since two years ago we set the goal.
We put a lot of emphasis onto like hiring,
the hiring process. We go extra miles to make sure
that we bring the right diversity candidates
into the the screening pool. We also, you know,
once we hire people, we focus a lot on retention and
inclusion. We wanna to make sure everybody in our company
come to work, you know, bring the full selves to,
to the work environment. So there are a lot of extra work
that we have to do but, yes, we pay attention to both skill
sets and also you know, supporting a, a diversity and
include it into the process. >> Same for the others,
or some significant differences with that?
>> Like, I can
say actually, so
when I started like hiring I was really focused on like
keeping the ratios, right? And so at some point I was like I,
I, I was feeling like I was spending too much time trying
to focus on this. So I just completely ignored the names
and just looking at the resumes. And, and
now what I'm really after is really diversity of thoughts.
So I actually realized maybe a little bit late in my career,
that'll, I come from a applied math background, right? And so
for a while I was working on the team where all
the others were coming from an engineering background. And so
I realized like I'm the only person here who cares about
validating a model. Who cares about doing things right or
doing things like off, you know,
like the mathematical way, right?
And so I, I realized later that it really matters to have
people coming from different backgrounds. And so this like,
I really value a lot when I see on the resume like what
has this person been doing? Do they have something different
to bring to the team that no one on the team has right now?
>> Very nice.
>> I just wanted to add
something very quick. Because I don't do recruiting in
terms of hiring people and but students and faculty. But
what I find is that, in data science, also cyber security,
especially data science and finding there are a lot of,
international students. Master's for graduate level,
Master's and PhDs. So what we are really trying to do is
to actively recruit domestic students for a Master's and
PhD in data science. And
I think cyber security also it's in the same situation, so
you really need to do a much better job.
>> Yeah.
>> I think the only thing I
would add is also looking for people who demonstrate growth
mindset to some degree. You known, trying out new courses,
or, you know, demonstrating that they really
enjoy learning. Because again that's been a theme throughout
that the field is constantly evolving.
And so someone who's excited to learn something new and,
and try something different so that they can keep up to date
with their skills. I think that's really important.
You know, other than that, we definitely focus a lot on
ensuring that we have a diverse
pipeline. And, you know, I think thinking holistically
about diversity as well is really important. You know,
that's, that's, that's what will bring that diversity of
thought that leads to innovation. And so, you know,
not just focusing only on gender, but thinking
about other aspects as well. >> Great.
>> And I really like the idea
of, like, different backgrounds, too. U, I think
that's super important. >> Well, thanks very much for
that, I think. Useful enough, yeah?
You are happy with your answer?
Julia, she, by the way, she's also a high school student.
So and this is her second with. [LAUGH] Go for it.
>> Well thank you.
Miss Jennifer, frankly, you mentioned that you loved
the type of data that you're working with, partly because
it enables you to understand, how people might work.
What about that data, and are there potentially other
data sources out there that you think lend themselves to
a similar purpose? >> So
there is definitely a lot of data, and
there is an explosion of like the different tools that
people use to get their work done. And so you know,
like obviously there is a lot of data.
And so for me like this is like a completely unscratched
part of data science, right? Try to understand how people,
I mean, people talk about social media,
so how do people talk together like casually, right? But
not necessarily how do they get work done, right? But what
I think is really unique about Atlassian is that we don't
have just one product, we have an entire suite of product.
And usually we have customers who have the entire suite, and
so basically, it means that we can form a team from,
you know, like the time that they start a project all
the way to completion. And then, we can do a tone of
really cool things that have not been done yet.
And so that's why I'm definitely excited that we're,
with what we're doing. >> Great, next question.
>> Hi, thank you very much,
and I had a concern with the word democratization.
Just because it just seemed like there was a connotation
there of speed and ease and perhaps shallowness.
And the word that kind of popped into my head as
the counterpoint was respect, respect for
privacy, respect for human rights. And also kind of
respect for the professional integrity of the data science.
I know that for myself going from learning,
there was this big jump when I got a job. Like, no,
it's on me to know what this data is and ask where it is.
It's on me to choose the, the models that I'm gonna use.
It's on me to decide what gets stored and
what gets thrown away. And there were so
many of these things that you couldn't possibly cover in
school. But I was interested in what the panel
had to say about this concept of.
I mean, of course, I love the idea of democratization,
I would love the idea of more women being in the field. But
this idea of what it means, what is our professional
integrity, what is the, what is the, where is our respect?
[LAUGH] And what do we shoot for, I don't know
if that makes sense. >> I would say,
although I'm not working in the industry.
I did work in the industry and federal research lab, NSF, and
now UT Dallas. I think some of the things I'm finding,
you've got to teach the students at an early age when
they are in grad school.
I was, during the lunch I was at the ethics,
data science ethics. Because with data science, you have,
the people that are working in data science,
they have access to all this data. Right,
we are seeing from companies like Airbnb and Atlassian and
Intel and Visa, they have all this data. So you have to make
sure that they are not misusing the data, and
that is something that I am really concerned about.
And that's why I think also
it's very important to have a data science ethics course,
as well as cybersecurity ethics, ethic,
ethical hacking, all of that is so
important to the students. >> Yeah, I so agree with you,
especially nowadays where trust in science is really not
that high. And so a lot of us are really worried about it,
what about you, Ziya, how would you respond?
>> So yeah,
I think it's a very valid concern, but if I look at this
field, I think today the field is still not that mature, and
it's too busy advancing the basic,
what's the basic capabilities. So, and
as we have more complete and
more visible capabilities come into the field,
I think privacy, security will definitely follow.
If you look at it in the domains, before like AI,
before data science, you know big data has been in place for
the last ten years now. But when, the first five years
when big data was evolving, very few people, very few
customers even talked about privacy or data protection.
But nowadays, it is a prerequisite in order for
any enterprise to use big data.
You must have a well governed data store,
well managed data with great privacy.
So I think it's just a natural progression with this field,
and as the capabilities advance, I think,
privacy will come into place. >> Well,
we're catching up a little bit, aren't we, yeah.
So let's go the next question because we only have another
four minutes or so and there are many questions, so
[INAUDIBLE]. >> Hi, good afternoon,
thank you for sharing all your knowledge,
the one piece that I took away was, jump in. Don't be afraid,
like, be comfortable with uncomfortable. So what, so
my question is more around when you jump in, and
I just did that three months ago.
I'm also at Intel [LAUGH] on the marketing side. And
companies can be in a whole paradigm on how they adopt
a data science. So what's your advice on to keep going and
not give up if, and convince your organization to,
to be under, to support data science,
as you keep, >> Not give up,
what's your advice? >> Yes, never give up,
you have to keep going. >> I mean there are days where
you might want to just give it all up,
but never. You got to, I mean, I want to work for
another 20 years, health permitting. Right, cuz you,
there's so much to do, and I really feel sometimes,
I recently tell my husband, I wish I was 30 years younger or
40. It's just glorious days for data science right now,
and it's just so fantastic in all the opportunities that
you guys have. >> So jump in, keep going,
that's the big thing. >> I will say that something
that's very important is to get the right support for it,
I mean, because it's very easy.
Especially because, there are still not a lot of women in
data science. You can end up being the only woman on
your team, right, I mean, so basically, like, make sure you
have the right support system in your family, among your
friends. They understand what you're doing and
what you're going through and, and tap on that
resource as much as possible. >> Well, Eleanor,
now what about you? >> That's great, no,
I 100% agree with, you know, figuring out what will help
you to feel supported. And, you know,
everyone has a different way to kinda get
through adversities, so, find what works for you.
And if it's, you know,
listening to talks like this or talking to your friends,
I think just figure out what you need to do to keep going.
Because the reality is that all of us have had times when
we've failed or times when things didn't go well.
And, and those are dark times,
but, you know, you get through them. And, again,
that's when you really know you're learning. So
I, I tend to think that like if I'm like really stressed or
don't know what to do, that's kind of a signal to me.
That like okay,
I'm gonna learn something here, so keep that in mind.
And I think that also can give you hope to get through it.
That maybe you don't get it right the first time, but
you'll learn and you'll do it better the second time.
>> All right, well,
some last thoughts, I mean, the time just flies,
it's amazing. I don't know Darren what you're doing
with this clock, but I think the time in this auditorium
goes faster than outside. >> So
the clock is being maybe manipulated or-
>> I'm not sure,
I'll have to talk to Darren here. But before we leave,
I would like to give you the opportunity to give one quick
message to give to the audience, your last thought.
Well, not your very last thought, but your last thought
in this career panel. >> [LAUGH]
>> I'm happy to start,
I think that my final thought is to be curious and
look at the data. If you see something that looks funny,
go and investigate. Some of the most fun I've ever had
are looking into something that seemed a little off and
figuring it out. And, I think that's something that once you
develop the habit to do that, it will be really powerful for
you. >> I have said all the things
I need to say except one more thing. I would say,
don't oversell data science, because that's
only going to come and hurt us in the end. So be realistic,
be true to yourself, and as I said, work hard,
never give up. And that's my sort of last piece of advice.
>> [LAUGH]
>> Great, Jennifer?
>> I would say like one thing
that really helped me been like here is that I was
actually a very soft, soft-spoken kind of person.
And then, at some point,
I realized I have to be more assertive, right.
And so, to the point that now sometimes like,
when you like somebody, guys. I tried to explain somebody,
like this is not the right way to do it, you're pushing your
ideas on me, right. So if somebody tells you that,
it's okay, it's like, it's what we're trying to do,
right. We're trying to change the way
data science is perceived and
women in profession are being perceived.
So, I mean, build some ways to build your own self esteem,
and be ready to be assertive and speak up your mind
whenever that is needed. >> Wonderful,
Ziya? >> And
data science is fastly growing, actually,
it's the most promising area. I think we're definitely
moving or already working in the right space. But to me,
I think the advice I have is, collaboration is key.
Because today, a lot of innovations are happening with
academia, with industry top leaders, and
also with open source community.
So collaboration with those partners to make sure that you
stay on top of the curve.
And you are able to leverage the latest technology trend to
solve real business problems. >> Well, thank you all so very
much for joining this panel. >> Thank you.
>> [APPLAUSE]
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The New Boosted Mini Is Dangerously Fun - Duration: 1:59.
For more infomation >> The New Boosted Mini Is Dangerously Fun - Duration: 1:59. -------------------------------------------
This Is Us "Returns This Fall" Recap Trailer (HD) - Duration: 2:28.
-Mrs. Pearson, your husband went into cardiac arrest.
And I am afraid we lost him.
[ Paper rustles ]
♪♪♪♪
-We all knew the pressure that we were under
to deliver this moment.
And when we were doing the scene,
I didn't know that Milo was gonna be there in the bed.
I walked into the room, and I was completely taken aback.
It was like I was able to live out that moment for real.
You're just like -- You're just hit by it.
Oh, my God. I'm such a mess.
-When you sit down and watch "This Is Us,"
you are thinking about your family.
When I held you for the first time,
it hit me like a bolt of lightning.
You were my purpose, Kevin.
-The moments are genuine,
and I think our audience sort of picks up on that.
-And I swear to you, son, you will find yours.
♪♪♪♪
-So, what I really appreciate
is hearing what our fans have to say.
-[ Trumpeting ] -[ Laughs ]
-They want us to know how it makes them feel.
-I've gotten people who've written about adoption,
seeing a black couple who actually really love each other.
-There's something comforting
about knowing that you're not alone.
-I'm right here. -[ Sobbing ]
-Being vulnerable with somebody is such a big deal.
-I think that's why our audience loves the show so much.
That's why I love it.
♪♪♪♪
-♪♪ Let it lead your love away ♪♪
-Dad!
-Take a deep breath, okay?
-♪♪ Oh, oh ♪♪
♪♪ Fade me away ♪♪
-[ Chuckles ] -♪♪ I won't ever be the same ♪♪
-I want to stay out of your way.
-You are not in my way. You are my way.
♪♪♪♪
-I see you, baby.
And I can see our baby's head, and we got this.
-I had the highlight of my day right there,
sitting by my side every day.
-Every piece of the show was made with love.
And people feel that.
♪♪♪♪
-♪♪ Oh, fade me away ♪♪
♪♪ I won't ever be the same ♪♪
-Oh, my God. I think I actually need a tissue.
[ Laughs ]
-------------------------------------------
This Is Us - We're Back This Fall (Digital Exclusive) - Duration: 2:38.
For more infomation >> This Is Us - We're Back This Fall (Digital Exclusive) - Duration: 2:38. -------------------------------------------
Kushner: Standing with Israel is the right thing to do - Duration: 12:14.
For more infomation >> Kushner: Standing with Israel is the right thing to do - Duration: 12:14. -------------------------------------------
Royal wedding: Who is paying for Prince Harry and Meghan Markle's wedding? - Duration: 4:35.
Royal wedding: Who is paying for Prince Harry and Meghan Markle's wedding?
Prince Harry, 33, proposed to Meghan Markle, 36, in November, and the lovebirds will tie the knot this Saturday, May 19.
They will say their vows at St George's Chapel in Windsor, with thousands expected to turn up for the event.
A royal wedding is a joyous occasion, but the price tag for such an event is bound to be eye-watering.
Most weddings are pricey, but a royal wedding does not usually hold back on the budget.
How much will the royal wedding cost? The extravagant event is expected to cost around £32million, according to wedding planning company Bridebook.
The company calculated how much would be spent on flowers, food, entertainment and other expenses such as the dress.
The wedding dress itself is said to cost a whopping £40,000, which Meghan will likely pay for herself.
However, experts claim the vast amount of money will be spent on the security of the event.
The security costs will amount to a massive £30million, with the rest being spent on the event itself.
The wedding is expected to cost more than William and Kate's wedding.
Although the Duke and Duchess of Cambridge's wedding was larger, theirs cost around £20million.
"As was the case with the wedding of The Duke and Duchess of Cambridge, The Royal Family will pay for the core aspects of the wedding, such as the church service, the associated music, flowers, decorations, and the reception afterwards" Kensington Palace Who will pay for the royal wedding? Kensington Palace cleared up who would fork out for the royal wedding.
In a statement, Kensington Palace revealed: "As was the case with the wedding of The Duke and Duchess of Cambridge, The Royal Family will pay for the core aspects of the wedding, such as the church service, the associated music, flowers, decorations, and the reception afterwards." This means the royal family will pay for the bulk of the just-shy-of £2million bill.
The royal family get their money from the British government, known as the Sovereign Grant, which is in turn funded by the taxpayer every year.
The Queen also has a private income, known as a Privy Purse.
This money comes from the Duchy of Lancaster, land in the UK which the Royal family owns.
The monarch makes around £17million a year from the land.
Taxpayers will be paying for the day in terms of security costs.
Policing and keeping the estimated 100,000 visitors to Windsor safe is high priority.
The royal wedding is expected to provide a £500million boost to the country's economy through tourism and merchandise.
Katie Nicholl, author of the new biography Harry, Life, Loss and Love, said: "My sources tell eye that Meghan wants to make a contribution to the wedding. "She's a feminist and a wealthy and independent woman and the fact is that pretty much all of the wedding cost are being picked up by the royal family.".
-------------------------------------------
Why a career in early education is so rewarding - Duration: 3:33.
For more infomation >> Why a career in early education is so rewarding - Duration: 3:33. -------------------------------------------
Dating Today Is Exhausting | Dating In the Age Of Social Media | Dating Advice 2018 | - Duration: 5:28.
For more infomation >> Dating Today Is Exhausting | Dating In the Age Of Social Media | Dating Advice 2018 | - Duration: 5:28. -------------------------------------------
Cute is Not Enough - FUNNY DOG Compilation May 2018 | Life of Dogs - Duration: 4:07.
Thanks for watching
Hope you have a great time
Please, like, comment and subscribe for more!!
-------------------------------------------
Our Schnauzer Puppy is missing prank on Mexcian mom!!😂😂 - Duration: 5:17.
Mom: What happened?
Mom I had her
Well, you can't see them but we took out the dogs
we have to now Oreo and Bella, but we are going to say that Bella disappeared we were walking her and
Her leash broke and she left running and we can't find her guys can tell
It's dark out and we don't know where she went so we can't find her
we are gonna put her in the car in my car my mom's car and we are going to
You know hide her in there and then we're gonna make a big deal about it that we can't find her
Alright guys, so hopefully you guys enjoyed this video and give it a thumbs up, you know share it
What?
She lost Bella and now she blaming me
The what?
She lost Bella and she's blaming me
Mom: Mhm sure
she's always blaming me for everything
Well I had her and I told Chris to wait for me
and he didn't
I'm not going to argue I'm going to
my room
Now who's going to help me look for her?
I was calling her
ANGRY MOM: THAT'S WHAT YIOU GET! I TOLD YOU NOT TO GO OUT! BUT LIKE YOU DO
WHATEVER YOU WANT!
I TOLD YOU! BUT NO THERE YOU GUYS GO!
Chris Slams the door lol
What do I do now?
Angry mom: Let her stay out there
Me: CHRIS!
CHRIS!
Chris: Que!
Ayudame a buscarla
Chris: Pues tu siempre me culpas
Pero todavia ayudame
a encontrarla
Apurate!
Mom: Where did you guys lose her?
Chris: I don't know ask her
Where's the flashlight?
ANGRY ANGRY MOM: HOW FAR DID YOU GO??!!??
Defenseless me: We were just here in front of the house
ANGRY ANGRY MOM: WHY DIDN'T YOU TIE HER TO HER LEASH???!!!!
Me: I did! I don't know how she escaped
I kept pulling her and she kept pulling the opposite way
Are you going?
Donde la dejaste
Estabamos aqui
No estabas alla?
No, aqui estabamos
Entoces como la perdiste
porque ella se fue corriendo
ANGRY ANGRY MOM: SOMEONE STOLED HER BECAUSE SHE WOULD HAVE BEEN SCRATCHING ON THE DOOR BY NOW
SUPER ANGRY ANGRY WORRIED MOM: ARE YOU CRAZY! HOW THE HELL DID YOU LOSE HER??!!
(I had to run because she was going near my car and she was going to find Bella)
Alomejor esta aqui
ANGRY MOM: SHE'LL BE RIGHT THERE OF COURSE!! (VERY SARCASTIC)
RELIEVED MOM: Look where she is!!
Where you see her?? 😂
We were pranking you mom!! 😂
Tu licencia y registro? 😂😂
were you scared mom?
( THAT EVIL LOOK GIVES ME THE CHILLS!)
How do you feel mom??😂
At first you didn't believe us but then you did
Bueno pues, al principio no nos creyo pero despues se dio cuenta que si era verdad
MOM: YEAH SHE MADE ME RUN AROUND IN CIRCLES!
(lmaooo my mom always exaggerating!! 😂😂😂 she walked a small circle around the house😂😂😂)
Si te gusto el video, danos un like, comenten, y compartan el video. Que no se les olvide subscribirse a nuestro canal :)
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-------------------------------------------
🔴🔵MONDAY WEIGH-IN IS HERE HOW MUCH HAVE I LOST THIS WEEK❓❓❓❓❓❓🔴🔵 - Duration: 8:33.
hey guys today is Monday which is way in I didn't do away video last Monday
because I had went out with my sister on Saturday and had me something to eat yes
I did I did not have my juice okay I have my juices in the morning and then I
went to a Korean fried chicken place the chicken was not good we ended up getting
some takeout at Popeyes but we went out for some drinks um and I think because
my body is so detox out I had got severely sick from the alcohol so I was
down for like a good four days I was like sick for two days dehydrated and I
had bought like a five piece Popeyes it took me like four days to eat a piece of
chicken per day in order to get my strength back um so I didn't do a
weigh-in on Monday and I returned after a couple of days and you know started
putting up my videos I could have put a couple of pre-recorded videos up at that
time but I was so sick I definitely couldn't even do that as well I just I
was out of it but today is Monday is weigh-in and yes I have lost weight and
yes I will be telling you how much I lost I just want to let you guys know
that I'm getting close to the end of my Monday weigh-ins of course when I lose
all the weight that I have gained which before I tell you how much I lost total
weight that I have gained was 88 pounds that's probably other the three times
that I have gained weight the most that I have gained was the third time around
when I had gained weight the other two times that I had gained weight it was a
60 pound gain and I lost it quickly this time probably because of age I lost it
at a slower rate well I really can't say that because I started this weight loss
journey the April 2017 it wasn't working out
um but I still was doing my two-hour workout I was doing the one meal a day
intimating fast and I was just maintaining the weight that I was at and
maybe I may have lost a few pounds but I basically you know was still a large
girl so what I did was January 2018 so let's take you from there that's when I
got serious about my weight loss journey I started this weight loss journey so
let's say January February March and April so for months because it's not all
the way a full month made so it's been a full month journey weigh myself to
sworn-in got on the scale and the last time I weighed myself the last weigh-in
the last Monday's weigh-in I was a hundred and eighty six pounds this week
I'm 183 pounds which puts me at 23 more pounds that I need to lose and I will be
okay um but I also want to let you guys know
about some other changes I hope you guys like um this video here because I have a
new set up I like it better because you can see me closer in the video to me the
clarity in the videos a little bit better as well and also the lighting
it's not gonna be a problem anymore everything has been figured out and it
won't be you know up and down you know sometime the Lighting's or point but I
think today's Lighting's on point um and then there's days where you're
like what the hell is going on with our lighting so I have figured it out and
I'm going to stick with this recording style for the time being or not for the
time being for the rest of the time recording doing my videos but getting
off of that I'm not going to UM each day or each week let you guys know what kind
of a regiment don't that I'm doing I'm gonna kind of leave it as a surprise
neck Monday's weigh-in I'm hoping to be out
of the eighties so I'm hoping to be at least 180 to 175 this is a poll that I'm
giving myself and whoever's following me on a regular basis
remember that goal but I'm trying to at least get out the 180s I'm trying to by
the end of May which is a big goal be the 23 pounds that I have to lose down
now I have decided that I wanted to lose another extra five pounds that's just a
personal preference it has nothing to do with needing to lose an extra five
pounds I just want to leave that 5 pound margin open because of what they call
much mother nature the fecal matter and when you eat carbs and so forth how the
body retains water I have to factor in that those are what you know um when you
stop eating foods again like how I'm doing right now it's not that you're you
gain the weight back it's just that when you put in carbs and sugars and your
body also hasn't removed all the fecal matter from what you ate from the day
before when you get on the scale it's not going to weigh what you weighed that
morning the same thing applies if you get up in the morning you have a glass
of water 8 ounce glass of water if you get on the scale you could have weighed
125 pounds but if you drink the 8 glass glass of water 8 ounce glass of water
you probably will be weighing a couple of pounds more so keep that in mind guys
um I know that I keep it in mind I'm looking for a great extreme weigh-in
for next Monday but I will do just fine with the 3 pound weight loss that I lost
for this Monday that's not bad for seven days I'm back on track I'm feeling good
I had a juice that I was going to show you in this video I had a 32 ounce Vinny
juice that I had made it was the same juice because I like savory foods over
sweet things but I drank it down really quickly because it was so good ah but
with all that being said today I'm having my treat meal and that will
consist of 64 ounces of savory juice that I had freshly prepared today in my
juicer and I already drank 32 ounces of it
I won't probably have another juice until probably noontime or around the
time that I make my salad which will consist of arugula lettuce watercress
lettuce onions sun-dried tomatoes gorgonzola cheese tabbouleh hummus and a
whole avocado and then I'll put some fresh lemon juice and some zesty
wishbone robust dressing on top of that and then later on for dinner I will have
I think I was going to have two small baked potatoes loaded with the silent
treatment cheese but I think just by having that one juice today because it
does suppress my appetite that I probably will only have one baked
potato loaded with the sour cream in the cheese and I'll have me some baked
salmon and me some garlic a jumbo shrimp and that's what I'll have
for my dinner so today is my treat meal and I'm going to get it in where I fit
in so I'm not gonna stay on this video long because I'm trying to upload this
video and also enjoy my day because it's not every day while trying to be on the
hustle and grind or losing this weight that I'm able to eat like this every day
so I hope you like this um the new setup that I have and I'll see you guys in the
next video bye for now
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Donald Trump Is About To Cut A Deal With Robert Mueller That No One Can Believe - Duration: 13:30.
Donald Trump Is About To Cut A Deal With Robert Mueller That No One Can Believe
Robert Mueller's crusade to take down President Trump has dragged on for months.
But the end may be in sight.
That's because President Trump may be on the verge of cutting this deal with the special
counsel.
President Trump's lawyers have offered to have him sit for questioning with the special
counsel in exchange for Mueller agreeing to a deadline to end the Russia investigation.
Trump's legal team is also trying to negotiate limiting the scope of the questions.
The Wall Street Journal reports:
"PRESIDENT DONALD TRUMP'S LAWYERS ARE SEEKING TO NEGOTIATE A DEAL WITH SPECIAL COUNSEL
ROBERT MUELLER THAT USES AN INTERVIEW WITH THE PRESIDENT AS LEVERAGE TO SPUR A CONCLUSION
TO THE RUSSIA INVESTIGATION, ACCORDING TO A PERSON FAMILIAR WITH THE DISCUSSIONS.
THE PRESIDENT'S LEGAL TEAM IS CONSIDERING TELLING MR.
MUELLER THAT MR.
TRUMP WOULD AGREE TO A SIT-DOWN INTERVIEW BASED ON MULTIPLE CONSIDERATIONS, INCLUDING
THAT THE SPECIAL COUNSEL COMMIT TO A DATE FOR CONCLUDING AT LEAST THE TRUMP-RELATED
PORTION OF THE INVESTIGATION.
ONE IDEA IS TO SUGGEST A DEADLINE OF 60 DAYS FROM THE DATE OF THE INTERVIEW, THE PERSON
SAID.
ANOTHER CONSIDERATION FOR THE LEGAL TEAM IS REACHING AN AGREEMENT WITH MR.
MUELLER ON THE SCOPE OF HIS QUESTIONING OF THE PRESIDENT, WHICH THEY EXPECT TO FOCUS
LARGELY ON HIS DECISION TO FIRE FORMER NATIONAL SECURITY ADVISER MIKE FLYNN AND FORMER FBI
DIRECTOR JAMES COMEY, ACCORDING TO PEOPLE FAMILIAR WITH THE MATTER."
The Journal also reports that legal experts do not believe Mueller will accept Trump's
offer:
LEGAL EXPERTS SAID THEY WERE SKEPTICAL THAT THE SPECIAL COUNSEL WOULD BE OPEN TO THE TRUMP
LEGAL TEAM'S REQUESTS.
ASSOCIATES REMAIN SILENT?
"YOU CAN'T PUT A TIMELINE ON THESE THINGS," SAID PETER ZEIDENBERG, A FORMER FEDERAL PROSECUTOR
AND AN EXPERT IN GOVERNMENT INVESTIGATIONS.
"SOMEONE COULD WALK IN THE DOOR ON THE DAY BEFORE THEIR PROPOSED DEADLINE AND SAY, 'I'VE
GOT SOME INFORMATION THAT'S GOING TO BLOW YOUR MINDS.'
… MUELLER'S GOING TO SAY, 'OH, TOO BAD, THE DEADLINE'S TOMORROW?'
"
Mueller doesn't want limitations on his ability to question the President or a deadline
to wrap up his inquiry because his goal is to generate a report Congress can use to impeach
Trump.
An interview with Mueller is nothing more than a perjury trap.
If Trump misspeaks on who attended a meeting or gets a date or topic of conversation wrong,
Mueller can claim he lied to investigators.
Likewise, he would shoot down a deadline because he wants unlimited time to concoct his case
against the President.
Trump supporters believe he should not sit for an interview with Mueller.
It would serve no purpose and there is no upside.
Mueller is going to build his case regardless of what Trump says, and the special counsel
would likely use the interview to set a perjury trap
for Trump.
Do you agree?
Let us
know
your thoughts in the comment section.
-------------------------------------------
Chanel Iman Is Pregnant – Check Out The First Baby Bump Pics - Duration: 3:05.
Chanel Iman Is Pregnant – Check Out The First Baby Bump Pics
Chanel Iman took to social media today to announce that she and her New York Giants wide receiver hubby Sterling Shepard are expecting their first baby together! It is safe to say that this is a very special and exciting Mother's Day for the Victoria's Secrets model.
The beauty shared a couple of pics in which she showed off her baby bump.
Alongside a black and white photo of her, Chanel wrote: 'Daddy and Mommy can't wait to meet you.
As I approach motherhood I want to wish a Happy Mother's Day to all mothers and mommies to be.'.
But that was not all! She also shared with her many followers a very sweet picture that also featured her husband kissing her belly.
For the photoshoot the couple was shirtless, only rocking a pair of light, distressed jeans and Calvin Klein underwear.
'We are both blessed in many different ways, that our love's created a wonderful new life and cannot wait for this ultimate blessing to arrive.
We thank God for allowing us this opportunity to become parents and cannot wait to hold our baby in our arms!' the model also wrote.
The father to be shared the same pic with his own followers, captioning it: 'My baby is making my baby! Happy Mother's Day @chaneliman I love you, mama.
Sterling and Iman tied the knot back in March in a celeb-packed ceremony.
Speaking of star guests, Tiffany Haddish delivered an incredible toast at the wedding.
'Make sure y'all have fun together.
I ain't that old, but I am old, but I ain't that old.
But I have seen things.
From my observations, the relationships that last the longest are the relationships where you have fun.
You're going to have your ups and your downs, but find time to have fun,' Haddish said.
-------------------------------------------
The Lady is a Tramp - Tony Bennet feat. Lady Gaga | Karaoke Higher Key - Duration: 3:35.
THE LADY IS A TRAMP (KARAOKE VERSION)
A SONG MADE FAMOUS BY TONY BENNET
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